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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of the Odd Thoughts podcast.

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Speaker 2: I'm Tracy Alloway.

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Speaker 3: And I'm Joe Wisenthal.

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Speaker 2: Joe, I know we did one episode on pod shops, Yeah,

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Speaker 2: on multi strategy hedge funds, but it was primarily focused

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Speaker 2: on their impact on the market, and I have to

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Speaker 2: say I still came away from that conversation sort of

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Speaker 2: wondering if I worked at a pod shop, what is

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Speaker 2: it exactly that I would be doing all day?

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Speaker 3: I would love to know the exact same thing. I mean, like,

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Speaker 3: I guess I have this very vague sense of sort

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Speaker 3: of they have a bunch of people all focused on

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Speaker 3: their specific areas in the sort of ever out, and

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Speaker 3: they net out a bunch of stuff and it's capital

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Speaker 3: efficient and you know, it's like market neutral and theory

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Speaker 3: and et cetera. But beyond that, like, I still don't

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Speaker 3: really like understand. The only thing I know is like

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Speaker 3: they've done really well and many people are launching more

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Speaker 3: of them.

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Speaker 2: Yes, they seem to be all the rage. They seem

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Speaker 2: to be where everyone kind of wants to go in

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Speaker 2: the quantitative finance space. At least everyone's sort of aiming

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Speaker 2: for these big names, you know, places like Citadel Millennium.

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Speaker 2: Maybe yeah, But my question is like, why was it

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Speaker 2: just that they're minting money they're expected to continue minting

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Speaker 2: money in the future, or is there something that's like

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Speaker 2: fundamentally intriguing and attractive about working in that space that

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Speaker 2: means lots of people want to get in.

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Speaker 3: I mean, I think that could be two ways of

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Speaker 3: saying the same thing. If they're minting money, then that

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Speaker 3: probably is fundamentally attractive to people in that space. But

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Speaker 3: I do think like backing up the questions, like what

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Speaker 3: we know is that many and including apparently even like

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Speaker 3: B tier C tier funds have done like very well.

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Speaker 3: So I'm just like curious like how and why? And

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Speaker 3: then yeah to the question of like what does it

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Speaker 3: take to succeed in them or who is the type

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Speaker 3: of person who can succeed in this environment?

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Speaker 2: All right, well, I'm glad you put it that way,

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Speaker 2: because today we're going to be speaking with someone who

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Speaker 2: has done exactly that succeeded in this particular environment. We

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Speaker 2: have the perfect guest. We're going to be speaking with

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Speaker 2: Giuseppe Palielogo aka Gappy. He describes himself as a constant gardener,

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Speaker 2: someone who's on gardening leave quite a lot. He is

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Speaker 2: also the author of Advanced Portfolio Management, a quant's guide

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Speaker 2: for fundamental investors, and I have to say it is

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Speaker 2: one of the funniest books that I've read in quant finance.

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Speaker 2: I can't say it's the funniest because I did read

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Speaker 2: My Life as a Quant from Emmanuel Derman, but it's

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Speaker 2: definitely up there. And Joe, I know you enjoyed it too.

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Speaker 3: I did you know, I like skipped over all the numbers.

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Speaker 2: And equations and you just looked at the jokes.

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Speaker 3: And Greek letters. But it's very breezily written for what

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Speaker 3: it is, and I did. Actually, I think maybe I

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Speaker 3: learned a little bit even in my sort of basic

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Speaker 3: reading of it. Extremely well written. I'm extremely excited about

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Speaker 3: this conversation. You know, you mentioned that our guest is

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Speaker 3: the king of gardening leave. If you look in his LinkedIn,

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Speaker 3: it really is many different roles. Well.

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Speaker 2: I also have to say he is the only person

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Speaker 2: I know who has both an alpha and a beta

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Speaker 2: tattoo on his shoulder.

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Speaker 3: Oh wow.

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Speaker 2: You know, some people do get the alpha symbol, but

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Speaker 2: he has both. So you know, a well balanced portfolio

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Speaker 2: of tattoos all around. Yeah, so gappy. Thank you so

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Speaker 2: much for coming on all thoughts.

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Speaker 4: Hi Tracy, Hi Joe.

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Speaker 2: So maybe to begin with, I'm going to let you

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Speaker 2: explain your previous job history because there is quite a lot.

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Speaker 2: What is it that you've been doing in this industry.

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Speaker 4: I'm not sure, I'm not sure, Okay, good question. Well,

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Speaker 4: I got into this industry almost accidentally. I was for

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Speaker 4: a few years a researcher in the math department at

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Speaker 4: IBM Research, and then I got a little bit bored.

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Speaker 4: So the only place that you can the only industry

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Speaker 4: you can work in New York other than you know,

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Speaker 4: IBM or tech, is finance. So I got into finance

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Speaker 4: almost accidentally. And then again, there is no major plan

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Speaker 4: to you know, to to my career choices. When I

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Speaker 4: was getting bored for some reason, somebody called me and

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Speaker 4: offered me a more interesting job. And so I have

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Speaker 4: been working mostly on the so called buyside of the industry,

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Speaker 4: so the part of the industry that invests, actively invests

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Speaker 4: and takes risks. So I've worked for Citadel twice for

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Speaker 4: a small edge fund as a portfolio manager, and then

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Speaker 4: Millennium and Hudson River Trading, and I've kind of taken

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Speaker 4: turns between doing quantitative research and risk management. So most

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Speaker 4: recently I was at Hudson River Trading until the beginning

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Speaker 4: of November.

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Speaker 3: I think when people think about like multi strategy hedge

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Speaker 3: fund or shop or whatever, maybe sort of Millennium is

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Speaker 3: the first one that would come to mind for people.

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Speaker 3: If someone asks you, how does Millennium make money? And

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Speaker 3: they seem to have made a lot of money over

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Speaker 3: the years, what's the answer?

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Speaker 4: Okay, I hope without saying anything that is proprietary, but

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Speaker 4: I think.

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Speaker 3: That like the business model of Millennium, Yeah.

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Speaker 4: I think that what Millennium has excelled at has been

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Speaker 4: the ability to scale up, so to adapt its existing

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Speaker 4: platform to accommodate new strategies and new portfolio managers and

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Speaker 4: so sometimes actually in some of their marketing material they

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Speaker 4: called it something like an investment operating system. So it's

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Speaker 4: a system that is a firm that is willing to

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Speaker 4: absorb some relatively new strategy and create an environment for

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Speaker 4: that strategy to succeed. And so because of that, I

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Speaker 4: think they might be having right now the highest number

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Speaker 4: of individual pods maybe close to three hundred and hovering

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Speaker 4: around sixty billion dollars of AUM of assets under management.

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Speaker 4: But I would say, what is their superpower is really

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Speaker 4: their ability to scale in number of pods.

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Speaker 2: So you mentioned creating an environment for success there, what

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Speaker 2: does that look like at an organization like that? What

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Speaker 2: are the sort of like conduits that allow trades in

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Speaker 2: that particular organization to be successful?

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Speaker 4: So I would give a sort of an idiosyncratic maybe

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Speaker 4: a story around, please the rationale for success of platforms.

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Speaker 4: So I see platforms a little bit like managing an

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Speaker 4: arbitrage or some kind of gap between the single platform,

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Speaker 4: the single manager, or the small hedge funds and the

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Speaker 4: fund of funds. So if you're a fund of funds,

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Speaker 4: you do have the scale, but you do not have

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Speaker 4: the ability to observe from a close distance the performance

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Speaker 4: of your vehicles for investment. And let's say that they

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Speaker 4: don't perform well, you have to wait a year in

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Speaker 4: order to take your money back. In the case of

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Speaker 4: a hedge fund platform, you could actually not only observe

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Speaker 4: the performance of pms or volume managers their skill from

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Speaker 4: a very close distance, but you can also help them

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Speaker 4: perform better. So you can centralize some of the functions

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Speaker 4: that make them better capital access, corporate access, risk management.

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Speaker 4: If they perform well, to give them more capital. If

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Speaker 4: they don't perform well, to take capital away from them

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Speaker 4: or let them go. And at the same time you

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Speaker 4: also solve two for two other problems. So one is

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Speaker 4: there is a risk transfer happening because a platform almost

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Speaker 4: by design otherwise is not really a platform, has a

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Speaker 4: pass through fee structure that's fundamental for the existence of

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Speaker 4: a platform that makes really a platform what it is

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Speaker 4: instead of a just multi manager hedge fund like the show.

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Speaker 4: So this means that a portfolio manager is not paid

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Speaker 4: with the incentive fee that the hedge fund as a

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Speaker 4: whole receives from the limited partners, but instead the portfolio

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Speaker 4: managers are paid a percentage of their p and L.

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Speaker 4: This payment is passed through directly to the limited partners

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Speaker 4: to the investors, and this basically transfers the risk directly

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Speaker 4: basically from the PM into the limited partner, And so

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Speaker 4: this makes the system more robust in a sense, right,

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Speaker 4: And combine this with the diversification across investment styles and

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Speaker 4: the number of pms, and now you start having a

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Speaker 4: mote around a platform that makes it successful.

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Speaker 3: If a entity has three hundred pods and everyone's doing

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Speaker 3: their own thing, et cetera, why doesn't the return just

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Speaker 3: become the market return like it seems like because there's

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Speaker 3: a right Like, one intuition could be that this model

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Speaker 3: wouldn't scale. I mean, I know it does, but one

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Speaker 3: intuition could be that this model wouldn't scale that the

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Speaker 3: more you add, you overdiversify and then you just end

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Speaker 3: up with like whatever, like you know, like buy the

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Speaker 3: VTI ETF or something like that. Why doesn't it work

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Speaker 3: out that.

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Speaker 4: A simplest explanation for this is actually just to look

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Speaker 4: at what a retail investor right would hold in their portfolio.

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Speaker 4: So let's say that they are, you know, long Apple

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Speaker 4: and IBM. Okay, they have a little bit of an

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Speaker 4: imperfect version of the market, right, But what makes their

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Speaker 4: skill is how different are the weights of their Apple

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Speaker 4: and IBM holdings compared to the market. Okay, So you

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Speaker 4: can decompose your performance in your personal account into the

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Speaker 4: sum of let's say the market and your idiosyncratic bets

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Speaker 4: into these stocks. Now, what the hedge funds do is

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Speaker 4: they do the same, but they completely eliminate as much

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Speaker 4: as they can their exposure or their investment in the market,

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Speaker 4: So they run purely market neutral and factor neutral portfolios.

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Speaker 4: So there is diversification, but these indiosyncratic bets don't get

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Speaker 4: diversified away into a big market, but they actually become

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Speaker 4: essentially a bunch of independent bets that by the law

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Speaker 4: of large numbers, they tend to have better and better

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Speaker 4: risk adjusted profiles.

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Speaker 2: So I still see some platform heads describe like the

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Speaker 2: overall tilt as market neutral. So what do they mean

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Speaker 2: by that? Exactly?

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Speaker 4: I mean they typically run a wide range of strategies,

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Speaker 4: so let's focus because it's more relatable. Let's focus on

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Speaker 4: discretion ory long short equities and systematic equities because everybody

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Speaker 4: knows star.

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Speaker 2: I've love that you think systematic equities is relatable.

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Speaker 4: Yeah, yeah, I mean relatively to I don't know, treasury

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Speaker 4: basis or sell involved. So they mean that typically they

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Speaker 4: do have a so called factor model, and a factor

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Speaker 4: model is a little bit like having a market model

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Speaker 4: on steroids, So you have a market term, so you

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Speaker 4: can see your portfolio as having exposure to the market,

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Speaker 4: so behaving a little bit like a market. And then

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Speaker 4: it's also behaving a little bit like a portfolio that

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Speaker 4: has momentum okay, and then it also has maybe a

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Speaker 4: tilt in terms of value. The platforms tend to run

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Speaker 4: portfolios that have no market exposure whatsoever, and then they

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Speaker 4: also tend to have controlled exposure in these more exotic factors.

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Speaker 3: How do they know that? I mean, so there's someone

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Speaker 3: up there at the center, there's all that three hundred pods.

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Speaker 3: The data gets probably aggregated and sliced in various ways,

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Speaker 3: but what is the job or how do they actually

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Speaker 3: sure that on that their portfolio managers don't have that

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Speaker 3: market beta.

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Speaker 4: Yeah, they typically have at the very minimum. They will

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Speaker 4: buy some commercial factor model, which is a model of

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Speaker 4: the market, like of your investment universe, how the how

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Speaker 4: a stock behaves. How can you decompose the performance of

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Speaker 4: the stock in this various systematic or let's call them

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Speaker 4: pervasive market wide factors and instead idiosyncratics. So you buy

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Speaker 4: them off the shelves. I mean, they're really expensive and

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Speaker 4: they do a job. And so once you've bought them,

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Speaker 4: you create some kind of user friendly interface so that

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Speaker 4: a portfolio manager can always see how the portfolio looks

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Speaker 4: like at any point in time. It's a little bit

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Speaker 4: like having an X ray of you know, your body

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Speaker 4: in real time. You know, you can see, oh, well,

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Speaker 4: my portfolio is is a little bit short, the market

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Speaker 4: is a little bit long momentum, maybe there is some crowding, exposure, whatever,

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Speaker 4: And so this is in the hands of the portfolio manager.

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Speaker 4: And then there is another layer on top of that,

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Speaker 4: which is very important risk management, which ensures that pms

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Speaker 4: are behaving well, that they're not going out of scope.

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Speaker 4: You know, they're not buying microstocks or you know, investing

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Speaker 4: in crazy stuff just.

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Speaker 3: Going or just going along in video.

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Speaker 4: Or long in Vidia. Yeah, if their idea is going

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Speaker 4: long in Vidia, probably that's not an ideal portfolio manager. Yeah.

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Speaker 2: So the other thing I've been wondering is how much

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Speaker 2: visibility are there between the different pods within one shop. Yeah,

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Speaker 2: And I mean that like, I assume there's a centralized

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Speaker 2: risk management system of some sort that is like netting

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Speaker 2: out positions and trying to make use of capital most efficient,

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Speaker 2: and that's where a lot of the edge comes from.

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Speaker 2: But also, if you're just a trader pursuing your own strategy,

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Speaker 2: do you know what the guy next to you is doing?

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Speaker 2: Do you have that kind of vision ability or is

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Speaker 2: the idea to keep everyone sort of intellectually separated so

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Speaker 2: that they're not influenced by each other.

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Speaker 4: Right, that's a good question. So there is no really

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Speaker 4: black and white answer to this, because historically there was

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Speaker 4: a time when platforms had more visibility and more collaboration

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Speaker 4: among pods or at least pods in the same sector,

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Speaker 4: for example. But I would say that the historical trend

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Speaker 4: has been more and more to give them the tools

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Speaker 4: to succeed, but not give them the ability to see

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Speaker 4: into each other's portfolios for example. And the rationale for

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Speaker 4: this is you probably prefer having independent bets to having

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Speaker 4: maybe corredated bets that could be like maybe a little

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Speaker 4: bit more informed. So that's the trade off. Let's if

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Speaker 4: we talk, maybe we can come up with slightly better ideas. Sure,

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Speaker 4: but yeah, I think that the trend is more and

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Speaker 4: more towards you are not seeing what I am, what

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Speaker 4: I'm having, what I'm holding.

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Speaker 3: Talk to us more about the risk management component, and

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Speaker 3: again I don't know very much. I understand that you know,

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Speaker 3: stop losses are very tight and you don't get a

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Speaker 3: long leash to lose money, and if you're not doing well,

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Speaker 3: your capitals reduced. If you're doing well, I guess you

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Speaker 3: get more, and if you do more, you get more,

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Speaker 3: et cetera. But from how would you describe the sort

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Speaker 3: of the essence of risk management at the hedge fund level.

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Speaker 4: So there are maybe two or three core functions that

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Speaker 4: can be described in a qualitative way, but you know,

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Speaker 4: I think pretty comprehensively. And then there there is something

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Speaker 4: that is a little bit more esoteric or like domain specific.

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Speaker 4: So let's talk about the general principles. Okay, So you

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Speaker 4: mentioned stop losses, so this is very important. You know,

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Speaker 4: there are always stop losses, the ones that you know

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Speaker 4: you have and the ones you don't know you have,

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Speaker 4: but everybody has stop losses in life. Okay, So those

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Speaker 4: are very important because you could imagine that a PM

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Speaker 4: is a little bit like somebody who's holding a coll

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Speaker 4: option and you you know, the PM who's losing money

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Speaker 4: has kind of a incentive to go for broke maybe sometimes.

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Speaker 4: But the stop laws is effectively at a sort of

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Speaker 4: a primitive tail insurance TAE risk management tool on the

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Speaker 4: left tail of a PM. So that's very important. The

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Speaker 4: second principle is sort of self enforcing, is true diversification.

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Speaker 4: So this is where you want to have some kind

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Speaker 4: of risk model that tells you what are the hidden

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Speaker 4: bets that kind of overlap and maybe compound at the

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Speaker 4: aggregate level, so that if everybody takes a little bit

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Speaker 4: of a factor exposure in the same direction and then

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Speaker 4: you sum this across three hundred pms, it becomes a

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Speaker 4: big factor exposure. So a risk management organization needs to

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Speaker 4: get that right. The third thing is making sure that

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Speaker 4: people stay in scope. Okay, so seems trivial, but actually

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Speaker 4: that requires a lot of domain expertise. So understanding the trades,

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Speaker 4: what can go wrong from an operational standpoint macrostructure standpoint.

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Speaker 2: Is this factor drift risk as well?

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Speaker 4: Or said that scope is more like factor drift or

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Speaker 4: in general strategy drift, not only factor but whereas being

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Speaker 4: in scope is more of a pure strategy drift or

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Speaker 4: just taking risks that a portfolio manager would be possibly

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Speaker 4: aware of, but that maybe the head of the hedge fund,

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Speaker 4: because it's not an expert in that area, is not

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Speaker 4: so aware of. So the risk manager has to know

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Speaker 4: very well what's going on and an alert. Talk to

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Speaker 4: the PM, talk to the business head and.

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Speaker 2: Can you give us concrete examples from your experience of

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Speaker 2: the kind of things that would set off alarm belts.

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Speaker 2: So is there like, I guess you don't have to

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Speaker 2: give us specific examples, but you know the kind of thing,

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Speaker 2: the types of examples, Yeah, the types of examples that

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Speaker 2: would catch your eye in a risk management position.

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Speaker 4: So we covered a little bit the easy stuff, right,

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Speaker 4: So the easy stuff is people taking too much risk.

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Speaker 4: First of all, it simple, but you know, we think

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Speaker 4: in terms of dollar volatility. Dollar volatility is a little

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Speaker 4: bit like how much you can make or lose in

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Speaker 4: one year for it.

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Speaker 2: So like value at risk, those kind of kind of

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Speaker 2: value risk.

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Speaker 4: Yes, I mean most people think in terms of all

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Speaker 4: value risk too. Okay, yeah, choose your risk metric you

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Speaker 4: want to stay within that. Then factor exposures. Okay, that's

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Speaker 4: also easy concentration. So if you take a mega bet

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Speaker 4: in Nvidia, it has to surface. Okay. So these are

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Speaker 4: relatively simple. There are things that are a little bit

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Speaker 4: more complicated, like, for example, you take some true arbitrage

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Speaker 4: positions where you think that something is running cheap versus

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Speaker 4: rich in say bond versus futures, or you do some

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Speaker 4: kind of funding arbitrage trade where different agents in the

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Speaker 4: investing world have different funding rates for their assets, and

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Speaker 4: those can break, like in a dislocation, that can break.

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Speaker 4: And so the way that typically you manage these things

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Speaker 4: a little it's a little bit like in merger arm.

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Speaker 4: You give it a max size and you want to

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Speaker 4: make sure that this is correct, that this size is correct,

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Speaker 4: and it's monitored. So this is stuff that can go wrong.

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Speaker 3: Two managers like, how much do they I mean, I'm

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Speaker 3: sure there's sort of I don't know if it's accidental

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Speaker 3: style drift or you know, drift is sort of a

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Speaker 3: neutral term. How much does the risk manager have to

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Speaker 3: watch out for I guess intentional drift or this is

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Speaker 3: a working I know this is not quite my mandate,

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Speaker 3: this is not quite what I was made to trade.

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Speaker 3: But I could sort of justify it this way, or

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Speaker 3: I just see all these lines up over here going up,

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Speaker 3: I need to how much of a risk management concern

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Speaker 3: is that?

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Speaker 4: Okay, I think that in general the principle should be trust,

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Speaker 4: but verify. I would say that the vast majority of

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Speaker 4: portfolio managers are very responsible, and because they're in that role,

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Speaker 4: they have been educated to control their risks, to understand

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Speaker 4: them with occasional screw ups, and so that's why you

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Speaker 4: need very fie got it?

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Speaker 3: Okay.

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Speaker 2: On the opposite side of screw ups, I'm curious how

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Speaker 2: capital gets kind of doled out. And if I'm running

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Speaker 2: a massively profitable, successful training strategy, do I automatically start

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Speaker 2: giving start being given more money to you know, play

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Speaker 2: around with or is there some amount of discipline here

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Speaker 2: where you don't want people to be bumping up against

359
00:20:29,119 --> 00:20:33,080
Speaker 2: you know, sizing positions or additional trading costs and things

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Speaker 2: like that. Imagine I am the most popular trader, the

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Speaker 2: most successful popular also popular, I'm both the most popular

362
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Speaker 2: trader and most successful trader at Citadel.

363
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Speaker 3: What is the process for traces getting more money to trade?

364
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Speaker 2: How do I get more popular and successful? Probably not popular?

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Speaker 4: Okay, assume that you're popular and successful, okay, So do

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Speaker 4: you get more capital? You do get more capital up

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Speaker 4: to a point. So there are a couple of factors.

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Speaker 4: The first one is there is like a natural limit

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Speaker 4: where somebody can be too successful. And without giving examples,

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Speaker 4: but there are large funds whose daily p and l

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Speaker 4: sometimes at points are driven is driven by a single

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Speaker 4: strategy Okay, and maybe that's justified, right, But there is

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Speaker 4: a point where there could be just too much because

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Speaker 4: the concentration across strategies. Or think of pods as stocks, right,

375
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Speaker 4: you don't want to have ninety percent of your savings

376
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Speaker 4: in Nvidia, So okay, so that's number one. So there

377
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Speaker 4: is some kind of basic heuristics. Then there is just

378
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Speaker 4: a natural limit to growth for strategies, like there is

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Speaker 4: a trade off because your market impact is very high

380
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Speaker 4: and so, or there is just a hard size for

381
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Speaker 4: your strategy, so you cannot scale high frequency, you cannot

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Speaker 4: scale to infinity even index rebalancing. Or if you're a

383
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Speaker 4: consumer PM, your costs increase faster than the size of

384
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Speaker 4: your portfolio, so your P and L in the absence

385
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Speaker 4: of costs goes more or less lilenar linearly, but your

386
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Speaker 4: costs grow faster than linearly. So there is a point

387
00:22:10,359 --> 00:22:13,880
Speaker 4: where you just don't want to grow all Right.

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Speaker 3: On the flip side, Let's say Tracy comes in and

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Speaker 3: she is a PM and she has her pod. How

390
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Speaker 3: long is she likely to last and what would cause

391
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Speaker 3: her what would be the threshold at which she gets fired.

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Speaker 4: I don't have the statistics on the average tenure of

393
00:22:34,920 --> 00:22:39,080
Speaker 4: a PM, Okay, if I had them, probably I shouldn't say. Well,

394
00:22:39,119 --> 00:22:42,400
Speaker 4: and also depends a lot on the place. Okay, so

395
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Speaker 4: how long I would say that it's like everything in life, right,

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Speaker 4: So like ninety percent of everything is of poor quality,

397
00:22:50,880 --> 00:22:53,040
Speaker 4: I'm sorry to say, but the same applies to pms.

398
00:22:53,160 --> 00:22:56,240
Speaker 4: But this is another beautiful aspect of platforms, by the way. Okay,

399
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Speaker 4: so let me take a quick ditchure about this again,

400
00:22:58,440 --> 00:23:02,480
Speaker 4: because like a beautiful and under appreciated aspect of platforms

401
00:23:03,040 --> 00:23:08,560
Speaker 4: is that they act like sieves. So you go through

402
00:23:09,160 --> 00:23:14,320
Speaker 4: basically every possible PM on the market, and there is

403
00:23:14,359 --> 00:23:17,000
Speaker 4: a turnover, let's say, of twenty percent, So twenty percent

404
00:23:17,080 --> 00:23:19,879
Speaker 4: of pms more or less are let go every or

405
00:23:19,960 --> 00:23:22,880
Speaker 4: leave every year, but you keep the good ones right,

406
00:23:22,960 --> 00:23:27,000
Speaker 4: and so eventually you have a sufficient number of pms

407
00:23:27,520 --> 00:23:31,840
Speaker 4: who really can carry make the business sustainable. And a

408
00:23:31,880 --> 00:23:35,439
Speaker 4: platform is an instrument for exploration. Okay. So I'm not

409
00:23:35,520 --> 00:23:38,080
Speaker 4: saying how long they last or whatever, right, but okay,

410
00:23:38,280 --> 00:23:40,919
Speaker 4: how good do you need to be? I think that

411
00:23:40,960 --> 00:23:44,720
Speaker 4: if you have a market neutral sharp RAI show which

412
00:23:44,800 --> 00:23:47,119
Speaker 4: for those who are not used to this number, this

413
00:23:48,000 --> 00:23:51,119
Speaker 4: basically is a risk adjusted measure of profits. So you

414
00:23:51,160 --> 00:23:53,280
Speaker 4: take your P and L and you divide by some

415
00:23:53,359 --> 00:23:55,160
Speaker 4: measure of risk, and you get the sharp pray show.

416
00:23:55,680 --> 00:23:58,919
Speaker 4: If you don't have these kind of market exposures, you

417
00:23:58,960 --> 00:24:01,480
Speaker 4: call it information race show. If you have an information

418
00:24:01,640 --> 00:24:04,960
Speaker 4: ratio of one, and you are managing your left tail

419
00:24:05,040 --> 00:24:11,040
Speaker 4: sufficiently wisely, you can survive. Okay, So you know, start practicing.

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00:24:11,240 --> 00:24:15,199
Speaker 2: Okay, okay. But on this note, the other thing I

421
00:24:15,200 --> 00:24:17,879
Speaker 2: wanted to ask you was, you know, we tend to

422
00:24:17,960 --> 00:24:22,560
Speaker 2: talk about these things platforms, pod shops, multi strat as

423
00:24:22,600 --> 00:24:27,320
Speaker 2: like this one big blob basically doing a similar thing.

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Speaker 2: But my impression is that the culture varies quite substantially

425
00:24:32,320 --> 00:24:35,960
Speaker 2: across firms. And again there aren't that many that are

426
00:24:36,000 --> 00:24:38,200
Speaker 2: doing this, although as Joe said and the intro, the

427
00:24:38,280 --> 00:24:41,080
Speaker 2: number is growing. But when we talk about that kind

428
00:24:41,119 --> 00:24:43,400
Speaker 2: of cultural variation, what do we mean exactly?

429
00:24:44,240 --> 00:24:49,080
Speaker 4: To an amazing extent, I think that platforms are shaped

430
00:24:49,160 --> 00:24:54,800
Speaker 4: by the personalities of their founders. So is Englander as

431
00:24:54,840 --> 00:24:58,800
Speaker 4: a personality, and a personal history can grief in as

432
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Speaker 4: a different one, So of wads and river trading not

433
00:25:01,920 --> 00:25:04,439
Speaker 4: a platform, you know, strict to censu but you know,

434
00:25:04,520 --> 00:25:08,159
Speaker 4: to some extent, multi strategy, and so and so the

435
00:25:08,200 --> 00:25:11,640
Speaker 4: cultures are very affected by this. So if you are

436
00:25:11,920 --> 00:25:17,960
Speaker 4: a trader like Ken Griffin, it's more likely that the

437
00:25:18,000 --> 00:25:20,159
Speaker 4: fund that you work in it's as more of a

438
00:25:20,160 --> 00:25:24,040
Speaker 4: trading as opposed to maybe a pure technology culture. Millennium

439
00:25:24,160 --> 00:25:28,919
Speaker 4: is very decentralized. Citadel tends to run more like a

440
00:25:29,000 --> 00:25:31,879
Speaker 4: centralized and efficient organization. So in the words of a

441
00:25:32,359 --> 00:25:35,520
Speaker 4: of a Hedgehund manager, you know, Citadel is like Singapore

442
00:25:36,000 --> 00:25:40,160
Speaker 4: and Millennium is like the United States. Right, Singapore very efficient,

443
00:25:40,600 --> 00:25:44,520
Speaker 4: efficiently run technocratic to some extent, and the US is

444
00:25:45,400 --> 00:25:49,720
Speaker 4: messy and inefficient, but it's very robust. And in a sense,

445
00:25:49,800 --> 00:25:53,000
Speaker 4: you know, Millennium has these features of robustness of it's

446
00:25:53,040 --> 00:25:56,480
Speaker 4: like an organic creature. It does change a lot. So

447
00:25:56,560 --> 00:26:00,000
Speaker 4: other some firms are more collaborative. I think Ballyasni for example,

448
00:26:00,200 --> 00:26:03,000
Speaker 4: tends to be more collaborative than these other two firms.

449
00:26:03,080 --> 00:26:05,159
Speaker 4: But by the way, and your marriage may vary between

450
00:26:05,200 --> 00:26:09,000
Speaker 4: different teams, like depending on where you work, you know,

451
00:26:09,000 --> 00:26:10,560
Speaker 4: it can be heaven or it can be hell.

452
00:26:11,480 --> 00:26:14,560
Speaker 3: All Right, someone hears this podcast, maybe they're in college

453
00:26:14,600 --> 00:26:18,040
Speaker 3: studying finance or maybe something in tech or something engineering

454
00:26:18,119 --> 00:26:20,080
Speaker 3: or whatever. They're like oh, this sounds really cool. I

455
00:26:20,080 --> 00:26:22,000
Speaker 3: want to work, for one, what is sort of the

456
00:26:22,040 --> 00:26:25,679
Speaker 3: basic path that one winds up maybe first in a

457
00:26:25,760 --> 00:26:27,000
Speaker 3: pod and then running a pod.

458
00:26:27,240 --> 00:26:29,240
Speaker 4: Okay, So first of all, I would like to dissuade

459
00:26:29,280 --> 00:26:32,440
Speaker 4: everybody who's listening from studying a career in finance.

460
00:26:32,480 --> 00:26:35,560
Speaker 3: Okay, okay, So everyone's going to take that as a challenge,

461
00:26:35,600 --> 00:26:36,440
Speaker 3: but keep going.

462
00:26:36,440 --> 00:26:41,480
Speaker 4: Of course. And so I wrote a small document because

463
00:26:41,480 --> 00:26:44,800
Speaker 4: I got a lot of questions like this from students,

464
00:26:45,000 --> 00:26:47,399
Speaker 4: and the brutal answer is that it's very difficult and

465
00:26:47,760 --> 00:26:51,399
Speaker 4: there is some luck involved. So it does help to

466
00:26:51,520 --> 00:26:55,439
Speaker 4: go to schools with a brand name, for sure. It

467
00:26:55,520 --> 00:26:58,400
Speaker 4: definitely does help if you want to do quantitative stuff

468
00:26:58,520 --> 00:27:01,080
Speaker 4: to be a very good programmer, and you know, you

469
00:27:01,200 --> 00:27:04,080
Speaker 4: need to have the ability to think quantitatively. So that's

470
00:27:04,320 --> 00:27:06,960
Speaker 4: that's for sure. There are couting tests that make the

471
00:27:07,000 --> 00:27:11,240
Speaker 4: admission a little bit more democratic nowadays, but still still

472
00:27:11,240 --> 00:27:15,640
Speaker 4: it's very selective. I am not particularly qualified to give

473
00:27:15,680 --> 00:27:19,679
Speaker 4: advice on how to get food in the industry. I

474
00:27:19,720 --> 00:27:22,400
Speaker 4: think I have a better view of how to succeed

475
00:27:22,440 --> 00:27:24,280
Speaker 4: in how to be happy, not succeed how to be

476
00:27:24,320 --> 00:27:25,840
Speaker 4: happy in the industry.

477
00:27:25,960 --> 00:27:27,800
Speaker 3: So that's probably more important, let's hear this.

478
00:27:28,080 --> 00:27:30,600
Speaker 4: Yeah, yeah, So I mean how to be happy in

479
00:27:30,640 --> 00:27:36,080
Speaker 4: the industry. I think that I ask a lot the

480
00:27:36,240 --> 00:27:39,200
Speaker 4: question of what makes a good analyst or a good

481
00:27:39,280 --> 00:27:43,199
Speaker 4: quantitative researcher to people, and I get very often the

482
00:27:43,240 --> 00:27:47,040
Speaker 4: same answer, which is people who are curious do well

483
00:27:47,160 --> 00:27:49,840
Speaker 4: and seem to be happy. So as usual, you need

484
00:27:49,880 --> 00:27:52,840
Speaker 4: to have passion, you need to go, you know, to

485
00:27:52,880 --> 00:27:55,600
Speaker 4: get into the weekend, and not being able not to

486
00:27:55,600 --> 00:27:59,200
Speaker 4: think about a problem. So I think obsession helps. Okay,

487
00:27:59,280 --> 00:28:02,040
Speaker 4: So I think the belongs to the obsessed, for good

488
00:28:02,119 --> 00:28:04,879
Speaker 4: or worse in the future. Like you can see this,

489
00:28:05,720 --> 00:28:08,679
Speaker 4: it's a heavy tailed world. So if you want to

490
00:28:08,880 --> 00:28:14,480
Speaker 4: have a more stable job and less absorbing, I think

491
00:28:14,640 --> 00:28:17,800
Speaker 4: being a dentisty is a better career path. But having

492
00:28:17,840 --> 00:28:21,360
Speaker 4: some level of obsessions into this stuff it's good. Otherwise

493
00:28:21,480 --> 00:28:23,199
Speaker 4: at some point, you know, you leave the industry. It's

494
00:28:23,200 --> 00:28:23,840
Speaker 4: perfectly fine.

495
00:28:23,880 --> 00:28:27,840
Speaker 2: By the way, So this actually reminds me of something

496
00:28:27,840 --> 00:28:30,280
Speaker 2: else I wanted to ask you. So you said the

497
00:28:30,280 --> 00:28:33,080
Speaker 2: world belongs to the obsessed, which great line is a

498
00:28:33,200 --> 00:28:38,520
Speaker 2: very good line. But when I read books on quantitative finance,

499
00:28:38,600 --> 00:28:41,680
Speaker 2: so much of it seems to be about Greek letters

500
00:28:41,960 --> 00:28:46,400
Speaker 2: for a start, but basically sizing and managing risk and

501
00:28:46,440 --> 00:28:48,800
Speaker 2: how to look at your positions and all of that,

502
00:28:49,200 --> 00:28:52,680
Speaker 2: how do you actually generate trade ideas? Like where does

503
00:28:52,720 --> 00:28:56,719
Speaker 2: the strategy come from? Am I just looking for you know,

504
00:28:56,960 --> 00:29:01,400
Speaker 2: mathematical dislocations in the market and arbitrary opportunities? Or am

505
00:29:01,440 --> 00:29:04,680
Speaker 2: I thinking like I want to go big on something

506
00:29:04,720 --> 00:29:06,840
Speaker 2: like AI or clean energy or whatever.

507
00:29:08,000 --> 00:29:10,320
Speaker 4: So I think that there are two dimensions to your question.

508
00:29:10,440 --> 00:29:15,160
Speaker 4: So the first one is how objectively do you create alpha? Okay?

509
00:29:15,160 --> 00:29:17,840
Speaker 4: And so there are only a certain finite number of

510
00:29:18,200 --> 00:29:22,160
Speaker 4: ways to go about alpha okay. So there are structural,

511
00:29:22,600 --> 00:29:28,440
Speaker 4: structural imbalances that are not adaptively filled because the market

512
00:29:28,600 --> 00:29:31,960
Speaker 4: is poorly designed, because we don't live in a neoclassical world, okay,

513
00:29:32,680 --> 00:29:37,320
Speaker 4: and so these imbalances persist. And how do you exploit

514
00:29:37,480 --> 00:29:41,719
Speaker 4: this physical alpha? Is two ways. The first one is

515
00:29:41,760 --> 00:29:45,400
Speaker 4: you're a freaking genius and you face a wall for

516
00:29:45,440 --> 00:29:47,480
Speaker 4: two years, do research, and you come up with an

517
00:29:47,480 --> 00:29:50,280
Speaker 4: originally okay, there are people like this, very few. The

518
00:29:50,320 --> 00:29:53,920
Speaker 4: other is simpler. It's like a Renaissance style. You are

519
00:29:53,960 --> 00:29:57,640
Speaker 4: an apprentice in a famous painter's shop and you learn

520
00:29:57,680 --> 00:30:00,320
Speaker 4: the trade, and then you strike it on your own

521
00:30:00,400 --> 00:30:03,120
Speaker 4: and you make it a little bit better, and even

522
00:30:03,160 --> 00:30:05,560
Speaker 4: making it a little bit better can make a huge difference.

523
00:30:06,000 --> 00:30:09,800
Speaker 4: So I would say imitation plays a big role. And

524
00:30:10,320 --> 00:30:13,760
Speaker 4: then maybe there is another characteristic, which is you just

525
00:30:13,840 --> 00:30:17,040
Speaker 4: have to have the right makeup in terms of you know,

526
00:30:17,160 --> 00:30:22,120
Speaker 4: drive tolerance, risk tolerance, so you know when you I

527
00:30:22,160 --> 00:30:24,960
Speaker 4: was actually having lunch with a former zero point seventy

528
00:30:25,000 --> 00:30:30,200
Speaker 4: two pm now and his biggest jordan was ninety million dollars,

529
00:30:30,240 --> 00:30:33,600
Speaker 4: which is, by the way, not crazy crazy high. If

530
00:30:33,600 --> 00:30:38,400
Speaker 4: you're down half a billion dollars, you're literally losing your marbles. Okay,

531
00:30:38,720 --> 00:30:41,160
Speaker 4: your you know, your face looks different.

532
00:30:41,240 --> 00:30:42,480
Speaker 3: So have you seen that?

533
00:30:42,720 --> 00:30:45,280
Speaker 4: Oh sure, yeah, yeah, yeah.

534
00:30:45,360 --> 00:30:49,120
Speaker 3: I remember in a flowed by randomness to Lev talks

535
00:30:49,160 --> 00:30:52,800
Speaker 3: about watching all of like the hormones of someone who

536
00:30:52,840 --> 00:30:54,479
Speaker 3: just lost a lot of money, like pour out, and

537
00:30:54,520 --> 00:30:55,440
Speaker 3: how pale they look.

538
00:30:55,800 --> 00:30:56,000
Speaker 4: Right.

539
00:30:56,040 --> 00:30:58,240
Speaker 3: He had a specific comment about that if there are

540
00:30:58,280 --> 00:31:01,880
Speaker 3: only so many geniuses, if there isn't an infinite supply

541
00:31:01,960 --> 00:31:05,840
Speaker 3: of alpha, if the structural forces, the physical forces as

542
00:31:05,920 --> 00:31:08,960
Speaker 3: you describe them, you know, there's only so many sort

543
00:31:08,960 --> 00:31:12,440
Speaker 3: of these dislocations or reasons why reality is separate from

544
00:31:12,440 --> 00:31:16,400
Speaker 3: the neoclassical world. Does it imply that as we see

545
00:31:16,440 --> 00:31:19,880
Speaker 3: more of these launches, and as these hedgephones get bigger,

546
00:31:20,000 --> 00:31:23,600
Speaker 3: that the opportunity diminishes. Yes? Cool?

547
00:31:26,320 --> 00:31:31,840
Speaker 4: Wait, why, well, because everything has a finite capacity, that's it.

548
00:31:31,960 --> 00:31:34,680
Speaker 4: I mean, And you know, as you say, Joe, right,

549
00:31:34,720 --> 00:31:37,840
Speaker 4: there is there are only that many opportunities, and each

550
00:31:37,880 --> 00:31:41,400
Speaker 4: opportunity has a finite capacity, and so at some point

551
00:31:41,480 --> 00:31:43,520
Speaker 4: everybody is doing the same thing and you get to

552
00:31:43,600 --> 00:31:46,560
Speaker 4: some kind of equilibrium which is not necessary that everybody

553
00:31:46,880 --> 00:31:49,640
Speaker 4: makes the minimum rate of return. Right, But you know.

554
00:32:05,160 --> 00:32:09,160
Speaker 2: You mentioned earlier the systematic equities are more relatable than

555
00:32:09,200 --> 00:32:11,720
Speaker 2: other things like the treasury basis trade and I kind

556
00:32:11,720 --> 00:32:14,400
Speaker 2: of my personal experience, I would beg to differ because

557
00:32:14,440 --> 00:32:17,440
Speaker 2: I come from a sort of credit background. But it

558
00:32:17,520 --> 00:32:21,719
Speaker 2: reminded me a lot of these firms are becoming bigger

559
00:32:21,720 --> 00:32:26,200
Speaker 2: presences in the bond market, bigger market making roles and

560
00:32:26,240 --> 00:32:29,800
Speaker 2: that sort of thing. Does the day to day of

561
00:32:30,080 --> 00:32:34,160
Speaker 2: being in equities versus fixed income in this kind of world?

562
00:32:34,440 --> 00:32:38,040
Speaker 2: Is it very different or do similar principles? Supply?

563
00:32:38,120 --> 00:32:41,440
Speaker 4: I think it's very different, actually, you know, And why first,

564
00:32:41,600 --> 00:32:47,360
Speaker 4: in fundamental equities, your edge is mostly informational, so you

565
00:32:47,400 --> 00:32:50,640
Speaker 4: do have a model of the world that differs from consensus,

566
00:32:51,040 --> 00:32:54,960
Speaker 4: and you monetize that. It's really informational. In the case

567
00:32:55,000 --> 00:32:58,160
Speaker 4: of a lot of fixed income, is truly structural. You know,

568
00:32:58,240 --> 00:33:02,600
Speaker 4: there are predictable flows, there are well known imbalances, there

569
00:33:02,600 --> 00:33:08,120
Speaker 4: are different demands for liquidity. So it's more of a

570
00:33:08,160 --> 00:33:12,800
Speaker 4: strategy or a class of strategies that has skew. So

571
00:33:13,000 --> 00:33:15,360
Speaker 4: you could lose a lot of money, but to collect

572
00:33:15,440 --> 00:33:19,280
Speaker 4: pennies on a regular basis, so you need to manage

573
00:33:19,360 --> 00:33:21,960
Speaker 4: risk for that. You need to have more capital for that,

574
00:33:22,320 --> 00:33:25,400
Speaker 4: and scenarios for that. So the risk management. The way

575
00:33:25,440 --> 00:33:29,240
Speaker 4: you think about investment is different, is more scenario based,

576
00:33:29,960 --> 00:33:33,840
Speaker 4: it's less diversified. Fundamentally, you have relatively correlated bets.

577
00:33:33,960 --> 00:33:38,080
Speaker 3: Why isn't the world actually mapped to the neoclassical view

578
00:33:38,120 --> 00:33:40,280
Speaker 3: of the world because there's so much money and there's

579
00:33:40,360 --> 00:33:44,640
Speaker 3: so much investment and effort being put into spotting any

580
00:33:44,680 --> 00:33:48,080
Speaker 3: price dislocation anywhere, So why is it with all the

581
00:33:48,120 --> 00:33:51,040
Speaker 3: money and all of the professionals and the geniuses and

582
00:33:51,080 --> 00:33:54,840
Speaker 3: the supercomputers and the AI that are like essentially attacking

583
00:33:54,840 --> 00:33:58,600
Speaker 3: the question of finding mispriced securities. Why are there still

584
00:33:58,680 --> 00:33:59,760
Speaker 3: mispriced securities?

585
00:34:00,080 --> 00:34:02,040
Speaker 2: Theory everything should get arbed out.

586
00:34:02,040 --> 00:34:05,000
Speaker 4: Yeah yeah, but not in practice.

587
00:34:05,000 --> 00:34:07,120
Speaker 3: Well yeah, but that's why. Why not? Why does it

588
00:34:07,160 --> 00:34:09,880
Speaker 3: even with all the professionals and money trying to do this,

589
00:34:10,320 --> 00:34:14,759
Speaker 3: did there still persist in these anomalies or dislocations, whatever

590
00:34:14,760 --> 00:34:15,279
Speaker 3: you want to call it.

591
00:34:16,120 --> 00:34:18,799
Speaker 4: I don't I'm not really qualified to answer, but I

592
00:34:18,960 --> 00:34:21,560
Speaker 4: just see, there is only a finite number of professionals,

593
00:34:21,960 --> 00:34:24,080
Speaker 4: you know, and there is only a finite number of

594
00:34:24,080 --> 00:34:28,640
Speaker 4: professionals with a certain risk tolerance. So and there are

595
00:34:28,640 --> 00:34:31,960
Speaker 4: constraints all around their constraints on your balance sheet, there

596
00:34:31,960 --> 00:34:35,120
Speaker 4: are constraints on how much money can you lose. So

597
00:34:35,160 --> 00:34:38,160
Speaker 4: there are all sorts of limits to arbitrage that go

598
00:34:38,280 --> 00:34:41,880
Speaker 4: beyond the toy model of you know, slife with AMBITIONI

599
00:34:42,320 --> 00:34:45,719
Speaker 4: but they go So that's kind of a funding arbitrage.

600
00:34:45,719 --> 00:34:49,080
Speaker 4: And the mechanism, by the way, it's wrong for that paper.

601
00:34:49,200 --> 00:34:52,680
Speaker 4: I mean, it's not realistic, not wrong, it's like artificial.

602
00:34:52,719 --> 00:34:56,040
Speaker 4: But wherever there is a constraint, independently of how many

603
00:34:56,080 --> 00:35:01,480
Speaker 4: players you have, you have a potential inefficiency and it's

604
00:35:01,480 --> 00:35:02,560
Speaker 4: not going to go away.

605
00:35:03,600 --> 00:35:06,279
Speaker 2: I have a practical question, And I always wanted to

606
00:35:06,320 --> 00:35:09,080
Speaker 2: ask this of someone, and I think you're the perfect

607
00:35:09,160 --> 00:35:12,239
Speaker 2: person to perhaps answer this. But if you are a

608
00:35:12,400 --> 00:35:16,919
Speaker 2: risk manager at this kind of firm, and I don't

609
00:35:16,960 --> 00:35:20,279
Speaker 2: know you're you come into the office and it's let's

610
00:35:20,320 --> 00:35:23,160
Speaker 2: say it's like the day of a FED meeting and

611
00:35:23,920 --> 00:35:28,040
Speaker 2: Jerome Powell comes out and says something completely unexpected, or

612
00:35:28,160 --> 00:35:31,520
Speaker 2: let's say it's twenty fifteen and China suddenly announces they're

613
00:35:31,560 --> 00:35:35,280
Speaker 2: devaluing the un And you're looking at your computer screen

614
00:35:35,320 --> 00:35:38,400
Speaker 2: and you're looking at the various risk metrics. How fast

615
00:35:38,520 --> 00:35:41,480
Speaker 2: do those move and how much of it is calculated

616
00:35:41,560 --> 00:35:44,480
Speaker 2: in real time versus all the numbers having to be

617
00:35:44,600 --> 00:35:46,839
Speaker 2: run at the end of the day when you net

618
00:35:46,840 --> 00:35:50,360
Speaker 2: out trading positions.

619
00:35:52,120 --> 00:35:56,160
Speaker 4: If you have the right model, you should be able

620
00:35:56,239 --> 00:36:00,960
Speaker 4: to either capture those risks directly in a sense, imagine

621
00:36:00,960 --> 00:36:06,040
Speaker 4: you have a sensitivity to the various points in the

622
00:36:06,080 --> 00:36:10,120
Speaker 4: Yell curve, either in your fixed income portfolio or in

623
00:36:10,160 --> 00:36:13,239
Speaker 4: your equities portfolio. If you capture those well, so it's

624
00:36:13,239 --> 00:36:15,640
Speaker 4: a risk that you you know you're taking and you

625
00:36:15,680 --> 00:36:18,680
Speaker 4: can hedge. You should see the factor moving, but not

626
00:36:18,760 --> 00:36:21,359
Speaker 4: your porfolio moving. Okay, And by the way. You can

627
00:36:21,400 --> 00:36:24,719
Speaker 4: also not have these factors, but you may have factors

628
00:36:24,719 --> 00:36:29,040
Speaker 4: that are proxying these microeconomic drivers, like say, for example,

629
00:36:29,080 --> 00:36:33,080
Speaker 4: momentum is one, crowding is another. And so even if

630
00:36:33,239 --> 00:36:36,560
Speaker 4: a portfolio manager doesn't think directly in terms of points

631
00:36:36,600 --> 00:36:40,560
Speaker 4: on the ill curve, but they have other related ways

632
00:36:40,560 --> 00:36:42,520
Speaker 4: of thinking, so they can still control for that. And

633
00:36:42,560 --> 00:36:45,839
Speaker 4: then there is, unfortunately the case where well we never

634
00:36:45,880 --> 00:36:48,040
Speaker 4: model this, we do not have a proxy for this,

635
00:36:48,160 --> 00:36:50,279
Speaker 4: and then you're screwed, And yeah, you don't want to

636
00:36:50,320 --> 00:36:53,200
Speaker 4: be in that situation. Typically, you know, you can see

637
00:36:53,239 --> 00:36:55,680
Speaker 4: these effects like I mean, there was a big surprise

638
00:36:55,760 --> 00:36:58,680
Speaker 4: when when rates went up a lot of equity portfolios

639
00:36:58,800 --> 00:37:02,120
Speaker 4: moved and they didn't know why, and there was no

640
00:37:02,200 --> 00:37:05,360
Speaker 4: interest rates sensitivity in commercial factor models.

641
00:37:05,760 --> 00:37:08,680
Speaker 3: So there you go in theory, on a day of

642
00:37:08,719 --> 00:37:12,840
Speaker 3: some sort of unexpected event. Tracy mentioned the China U

643
00:37:12,920 --> 00:37:18,040
Speaker 3: end evaluation. If everything is working perfectly and you truly

644
00:37:18,560 --> 00:37:23,160
Speaker 3: do have like completely eliminated your market exposure, does that

645
00:37:23,200 --> 00:37:26,359
Speaker 3: show up at that level, like does it still show

646
00:37:26,440 --> 00:37:27,000
Speaker 3: up somehow?

647
00:37:27,440 --> 00:37:29,719
Speaker 4: It still can show up in weird ways, right, So

648
00:37:30,120 --> 00:37:33,240
Speaker 4: for example, you can be market neutral. Yeah, the market

649
00:37:33,280 --> 00:37:36,080
Speaker 4: has a big drowdown and you still lose money. Why

650
00:37:36,120 --> 00:37:42,759
Speaker 4: because the market the drawdown starts weird processes of the

651
00:37:42,880 --> 00:37:47,280
Speaker 4: risking that affect your portfolio. So even if I'm market neutral,

652
00:37:47,320 --> 00:37:52,120
Speaker 4: somebody is selling my stock to reduce their risk and

653
00:37:52,200 --> 00:37:54,960
Speaker 4: it's affecting even though I'm perfectly market neutral. So weird

654
00:37:55,040 --> 00:37:58,080
Speaker 4: things can happen. Unfortunately, you know, so there is no

655
00:37:58,160 --> 00:37:59,839
Speaker 4: perfect model, that's the short answer.

656
00:38:00,040 --> 00:38:04,840
Speaker 2: Unfortunately, you mentioned crowding in multi strap and the idea

657
00:38:04,920 --> 00:38:07,640
Speaker 2: that maybe you know, eventually you would reach a limit

658
00:38:07,760 --> 00:38:11,320
Speaker 2: for the efficacy of some of this type of trading.

659
00:38:12,280 --> 00:38:15,479
Speaker 2: What's next for hedge funds? So we went from fund

660
00:38:15,480 --> 00:38:18,200
Speaker 2: of funds to pod shops. They became the hot new thing.

661
00:38:18,440 --> 00:38:20,200
Speaker 2: What comes after pod shops?

662
00:38:20,719 --> 00:38:21,360
Speaker 3: What's exciting?

663
00:38:21,440 --> 00:38:24,760
Speaker 4: I'd love to know. It's for the next guest to answer,

664
00:38:25,120 --> 00:38:25,520
Speaker 4: I don't know.

665
00:38:25,880 --> 00:38:29,080
Speaker 2: This is where you reveal where you're gardening, your current

666
00:38:29,160 --> 00:38:31,799
Speaker 2: gardening leave ends, and where you're gonna wind up next.

667
00:38:31,800 --> 00:38:35,319
Speaker 4: Oh yeah, my best job is always the next I

668
00:38:35,320 --> 00:38:40,880
Speaker 4: don't know. But so what's next in terms of business model?

669
00:38:40,880 --> 00:38:45,880
Speaker 4: Would be very interesting to know what's next. So there

670
00:38:45,920 --> 00:38:48,120
Speaker 4: are some interesting ideas. So there is the idea of

671
00:38:48,200 --> 00:38:52,399
Speaker 4: alpha capture, which is kind of a big umbrella. And

672
00:38:52,600 --> 00:38:56,239
Speaker 4: you know, alpha capture has has an interesting story. So

673
00:38:56,719 --> 00:39:01,319
Speaker 4: there was external external sale set alpha capture. That's historically

674
00:39:01,560 --> 00:39:04,480
Speaker 4: like kind of a creation of Martial Ways, an English

675
00:39:04,480 --> 00:39:06,680
Speaker 4: hatch fund that in two thousand and three or four

676
00:39:07,640 --> 00:39:11,319
Speaker 4: study at program called tops where they gathered ideas from

677
00:39:11,320 --> 00:39:13,919
Speaker 4: the cell side, and that for a while was very

678
00:39:13,920 --> 00:39:17,840
Speaker 4: profitable and also has lots of other byproducts that are great.

679
00:39:18,400 --> 00:39:21,120
Speaker 4: Now I think it's kind of arbitraged out now there

680
00:39:21,160 --> 00:39:24,960
Speaker 4: is a similar concept of byside external alpha capture. So

681
00:39:25,000 --> 00:39:28,480
Speaker 4: there are firms that are trying to get ideas from

682
00:39:28,520 --> 00:39:31,759
Speaker 4: hedge funds, small edge funds. They don't have scale, they

683
00:39:31,840 --> 00:39:34,719
Speaker 4: can aggregate them and then they make into a portfolio.

684
00:39:34,800 --> 00:39:37,960
Speaker 4: That's a new business model. I don't know how scalable

685
00:39:37,960 --> 00:39:40,280
Speaker 4: it is, how sustainable it is, but that's an idea.

686
00:39:41,040 --> 00:39:45,040
Speaker 4: There is definitely an expansion into privates. I have like

687
00:39:45,200 --> 00:39:48,160
Speaker 4: zero skill or zero divisibility to this stuff, so that's

688
00:39:48,320 --> 00:39:50,800
Speaker 4: really another question for somebody else. And then there is

689
00:39:50,800 --> 00:39:55,960
Speaker 4: always product innovation. Every strategy is continuously innovating, has to change.

690
00:39:56,040 --> 00:39:59,640
Speaker 4: So just look at where fundamental Equities was one hundred

691
00:39:59,680 --> 00:40:02,800
Speaker 4: years ago. Go right, the recommendation was invest in a

692
00:40:02,880 --> 00:40:06,200
Speaker 4: railway single stock and you know, be happy. And now

693
00:40:06,239 --> 00:40:08,160
Speaker 4: we have, you know, and now we spend hundreds of

694
00:40:08,160 --> 00:40:11,240
Speaker 4: millions of dollars in alternative data and there are tools

695
00:40:11,280 --> 00:40:13,680
Speaker 4: and stuff. So what is it in ten years I

696
00:40:13,719 --> 00:40:15,360
Speaker 4: don't know, but it will be very different than it

697
00:40:15,440 --> 00:40:15,840
Speaker 4: is today.

698
00:40:16,200 --> 00:40:18,719
Speaker 3: I remember, you know, when I was over twenty years

699
00:40:18,719 --> 00:40:21,160
Speaker 3: ago and I first got interested in markets, picking up

700
00:40:21,200 --> 00:40:23,920
Speaker 3: the Intelligent Investor because of course, you know, Buffett and

701
00:40:23,960 --> 00:40:26,080
Speaker 3: Munger were into it and like reading is like and

702
00:40:26,080 --> 00:40:30,040
Speaker 3: so if you buy the Brooklyn rail bond yielding eight percent,

703
00:40:30,160 --> 00:40:31,160
Speaker 3: I was like, what is this?

704
00:40:32,320 --> 00:40:32,480
Speaker 1: Yeah?

705
00:40:32,520 --> 00:40:34,640
Speaker 3: I just thought it seems so disconnected from me. I mean,

706
00:40:34,680 --> 00:40:36,160
Speaker 3: I'm sure there's a lot of deep wisdom and I

707
00:40:36,160 --> 00:40:38,239
Speaker 3: probably would have like internalized it. Yeah, but just in

708
00:40:38,320 --> 00:40:40,000
Speaker 3: terms of like what they were talking about, it seems

709
00:40:40,040 --> 00:40:42,520
Speaker 3: so funny because of how antique it all seemed.

710
00:40:42,719 --> 00:40:47,000
Speaker 4: Totally. Yeah. And so now pms are quantitative. Fundamental pms

711
00:40:47,000 --> 00:40:50,440
Speaker 4: tend to be quantitatively quite literate. In the future they

712
00:40:50,480 --> 00:40:54,120
Speaker 4: will be even different. Maybe they will be prompt experts.

713
00:40:54,160 --> 00:40:54,520
Speaker 4: I don't know.

714
00:40:54,800 --> 00:40:58,440
Speaker 3: Can you be a fundamental PM by just being a

715
00:40:58,840 --> 00:41:02,120
Speaker 3: domain expert in a certain area, say like you're really

716
00:41:02,320 --> 00:41:06,280
Speaker 3: understand biotech, or say you really understand the semiconductor industry

717
00:41:06,320 --> 00:41:08,719
Speaker 3: and you want to trade chip stocks versus and not

718
00:41:08,920 --> 00:41:11,680
Speaker 3: really have that sort of quant background but some other expertise.

719
00:41:11,840 --> 00:41:15,160
Speaker 4: So being a domain expert is definitely a necessary condition.

720
00:41:15,440 --> 00:41:19,160
Speaker 4: You absolutely need to be a domain expert. And since

721
00:41:19,200 --> 00:41:22,440
Speaker 4: you make the example of healthcare super domain experts, so

722
00:41:22,560 --> 00:41:26,040
Speaker 4: a lot of good healthcare pms have either worked in

723
00:41:26,120 --> 00:41:29,680
Speaker 4: healthcare companies they have never practiced, but they are domain expert.

724
00:41:30,120 --> 00:41:32,840
Speaker 4: Is is it sufficient to be just a domain expert.

725
00:41:32,920 --> 00:41:33,000
Speaker 1: No.

726
00:41:33,280 --> 00:41:35,680
Speaker 4: I think that you need to be able also to

727
00:41:35,760 --> 00:41:39,880
Speaker 4: monetize and to risk manage your portfolio, and that's very difficult.

728
00:41:40,120 --> 00:41:42,839
Speaker 4: So that's not sufficient, but it's definitely necessary.

729
00:41:43,080 --> 00:41:45,480
Speaker 2: How important are the data sets? Like what if I'm

730
00:41:45,640 --> 00:41:50,360
Speaker 2: just really good at finding original and alternative data someone's analysts.

731
00:41:50,480 --> 00:41:55,240
Speaker 4: Yeah, it varies a lot, so some pms, well, okay,

732
00:41:55,239 --> 00:41:59,920
Speaker 4: first of all, for systematic it matters a lot period Unconditionally,

733
00:42:00,560 --> 00:42:04,320
Speaker 4: for discretionary pms, it varies a lot. So some pms

734
00:42:04,800 --> 00:42:09,160
Speaker 4: will use alternative data, some will do deep research and

735
00:42:09,239 --> 00:42:13,880
Speaker 4: think three months to a year ahead. And the reality

736
00:42:13,920 --> 00:42:15,959
Speaker 4: is that there are not that many data that really

737
00:42:16,000 --> 00:42:18,680
Speaker 4: help you think at that horizon. So we don't live

738
00:42:18,680 --> 00:42:22,760
Speaker 4: in the world of really really big data for fundamental thinking.

739
00:42:23,200 --> 00:42:24,360
Speaker 4: So I think that's interesting.

740
00:42:25,280 --> 00:42:27,719
Speaker 2: I have just one more question, which is what do

741
00:42:27,760 --> 00:42:32,959
Speaker 2: you find most satisfying about your job? What gives you

742
00:42:33,000 --> 00:42:36,440
Speaker 2: the needs yeah or jobs? Yeah? What gives you the

743
00:42:36,440 --> 00:42:39,280
Speaker 2: most pleasure on a data day basis? Do you feel

744
00:42:39,320 --> 00:42:43,040
Speaker 2: fantastic if China devalues the un and you look at

745
00:42:44,000 --> 00:42:47,239
Speaker 2: positioning across the firm and you're not going under, Or

746
00:42:47,400 --> 00:42:50,960
Speaker 2: do you feel great if you identify a particular strategy

747
00:42:51,040 --> 00:42:51,879
Speaker 2: or something like that.

748
00:42:52,560 --> 00:42:54,680
Speaker 4: Now, the thing that gives me most pleasure when I

749
00:42:54,760 --> 00:42:58,200
Speaker 4: work is when I do something that is useful and

750
00:42:58,239 --> 00:43:01,880
Speaker 4: it works for others. So I just love the social

751
00:43:01,920 --> 00:43:06,239
Speaker 4: aspect of working, Like it's actually a job where you

752
00:43:06,320 --> 00:43:09,279
Speaker 4: can be of some use to other people, and I

753
00:43:09,400 --> 00:43:12,920
Speaker 4: just enjoy that. So when things work out, like you

754
00:43:13,000 --> 00:43:16,720
Speaker 4: come up with an idea after multiple failures and it works,

755
00:43:16,760 --> 00:43:19,920
Speaker 4: you implemented, and somebody else uses it or finds a

756
00:43:20,000 --> 00:43:23,000
Speaker 4: value to this, and everybody is happier and like and

757
00:43:23,040 --> 00:43:26,680
Speaker 4: we get drunk together. That's great, all right.

758
00:43:27,000 --> 00:43:30,640
Speaker 2: Giuseppe Palia logo aka Gappy, Thank you, so much for

759
00:43:30,719 --> 00:43:32,520
Speaker 2: coming on all blots. Really appreciate it.

760
00:43:32,680 --> 00:43:33,560
Speaker 4: Thank you, thank you.

761
00:43:33,600 --> 00:43:50,239
Speaker 3: That was fantastic, Joe.

762
00:43:50,320 --> 00:43:52,439
Speaker 2: I feel like that's good life advice. If it all

763
00:43:52,560 --> 00:43:55,440
Speaker 2: ends in people getting drunk, it's usually no, wait, that

764
00:43:55,480 --> 00:43:58,840
Speaker 2: doesn't make sense. Sometimes it's really bad, yeah, say never mind.

765
00:43:58,960 --> 00:43:59,959
Speaker 2: But sometimes it's great.

766
00:44:00,280 --> 00:44:03,640
Speaker 3: Sometimes it's good. I love that line. I feel like

767
00:44:04,000 --> 00:44:06,600
Speaker 3: the world belongs to the obsessed. It's just like a

768
00:44:06,640 --> 00:44:09,439
Speaker 3: really good line. That's sort of ominous to me because

769
00:44:09,480 --> 00:44:11,920
Speaker 3: I don't really get obsessed with anything besides country music.

770
00:44:12,000 --> 00:44:13,640
Speaker 3: And then the rest of my time, I'm just like

771
00:44:13,840 --> 00:44:16,440
Speaker 3: I want to talk about hedge funds one day, and

772
00:44:16,440 --> 00:44:17,680
Speaker 3: then the next day I want to talk about like

773
00:44:17,680 --> 00:44:18,120
Speaker 3: how energy.

774
00:44:18,320 --> 00:44:19,840
Speaker 2: Yeah, I was going to say, it's not really like

775
00:44:20,160 --> 00:44:22,880
Speaker 2: get obsessed. It's just you flip. Then I'm an obsession

776
00:44:22,920 --> 00:44:24,000
Speaker 2: to obsession, so.

777
00:44:23,960 --> 00:44:26,960
Speaker 3: It's not real obsession. It's kind of delet Wait, Tracy,

778
00:44:28,120 --> 00:44:30,560
Speaker 3: have I told you about when I got a job

779
00:44:30,640 --> 00:44:32,319
Speaker 3: offer at a prop trading shop?

780
00:44:33,160 --> 00:44:35,200
Speaker 2: This vaguely rings a bell.

781
00:44:35,239 --> 00:44:36,520
Speaker 3: So can I tell a quick story?

782
00:44:36,640 --> 00:44:37,040
Speaker 2: Go for it?

783
00:44:37,080 --> 00:44:39,600
Speaker 3: So I had traded stocks in college just because it

784
00:44:39,640 --> 00:44:41,080
Speaker 3: was like the dog Come era. It was fun, it

785
00:44:41,160 --> 00:44:44,080
Speaker 3: was very easy. Everything was going up. I managed to

786
00:44:44,120 --> 00:44:46,600
Speaker 3: sell for excellent reasons a good time, and I didn't

787
00:44:46,640 --> 00:44:48,279
Speaker 3: lose all my money anyway. I was always I got

788
00:44:48,360 --> 00:44:52,160
Speaker 3: interested in markets. Then I graduated with my useless liberal

789
00:44:52,239 --> 00:44:55,200
Speaker 3: arts degree and I had a job. I was making

790
00:44:55,200 --> 00:44:57,239
Speaker 3: minimum wage working at a Delhi and I saw this

791
00:44:57,600 --> 00:45:00,760
Speaker 3: help wanted ad at a prop trading shop and all Austin, Texas,

792
00:45:01,480 --> 00:45:03,880
Speaker 3: and it didn't seem like they had many requirements, so

793
00:45:03,920 --> 00:45:07,680
Speaker 3: I went. They asked me about my personal trading. I

794
00:45:07,760 --> 00:45:11,719
Speaker 3: played ping pong against the CEO. I played this video

795
00:45:11,840 --> 00:45:14,839
Speaker 3: game that involved me using two joysticks. One was to

796
00:45:14,920 --> 00:45:17,120
Speaker 3: control the tilt of a triangle and the other one

797
00:45:17,160 --> 00:45:19,000
Speaker 3: was to control the space and I kept it in

798
00:45:19,040 --> 00:45:22,080
Speaker 3: the square already, its weird. And I did this other

799
00:45:22,120 --> 00:45:24,560
Speaker 3: thing where I like typed without like too many typos

800
00:45:24,600 --> 00:45:26,160
Speaker 3: and stuff like that. And there were like two hundred

801
00:45:26,160 --> 00:45:30,200
Speaker 3: people applied and second round, I got one of the

802
00:45:30,280 --> 00:45:35,680
Speaker 3: four spots that they offered, and for reasons that still

803
00:45:35,719 --> 00:45:39,440
Speaker 3: allude me to this day, I didn't take the job.

804
00:45:39,600 --> 00:45:43,000
Speaker 3: I was enjoying making minimum wage at the deli. All

805
00:45:43,000 --> 00:45:44,879
Speaker 3: my friends worked there. It was like the cool place

806
00:45:44,920 --> 00:45:47,120
Speaker 3: to work in Austin. I didn't feel like giving that up,

807
00:45:47,440 --> 00:45:49,520
Speaker 3: and I didn't, and I just like, I always think

808
00:45:49,520 --> 00:45:52,120
Speaker 3: about what if, what does my life look like if

809
00:45:52,160 --> 00:45:55,960
Speaker 3: I took that job? The strangest most inexplicable career decision

810
00:45:56,040 --> 00:45:58,919
Speaker 3: I could ever imagine, not taking a trading job from

811
00:45:58,920 --> 00:46:01,319
Speaker 3: a five dollar minimum way job or whatever it is

812
00:46:01,360 --> 00:46:03,040
Speaker 3: at the time. Anyway, I'll never know.

813
00:46:03,239 --> 00:46:06,640
Speaker 2: Okay, Well, I once got offered a specialty sales position

814
00:46:07,080 --> 00:46:10,720
Speaker 2: in bank equities at a Swiss bank, and I never

815
00:46:10,920 --> 00:46:14,400
Speaker 2: question what my future would have been had I taken

816
00:46:14,480 --> 00:46:17,479
Speaker 2: that job. I'm very satisfied, but I actually have a question.

817
00:46:17,520 --> 00:46:19,680
Speaker 2: Do you think you were put off by the weirdness

818
00:46:19,840 --> 00:46:22,080
Speaker 2: no interview process? Like did you think that you were

819
00:46:22,080 --> 00:46:25,279
Speaker 2: going to be playing ping pong and like moving joysticks

820
00:46:25,360 --> 00:46:26,360
Speaker 2: as part of the job.

821
00:46:26,560 --> 00:46:28,680
Speaker 3: That was fun? And I didn't even beat the CEO

822
00:46:28,719 --> 00:46:31,480
Speaker 3: in ping pong. She beat me, but she still hired me.

823
00:46:31,719 --> 00:46:35,279
Speaker 3: I don't, no, I don't know why I can't. The

824
00:46:35,320 --> 00:46:37,360
Speaker 3: only thing I could explain is that in my post

825
00:46:37,360 --> 00:46:39,640
Speaker 3: college life, I had a cool job where I got

826
00:46:39,640 --> 00:46:41,239
Speaker 3: to hang out with my friends in the back of

827
00:46:41,280 --> 00:46:43,839
Speaker 3: this deli at a grocery store. I didn't really feel

828
00:46:43,880 --> 00:46:44,319
Speaker 3: like giving it.

829
00:46:44,280 --> 00:46:47,480
Speaker 2: Up, just yet all right, Well, I do feel like

830
00:46:48,760 --> 00:46:51,279
Speaker 2: coming out of that conversation with Giesseppe, I feel like

831
00:46:51,280 --> 00:46:54,440
Speaker 2: I have a much better conception of how multistrat actually

832
00:46:54,440 --> 00:46:56,239
Speaker 2: works and what people are sort of doing on a

833
00:46:56,280 --> 00:46:58,759
Speaker 2: day to day basis, and also just maybe a better

834
00:46:58,840 --> 00:47:02,160
Speaker 2: understanding of some of the terminology around the industry totally.

835
00:47:02,200 --> 00:47:04,759
Speaker 3: So now we'll probably do more episodes, but I feel

836
00:47:04,840 --> 00:47:07,640
Speaker 3: like I'm now like roughly grounded in at least some

837
00:47:08,040 --> 00:47:08,919
Speaker 3: core ideas here.

838
00:47:09,040 --> 00:47:13,440
Speaker 2: Yeah, and everyone should definitely check out Gappy's Buyside quant

839
00:47:13,600 --> 00:47:17,759
Speaker 2: Job Advice. It's nine pages and it actually it goes

840
00:47:17,800 --> 00:47:20,400
Speaker 2: into some detail on the structure of the industry itself

841
00:47:20,600 --> 00:47:24,040
Speaker 2: of how you know quantitative hedge funds actually work, and

842
00:47:24,080 --> 00:47:25,960
Speaker 2: like who are the big names and things like that.

843
00:47:26,239 --> 00:47:29,759
Speaker 2: So anyone's interested in the space, definitely check it out.

844
00:47:29,880 --> 00:47:30,600
Speaker 2: Shall we leave it there?

845
00:47:30,640 --> 00:47:31,319
Speaker 3: Let's leave it there.

846
00:47:31,560 --> 00:47:34,400
Speaker 2: This has been another episode of the Odd Thoughts podcast.

847
00:47:34,480 --> 00:47:37,719
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway and.

848
00:47:37,680 --> 00:47:40,480
Speaker 3: I'm Jill Wisenthal. You can follow me at The Stalwart.

849
00:47:40,520 --> 00:47:45,399
Speaker 3: Follow our guest Joseppi Polyioligo aka Gappy. He's Double Underscore

850
00:47:45,760 --> 00:47:49,840
Speaker 3: Palyioligo on Twitter Follow our producers Carmen Rodriguez at Kerman

851
00:47:49,920 --> 00:47:53,600
Speaker 3: Ermann Dashil Bennett a Dashbot, Killed Brooks at kill Brooks.

852
00:47:53,800 --> 00:47:56,319
Speaker 3: Thank you to our producer Moses on Them. For more

853
00:47:56,520 --> 00:47:59,279
Speaker 3: odd Lots content, go to Bloomberg dot com slash odd Lots,

854
00:47:59,280 --> 00:48:01,880
Speaker 3: where we have transfer a blog and a newsletter. And

855
00:48:01,960 --> 00:48:04,040
Speaker 3: check out the discord where you could chat with fellow

856
00:48:04,040 --> 00:48:08,040
Speaker 3: listeners twenty four to seven discord dot gg slash odd Lots.

857
00:48:08,360 --> 00:48:10,920
Speaker 2: And if you enjoy all Lots, if you like it

858
00:48:10,960 --> 00:48:14,080
Speaker 2: when we talk to practitioners in a particular space such

859
00:48:14,080 --> 00:48:17,320
Speaker 2: as multi strat, then please leave us a positive review

860
00:48:17,400 --> 00:48:20,799
Speaker 2: on your favorite podcast platform. And remember, if you are

861
00:48:20,840 --> 00:48:23,279
Speaker 2: a Bloomberg subscriber, you can listen to all of our

862
00:48:23,320 --> 00:48:26,200
Speaker 2: episodes absolutely ad free. All you need to do is

863
00:48:26,239 --> 00:48:30,200
Speaker 2: connect your Bloomberg account with Apple Podcasts. Thanks for listening.