Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the All Thoughts Podcast.
Speaker 2: I'm Tracy Alloway.
Speaker 3: And I'm Joe Wysenthal.
Speaker 2: Joe, we're back on the multi strat beat.
Speaker 3: I love this beat. I think it's really interesting. There's
Speaker 3: a lot we've learned, but there's a lot we haven't learned.
Speaker 3: I love this beat. If you said we're going to
Speaker 3: just do ten episodes about this, I'd be like, yeah,
Speaker 3: that's fine.
Speaker 2: Well, yeah, I look forward to part six hundred and
Speaker 2: seventy eight in our ongoing attempt to understand multi strat
Speaker 2: hedge funds. But you know, we've been sort of learning
Speaker 2: as we go along, and there are a bunch of
Speaker 2: questions that I still have. One of them is there
Speaker 2: seem to be a lot of different opinions and variation
Speaker 2: pod shops right on how exactly they can be designed.
Speaker 3: Right, So there's different sort of structures that I understand.
Speaker 3: There's different compensation structures. There's different degrees to which the
Speaker 3: different pods so to speak, coordinate with each other. There's
Speaker 3: different degrees to which they like centralize ideas and research.
Speaker 3: So like I get that, there's still some big questions
Speaker 3: in my mind, and I'll just say one of the
Speaker 3: big ones right off the bat, which is, if you
Speaker 3: have a bunch of teams doing a bunch of different
Speaker 3: strategies and trading a bunch of things, why are the
Speaker 3: returns good instead of average? Because in my intuition, if
Speaker 3: you have a bunch of teams like, okay, you're diversifying
Speaker 3: alpha across a bunch of things, but great, But then
Speaker 3: you have a bunch, my gut intuition will be like,
Speaker 3: you don't get great returns, you get average returns, right,
Speaker 3: And yet many of them put up really impressive returns
Speaker 3: year after year after year, And I don't think I
Speaker 3: totally have a grasp of life.
Speaker 2: Well, yes, and this is a question that I have,
Speaker 2: which is eventually the pod shop. Some of them are
Speaker 2: getting very very big, right, and so if you have
Speaker 2: one thousand pods working under your roof, that's a bit extreme.
Speaker 2: But at some point aren't you just sort of replicating
Speaker 2: the market and that alpha opportunity as you just described
Speaker 2: kind of goes away. Well, on that note, I am
Speaker 2: happy to say we have the perfect guests to discuss
Speaker 2: all of this. So these sort of variations behind multistrap
Speaker 2: funds and also the math that actually powers it. We're
Speaker 2: going to be speaking with Dan Morillo. He is the
Speaker 2: co founder of Freestone Grove Partners and also ex Citadel,
Speaker 2: so again, the perfect person to be speaking to.
Speaker 4: Dan.
Speaker 2: Welcome to the show.
Speaker 5: Thank you, thank you for having me.
Speaker 2: I guess my first question is why are we talking
Speaker 2: to you?
Speaker 3: Yeah, why are we talking?
Speaker 5: Well, you're probably in a better position to answer than me,
Speaker 5: but I guess I'll tell you my background and hopefully
Speaker 5: that helps a little bit. So I've been about twenty
Speaker 5: five years now dating myself in the by side, on
Speaker 5: the hedge fund by side in particular, and I grew
Speaker 5: up on the quantitative side of the world. I thought
Speaker 5: I was going to be a professor, and then I
Speaker 5: realized that life is more exciting on the industry side
Speaker 5: of things. And I've done a wide range of roles
Speaker 5: in the quote quant side of the world, so everything
Speaker 5: from you know, at some point I was the lead
Speaker 5: of the global long short business at Parkley's Global Investors
Speaker 5: before Black Crok required them. At Blackrok, I stuck around
Speaker 5: for a bit. I at some point ran the research
Speaker 5: group for I Shares. I also was one of the
Speaker 5: founders of the model Solutions business there as you said,
Speaker 5: I was etc.
Speaker 4: Where I had.
Speaker 5: Responsibility for the Equity Quantitariy Research Group that did a
Speaker 5: lot of the stuff that you guys have talked about,
Speaker 5: risk model stuff and the hedging stuff and all of
Speaker 5: these sort of things. I also had responsibility for the
Speaker 5: Center Book where a lot of that central stuff that
Speaker 5: you also have talked about happens, and then most recently
Speaker 5: have founded co founded that also does the pod long
Speaker 5: short thing. So I'd like to think I have some expertise,
Speaker 5: but I guess you'd tell me after you ask me
Speaker 5: all these questions.
Speaker 3: I have a really rudimentary question, what does the word
Speaker 3: quant mean in finance?
Speaker 5: Actually? So this is a good point, right, I think
Speaker 5: it can mean lots of things. From my point of view.
Speaker 5: The thing that has always been attracted to me about
Speaker 5: the quant side of things is the idea that you
Speaker 5: can be disciplined in how you make decisions.
Speaker 4: Right.
Speaker 5: You can be quantitative in the purely mathematical sense, like
Speaker 5: you brand some code and there's lots of numbers, while
Speaker 5: still not actually applying that much judgment. You can also
Speaker 5: actually be quite disciplined and systematic without using a lot
Speaker 5: of quant tools. Right. I think the right way of
Speaker 5: doing quant is where you also mix these two together, right,
Speaker 5: when you have the ability to bring in the judgment
Speaker 5: that comes from understanding what the humans in the market
Speaker 5: are doing, but to do certa in a way that
Speaker 5: is repeatable and disciplined, and that tends to require quantitative
Speaker 5: modeling tools, whether that's risk models, focusing, evaluation, attribution, all
Speaker 5: of these sorts of things, right, And in fact, that's
Speaker 5: the sort of thing that attracted me. That is sort
Speaker 5: of a I guess a common threat through all of
Speaker 5: these jobs that I mentioned that I've had is the
Speaker 5: idea that you can do this this sort of systematic
Speaker 5: modeling work not just with the numbers themselves, but also
Speaker 5: with the humans that participate in the market. Are also
Speaker 5: subject to analysis, right, whether you think about sentiment measurement
Speaker 5: or the sort of questions you guys have asked in
Speaker 5: this podcast, right, what is the right way to organize
Speaker 5: a team? You know, how many teams should you have,
Speaker 5: how should you pay them? What fees should you charge
Speaker 5: with those? These are all subject to analysis, right. So
Speaker 5: I like the idea that you can do the quant
Speaker 5: thing on human behavior, right.
Speaker 2: Oh, this is exactly what I wanted to ask you about. Actually.
Speaker 2: So if you go to Freestone's website, you can see
Speaker 2: that there are two partners on the front page, and
Speaker 2: you are the quantitative one, and you do have a
Speaker 2: large number of quant researchers. What's the value added by
Speaker 2: those quants to a fundamental equities fund?
Speaker 5: Yeah? I think the way you want to think about
Speaker 5: it is that the insight that is associated with understanding
Speaker 5: the mechanics of a firm, which is the fundamental in
Speaker 5: this case for equities. You know, the job of the
Speaker 5: PM analyst is to understand what drives revenue, earnings, margins.
Speaker 4: Et cetera.
Speaker 5: And in portucur what is likely to be surprising about
Speaker 5: those next time they know earnings or over the next
Speaker 5: couple of quarters. Right, the way you make money is
Speaker 5: you have a view that is different from that of
Speaker 5: the market and people come to agree with you. Right,
Speaker 5: that's sort of success.
Speaker 4: Right.
Speaker 5: And in that effort, whether it's modeling the firms, whether
Speaker 5: it's understanding what about that surprise is really surprising about
Speaker 5: the firm versus something that's happening in the broader market.
Speaker 5: The data that comes into all of this, right, alternative
Speaker 5: data stuff. All of that requires a huge amount of
Speaker 5: investment on the technology side, the analytics side, the forecasting side. Right.
Speaker 5: It's no longer the case that you can be a
Speaker 5: smart guy reading tank q's in ten case, as might
Speaker 5: have been the case twenty five years ago, and just
Speaker 5: kind of see what the surprise is going to be.
Speaker 5: It requires a significant investment in being the most sophisticated
Speaker 5: person at doing that job. And that's not a thing
Speaker 5: you can do without all of that investment on the
Speaker 5: quantitative tooling.
Speaker 4: Right.
Speaker 5: There's also all the behavioral stuff. Right. Humans have the
Speaker 5: ability to really get into the detail of what the
Speaker 5: firm is doing. Right. Many of the people who are
Speaker 5: very good at there's are people who have been covering
Speaker 5: the same firm literally for a decade. Right. They know
Speaker 5: their CFO or the CEO, the product. You know, they've
Speaker 5: visited the factories, and so they have this ability to
Speaker 5: preak up on really subtle patterns. But they're also human, right,
Speaker 5: and humans come with biases. Right. You project your own
Speaker 5: patterns of sort of your view of the world into
Speaker 5: what's happening on the ground, and so it is also
Speaker 5: helpful to think about how do you become as disciplined
Speaker 5: as possible in that process, right, So you think about
Speaker 5: risk models, attribution questions, how can you tell luck versus skill? Right?
Speaker 5: Most humans, if you do well, you tend to think
Speaker 5: it's all about you. And if you do poorly, well,
Speaker 5: there wasn't my fault, my fault, right, And so these
Speaker 5: processes of how do you make sure that humans are
Speaker 5: as discipline as possible again requires huge investment in that
Speaker 5: quantitative analytical capability. So that's kind of what you bring
Speaker 5: to the table.
Speaker 3: Right, So we'll get into how you go about measuring
Speaker 3: the skill of your portfolio managers and breaking all these
Speaker 3: things down, and we'll talk about that a lot. When
Speaker 3: you founded Freestone Growth, you and your co founder Todd Barker,
Speaker 3: you must think there's an opportunity there, right, You must
Speaker 3: think there's like some opportunity out there to make money,
Speaker 3: to have a fund that's different than something that already
Speaker 3: exists on the market, that you bring something to the
Speaker 3: table that you could structure a company in some way
Speaker 3: that's advantageous. What is the sort of theory or thesis
Speaker 3: behind Freestone Growth such that you wanted to build something new?
Speaker 5: Yeah, so you're correct, We do think we can compete
Speaker 5: at the highest level of the industry, right, Otherwise we
Speaker 5: would have started this time. The way in which we
Speaker 5: think we can do this isn't some new magic thing, right, like, oh,
Speaker 5: only we can do X, Y and Z.
Speaker 4: Right.
Speaker 5: A lot of how we and this is what we
Speaker 5: tell our clients is that in having spent all this
Speaker 5: time looking at what works and what doesn't in the
Speaker 5: space I call it the multi strategy or MULTIPM space,
Speaker 5: we have a view that you can sort of be
Speaker 5: optimal around key business decisions, right, the number of analysts
Speaker 5: and pms you have in your platform, the way you
Speaker 5: organize them, the way you think about the incentives or
Speaker 5: how they're compensated, the right mix of counnitata versus fundamental
Speaker 5: in a way that sort of is the best of
Speaker 5: what we've seen around. Right. So it's not so much oh,
Speaker 5: there's this one thing that is massively different about us,
Speaker 5: and instead lots of little things that we think you
Speaker 5: can optimize in a way that many of the other
Speaker 5: platforms for various reasons, haven't gotten to, particularly with the
Speaker 5: advent of a lot of new ones. Right, where you
Speaker 5: end up with business design that we happen to think
Speaker 5: is not nearly as optimal as it could be. Right,
Speaker 5: So it's sort of optimized the business as sort of
Speaker 5: the pitch and then run each piece the best you can.
Speaker 5: Does that make sense?
Speaker 3: Yeah?
Speaker 2: Well, on this note, so there's something that kept coming
Speaker 2: up when we were preparing for this podcast. But people
Speaker 2: keep talking about Dan's math. Can you put your professorial
Speaker 2: hat on and explain to us what exactly is Dan's
Speaker 2: math and how does it come into play when it
Speaker 2: comes to designing and optimizing the size of your firm.
Speaker 4: Yeah?
Speaker 5: So, first, in my defense, I did not come up
Speaker 5: with that. I believe it was actually somebody from BOOMEERG
Speaker 5: that came up with that after some interview that they
Speaker 5: did with us early on. But yeah, question. Look, the
Speaker 5: point is that many of the things that you think about,
Speaker 5: which range from how many people should you having a platform,
Speaker 5: what sort of risk models should you run, what risks
Speaker 5: should you take, how should you do capital location, these
Speaker 5: are things that are subject to systematic analysis, right, and
Speaker 5: so this idea of quote de math is that many
Speaker 5: of these decisions you don't have to wave your hands around, right,
Speaker 5: there's sort of reasonably clear answers about them, right, there's
Speaker 5: a couple of ones that and we can chase down
Speaker 5: whichever ones as you like. But one of the ones
Speaker 5: that in my mind is the most important is there's
Speaker 5: been this sort of press in the industry with this
Speaker 5: idea that more is always better, Right. You want to
Speaker 5: have more porfan managers, more as more assets like that
Speaker 5: that scales a sort of underlying strength.
Speaker 4: Right.
Speaker 5: It actually goes back to your question around how come
Speaker 5: do you get good results out of lots of people? Right?
Speaker 5: And the answer is to listener, is actually not wrong.
Speaker 1: Right.
Speaker 5: There comes a point where adding more people actually doesn't
Speaker 5: make any difference. Right, And so if you just allow
Speaker 5: me two minutes to set up a little example. Right. So,
Speaker 5: the way this business works is you're hiring individual risk
Speaker 5: takers let's call them analysts. Right, So there's some potential
Speaker 5: pool of people you can hire, and assuming you have
Speaker 5: good hiring practices, you expect to hire people who have
Speaker 5: some mean performance. Think of that as a sharp ratio.
Speaker 5: Let's say that sharp ratio is point seventy five, right,
Speaker 5: So have shot ratio point seventy five means that if
Speaker 5: you take a risk of a dollar of risk, you
Speaker 5: expect to januarate seventy five cents of per that amount
Speaker 5: of risk that you deployed, right. And so you want
Speaker 5: to think of performance in sharp ratio space, right, because
Speaker 5: in different spaces people have different risk. Right. There's you know,
Speaker 5: biotech names are riskier than say, bank names, and so
Speaker 5: you want to adjust for that. So typically you want
Speaker 5: to think in sharp ratio space. So you hire folks
Speaker 5: you expect to have some mean distribution, some mean outcome. Right.
Speaker 5: So I hire a person. I don't know what their
Speaker 5: sharp ratio is going to be. I hope it's good.
Speaker 5: And on avers I get people who are let's say
Speaker 5: point seventy five right. Some people are going to be
Speaker 5: better than that. Some people are going to be worse
Speaker 5: than that. Maybe I end up needing to hire them, right,
Speaker 5: But I get some distribution of them, right, and then
Speaker 5: you give them capital and they run a couple over
Speaker 5: the time. Right. And so the magic of the versification
Speaker 5: is that you get a higher sharp ratio as you
Speaker 5: add people. Right. If the correlation was exactly zero, then
Speaker 5: the more people you add, essentially, the more your sharp
Speaker 5: ratio increases. It increases where there's square root of end. Essentially,
Speaker 5: if there's correlation. However, there's like a maximum limit of
Speaker 5: how much your aggregator sharp ratio can be. Right. So
Speaker 5: let's take a simple example. Let's say these point seventy
Speaker 5: five people that you have on average, Let's say they're
Speaker 5: correlated by ten percent, which most people will tell you
Speaker 5: that sounds kind of low, not a lot of correlation.
Speaker 5: Then there's a maximum limit of what your starbration can
Speaker 5: be about two point three even if you have an
Speaker 5: infinite number of people. So you're intuition that if you
Speaker 5: add lots and lots of people, you add some gate
Speaker 5: to some quote average return is correct, it's just what
Speaker 5: is the scale of that average return, right, And so
Speaker 5: if you add lots and lots of people, you get
Speaker 5: to that sort of maximum level. And the thing that
Speaker 5: really matters is the correlation, right, So it is incredibly
Speaker 5: hard to get zero correlation like that just doesn't really happen.
Speaker 3: So, just to be clear what we're talking about when
Speaker 3: you say correlation, you hire one PM and they trade semiconductors.
Speaker 3: You hire another PM and they trade interest rates, or
Speaker 3: maybe they trade banks or something like that. Yeah, but
Speaker 3: because things in the market are generally correlated, you could
Speaker 3: have these different people all around the world, and implicitly,
Speaker 3: even though it looks like they have their own focus
Speaker 3: on the market, they might all implicitly be making money
Speaker 3: based on their read of the FED or something like that,
Speaker 3: and thus their returns are correlated. And therefore, even if
Speaker 3: they're really all really good at their jobs, that caps
Speaker 3: the amount of firm wide sharp by virtue of the
Speaker 3: fact that they're not really adding diversification.
Speaker 5: That is exactly correct. So, and it's as simple as
Speaker 5: if you were to observe somebody's return literally every day, right,
Speaker 5: and we observe the other persons return every day. You
Speaker 5: can just computer correlation, put it in Excel computer correlation.
Speaker 5: And if that number is low, you get more juice
Speaker 5: out of adding more people. If that almost is hi,
Speaker 5: you get less juice. To point, it matters enormously. So
Speaker 5: in that example, that maximum is about two point four.
Speaker 5: If your mean person is point seventy five, like with
Speaker 5: an infinite number right at correlation of ten percent, Let's
Speaker 5: say your correlation is actually twenty percent, right, you know,
Speaker 5: it's obviously more, but it's still low in the grand
Speaker 5: scheme of things, then that maximum number is only one
Speaker 5: point six, right, So a little bit of correlation has
Speaker 5: an enormous impact on how much you can deliver in
Speaker 5: the end. Right, And more importantly, you get pretty close
Speaker 5: to that maximum without a lot of people.
Speaker 4: Right.
Speaker 5: So, in the example of point seventy five, in a
Speaker 5: correlation of ten percent, if I have forty five risk takers,
Speaker 5: think of them as analysts. Let's say I put them
Speaker 5: in teams of three. Right, PM team made out of
Speaker 5: three risk takers. You know, there's not that many teams, right,
Speaker 5: fifteen teams. That gives me about ninety five percent of
Speaker 5: that ultimate maximum. Right, So I don't need to have
Speaker 5: one hundred teams to get to my maximum. In fact,
Speaker 5: there comes a point where it is actually more important.
Speaker 5: Let's say you have a million dollars actually to spend on.
Speaker 5: Something could be I hire another person, but something could
Speaker 5: also be, Hey, I might produce a better piece of
Speaker 5: software to help me manage that correlation. To teach people
Speaker 5: to think about whatever their return is really independent of,
Speaker 5: you know, for example, interest rates. As you highlighted them,
Speaker 5: that actually might be a significantly better investment than adding
Speaker 5: a team, because if I reduce my correlation by a
Speaker 5: little bit, that actually gives me more juice than just
Speaker 5: adding people. Right, And to look back to that original question,
Speaker 5: what do we think might be different in terms of
Speaker 5: how you set up your business. Is again that a
Speaker 5: lot of people have gone from scale for scale, even
Speaker 5: though you don't have to, at least not for performance reasons. Right.
Speaker 5: There comes a point where you just kind of have
Speaker 5: the right scale and you're better invest better off investing
Speaker 5: in other things.
Speaker 4: Right.
Speaker 5: The reason people have gone for scale is because they
Speaker 5: want to run more money. It's not because that gives
Speaker 5: you more performance, right, at least a past a certain amount. Right.
Speaker 5: And in fact, if you think about scale, scale comes
Speaker 5: with lots of other issues. It comes with complexity. You
Speaker 5: maybe end up with more management layers, You have to
Speaker 5: worry a lot more about you know, offices and coordination
Speaker 5: and you know management, etcetera. You might actually end up
Speaker 5: reducing your performance. That that complexity costs money. Right, And
Speaker 5: so one of the key things that we say to
Speaker 5: our clients, just as an example, is we look to
Speaker 5: cap our size so that we can run the right
Speaker 5: number of people at the minimum complexity if possible, while
Speaker 5: still delivering pretty much sad level of performance.
Speaker 4: Right.
Speaker 2: Why do hedge funds promise uncorrelated returns at all? Because
Speaker 2: it feels to me, as you just said, it's very
Speaker 2: hard to get correlation down to zero. But the pitch
Speaker 2: to investors is always, here are a bunch of uncorrelated
Speaker 2: returns that we can do over and over again. And
Speaker 2: then what you see repeatedly is that when there is
Speaker 2: a big event in the market, they all have drawdowns
Speaker 2: at the same time. So why do they keep pitching
Speaker 2: on correlated returns and why do you investors keep putting
Speaker 2: money in them?
Speaker 5: Okay, so there seems to be two questions then there,
Speaker 5: which is how come are they correlated even though they
Speaker 5: claim not to be a number? One? And two is
Speaker 5: that why is that even a thing in the first place? Right,
Speaker 5: So let me start with the second one. The reality
Speaker 5: is most correlation is driven by some common effect.
Speaker 4: Right.
Speaker 5: You know you've had guests here talking about risk models
Speaker 5: where you think about sort of common factors, right. And
Speaker 5: a key reason why if you're an allocator, say you're
Speaker 5: a pension fund, you know, in university endowment, is that
Speaker 5: you get paid for taking risk. Right. A lot of
Speaker 5: the allocation is into things that are risky, and you
Speaker 5: expect to get paid for taking that risk.
Speaker 1: Right.
Speaker 5: That's sort of in a sense, that's the function of
Speaker 5: a big endowment or a big punch of fund. Right.
Speaker 5: The thing is, most of the risks that pay you
Speaker 5: those returns, whether that's you know, market as a whole,
Speaker 5: whether it's you know, individual factors like momentum that you
Speaker 5: can buy separately, you know interest rate risk, you know
Speaker 5: inflation risk. All of these things you can allocate to
Speaker 5: those for like essentially like a tenth of a cent
Speaker 5: on the dollar, right. And so if you're going to
Speaker 5: make an allocation to something else, you don't want that
Speaker 5: allocation to be the same thing you already have at
Speaker 5: essentially no fees.
Speaker 4: Right.
Speaker 5: So let's say you have a hedgehund who charges you,
Speaker 5: I don't know two and twenty, but that hedge fund
Speaker 5: has you know, typically a beta of like say fifty
Speaker 5: percent on average. Right. Then half of the money you're
Speaker 5: giving that hedge run is beta that you could buy
Speaker 5: for essentially no fees.
Speaker 4: Right.
Speaker 5: And so the advantage of a hedgehund that is able
Speaker 5: to in fact deliver on coliter rator risk is that
Speaker 5: now you can make cleaner allocation.
Speaker 1: Right.
Speaker 5: You can say, Okay, this is my market risk, this
Speaker 5: is my interest rate risk, this is my you know,
Speaker 5: I don't know housing premium, whatever it is. However, you've
Speaker 5: sort of decided to do your allocation, and then there's
Speaker 5: a piece that boosts my returns because it is not
Speaker 5: correlated to those other things, right, and so it is
Speaker 5: the right objective if you will right, if you're an
Speaker 5: allocator right. Then the question is whether people can actually
Speaker 5: execute and delivering that you know that outcome right, which
Speaker 5: is a somewhat separate question.
Speaker 3: I want to get into how you hire people at
Speaker 3: Freestone Growth and why a talented PM would come to
Speaker 3: Freestone Growth from somewhere else in the conversation, et cetera.
Speaker 3: But before we get to that, I have to imagine
Speaker 3: there's certain like information asymmetry challenges. You probably have a
Speaker 3: limited visibility into not just a PM's returns, but exactly
Speaker 3: how they achieved those returns, whether they achieve those returns
Speaker 3: in a way that demonstrates their ability to actually extract
Speaker 3: alpha rather than ride the various betas that you're trying
Speaker 3: to extract out of them. I assume, if you're starting
Speaker 3: a fund, do you think you're good at identifying the
Speaker 3: people who will come to work for you? What information
Speaker 3: do you have to use and when you're accumulating pms
Speaker 3: or analysts, what is the basic process for identifying skill
Speaker 3: before they show up on your door.
Speaker 5: That's a really good question, and obviously it's it's partly
Speaker 5: a systematic process. But you know, like with like with
Speaker 5: hiring for everything, it's a bit of an art too, right,
Speaker 5: whether you're hiring a portfin manager or you know, quantitative research,
Speaker 5: there's there's always a bit of an art associated.
Speaker 4: With it, right.
Speaker 5: The I think the key objective that you should have
Speaker 5: is do you understand via what mechanism do they deliver
Speaker 5: this skill that they claim to deliver it?
Speaker 4: Right?
Speaker 5: And so it's a good thing that you typically can't see,
Speaker 5: you know, a good tracker over returns, because then you'd
Speaker 5: be tended to based it on past returns, which is
Speaker 5: not a good idea. If it's a bad idea, we
Speaker 5: can talk about that separately. It forces us to think about, Okay,
Speaker 5: if you claim that you can generate good returns via
Speaker 5: what mechanism do you do that? Right? For a typical analyst,
Speaker 5: at least inequities, it tends to be some form of
Speaker 5: I understand what the surprises and fundamentals are going to be, right?
Speaker 5: I can tell that this firm is going to, you know,
Speaker 5: announce a billion dollars worth of revenue, whereas everybody else
Speaker 5: is expecting is going to be nine hundred or whatever. Right,
Speaker 5: And if that's a claim which tends to be the
Speaker 5: common claim, right almost by definition in that job, you
Speaker 5: can then sort of back into what sort of process
Speaker 5: leads you there? Right, what sort of modeling capability you
Speaker 5: could do?
Speaker 4: Right?
Speaker 3: Does this sort of get to what you were saying
Speaker 3: in the beginning when I ask you, like, what is
Speaker 3: the definition of quant Where it's not an enough to
Speaker 3: just be able to math that out. There has to
Speaker 3: be some ability to like have the human intuition understand
Speaker 3: how these.
Speaker 5: Things are correct. Right, So just to use these examples, right,
Speaker 5: Let's say you tell me I'm having an interview, I'm
Speaker 5: interviewing you for an analyst, and you tell me I'm
Speaker 5: great at knowing what the fundamentals are going to be, right,
Speaker 5: And I say, okay, well, do you have a track
Speaker 5: record of your own estimates? Right? So presumably for having
Speaker 5: many names you covered, you knew you had an estimate
Speaker 5: in your head about what their revenue is going to be,
Speaker 5: what the margins are going to be what their earners
Speaker 5: are going to be. I could ask you, okay, what
Speaker 5: were those estimates back in time three days before the
Speaker 5: company announced their know the results for all the names
Speaker 5: are covered back many years, right, And to be clear,
Speaker 5: I'm not necessarily looking for you to have them and
Speaker 5: give them to me. But what processes did you used
Speaker 5: to think about even understanding whether you have skill in
Speaker 5: the first place? Right? And it is not uncommon to
Speaker 5: have folks answer that question by saying, well, I don't
Speaker 5: really know, because I keep my model saying Excel, right,
Speaker 5: And I have a very complicated Excel model with all
Speaker 5: the income say madlines and all the balance sheet lines
Speaker 5: and all these things. And as the firm evolves, I
Speaker 5: changed that model, right, I change the numbers, I change
Speaker 5: my assumptions. I maybe even add in supract lines. You
Speaker 5: add more complexity in the model. And keeping track of
Speaker 5: what it was at every point in time is horse right.
Speaker 5: You and I have to save the file every day,
Speaker 5: and you have some database to figure out what it
Speaker 5: was every day and change them and do some analysis.
Speaker 1: Right.
Speaker 5: And you want to talk to the people who understand
Speaker 5: that that's the thing they should be doing, and have
Speaker 5: made some effort to move in that direction, right, Meaning
Speaker 5: there's an interest in being disciplined and understanding your own skill, right.
Speaker 5: Just that is an auto significant difference between somebody who
Speaker 5: just does it for so somebody who's interested in understanding
Speaker 5: how they do it and how they improve. Right.
Speaker 2: So, on the flip side of identifying good portfolio managers,
Speaker 2: how do good portfolio managers or why do good portfolio
Speaker 2: managers want to come work for you? Because my impression
Speaker 2: is there are giants in the multi strat world. You
Speaker 2: used to work for one of them. They can pay
Speaker 2: millions to a talented PM that they really want. How
Speaker 2: do you compete with that kind of package? Is it autonomy?
Speaker 2: Is it the culture of the firm? What is the
Speaker 2: attraction for good traders?
Speaker 5: Yeah? So it's a mix of things. Let me give
Speaker 5: you sort of what I think are the key things
Speaker 5: that might make you want to talk to us, right
Speaker 5: as opposed to stay at your big job, you know,
Speaker 5: at one of the sort of big name platforms.
Speaker 4: Right.
Speaker 5: So number one, because of this drive to scale, what
Speaker 5: has sended to happen at many of the platforms is
Speaker 5: that if you are, say a tech portfilmer manager, you're
Speaker 5: one of ten, potentially fifteen. Right, and remember you're competing
Speaker 5: for your ability to have the resources necessary to do
Speaker 5: that job really well. Right, So rundown the sort of
Speaker 5: thing you need, right, You need corporate access. Right, So
Speaker 5: you would like to have the ability to talk to CFO, CEO,
Speaker 5: you know, even IR for the companies you cover, you know,
Speaker 5: go to the conferences, do the non deal roadtro and
Speaker 5: it doesn't matter how big you are. At some point,
Speaker 5: the CFO of some firm is not going to talk
Speaker 5: to a million managers, right, so they're going to say
Speaker 5: to the big names, Okay, I'll give you two slots.
Speaker 5: They're not going to give you fifteen slots just because
Speaker 5: you have fifteen pms. In fact, they really don't want
Speaker 5: to talk to you, right, Most companies don't prefer not
Speaker 5: to talk to the investors. And so you end up
Speaker 5: in a situation where you're competing for corporate access, you're
Speaker 5: also competing for data science resources, quantitative resources, PORTOFOIO, construction
Speaker 5: and risk management resources. Meaning as that scale happens, it
Speaker 5: becomes ever harder to get what I would describe as
Speaker 5: a truly integrated in sort of partner like relationship with
Speaker 5: the resources that you have, right, And so it is
Speaker 5: not a typical to find folks in the big platforms
Speaker 5: who might like their job, might like the way they
Speaker 5: get paid, but are actually frustrated about the fact that
Speaker 5: it's a bit like being a small cog in a
Speaker 5: big place. Right. So that's one aspect of it. The
Speaker 5: second aspect of it is again the fact that the
Speaker 5: firm is really large doesn't mean that you necessarily are
Speaker 5: running any more money at a large place than you
Speaker 5: would with us. In fact, our refile managers run likely
Speaker 5: more money than they would run in most other places, right,
Speaker 5: because yes, we're small, but we also have fewer people, right,
Speaker 5: And so we're looking to run as large a scale
Speaker 5: a team as you could with fewer teams, if that
Speaker 5: makes sense. It's a distinction. And so from the perfimer
Speaker 5: manager's point of view, that's actually not that different in
Speaker 5: terms of how a risk you might get, but you
Speaker 5: get better resources, more integrated platform on the technology, risk,
Speaker 5: corporate access, etc. There's other things that have this flavor, right.
Speaker 5: And remember, because most folks get paid out of some
Speaker 5: share of the return that they can generate from that
Speaker 5: amount of assets. It's not like your comp is going
Speaker 5: to be terribly different. Right, if you run just as
Speaker 5: much justice and your returns are good or better because
Speaker 5: you get better resources, more integration, and a better platform,
Speaker 5: it's not obvious why it's necessarily an unattractive platform. In fact,
Speaker 5: we have found that we have hired folks that we're
Speaker 5: performer managers, are other placers that came to be analysts
Speaker 5: with us because they understand the benefit of all of
Speaker 5: those things, right, as opposed to be one of I
Speaker 5: don't know, five hundred analysts in some really large place
Speaker 5: doesn't make sense.
Speaker 2: Wait, talk more about that, because I'm I'm curious. I
Speaker 2: get the impression that a lot of multistrat firms or
Speaker 2: podshops are always going after like the star portfolio managers
Speaker 2: or people who have experience, and I'm curious, is their
Speaker 2: scope for developing talent in house? For instance? Could you
Speaker 2: hire me or Joe and train us to be a
Speaker 2: really good portfolio manager. How much flexibility is there in
Speaker 2: that career path.
Speaker 5: There's actually a decent amount of flexibility. So your preference
Speaker 5: would be not to have to rely on imperfect information,
Speaker 5: particularly if you have to promise somebody lots of things
Speaker 5: in order to come to your platform. Right. So you
Speaker 5: should have a preference to develop talent internally. The question
Speaker 5: is what sort of culture insistence do you have to
Speaker 5: make that happen, right, And I fact, I think you've
Speaker 5: had guests on in the poscat on this podcast talking
Speaker 5: about those training grounds, right, And so people understand that
Speaker 5: you should have a preference to bring in people who
Speaker 5: you can shape into who you think are going to
Speaker 5: be the best analysm, the best portfolio manager in a
Speaker 5: way that really matches with you know, your culture and
Speaker 5: the way you pay and the way the systems work. Right.
Speaker 5: Part of the problem though, is that humans are humans, right,
Speaker 5: and so even if you train somebody, you can't guarantee
Speaker 5: that they're going to stay with you, and vice versa.
Speaker 5: You might, especially if you're really large and you have
Speaker 5: to run lots of assets. In a sense, you're forced
Speaker 5: into this turnover, right, because if you have to deploy
Speaker 5: all those assets, and if somebody quits for whatever reason,
Speaker 5: maybe they just have a personal thing they leave, not
Speaker 5: because they're going somewhere else, you're sort of forcing into
Speaker 5: this replacement process. And at some point, part of the
Speaker 5: problem is you might not have the next person ready
Speaker 5: to be promoted and therefore you've got to go outside,
Speaker 5: right And I don't, to be entirely honest, I don't
Speaker 5: think there's sertably different in this industry from any other
Speaker 5: industry right where you need to hire very talented people
Speaker 5: and there's a limited number of them, and you kind
Speaker 5: of have to go through that mix of ingrown talent
Speaker 5: hiring externally, you know, some mix of the two. And yes,
Speaker 5: I could train you to be really good perfile managers.
Speaker 3: I want to get into soon, like actual how the
Speaker 3: comp part, because it's nice to talk about access to
Speaker 3: teams and you know, lean management and all that, but
Speaker 3: you know it's finance will care about paychecks a lot.
Speaker 3: But before we do, there's something you said, and it's
Speaker 3: come up before and I still have a hard time
Speaker 3: wrapping my head around it. So I'd like to hear
Speaker 3: how you clarify it. When you talk about a PM
Speaker 3: having access to a company's management team, that makes sense.
Speaker 5: I get it.
Speaker 3: Investing, you want to talk to the CFO or whatever,
Speaker 3: the CIO or whatever the CEO, But you know we're
Speaker 3: not talking Berkshire Hathaway here where you're holding a stock
Speaker 3: for twenty five years and you really get to know it.
Speaker 3: In fact, the sort of hold times for a stock
Speaker 3: within one of the within a firm like yours supposedly
Speaker 3: is extremely short, and sometimes maybe five days or ten
Speaker 3: days or one quarter or something like that, in which
Speaker 3: it's not intuitive to me that if I'm holding a
Speaker 3: stock for twenty days, it's particularly important to say no
Speaker 3: the management team the way Warren Buffett gets to know
Speaker 3: a management team. Can you explain to me the importance
Speaker 3: of that sort of insight into a company given the
Speaker 3: short holding periods, given the high amount of actual training
Speaker 3: that you do.
Speaker 5: Yeah, that's a really good question. I think it's just
Speaker 5: like you're munging two things together that don't go together.
Speaker 1: Right.
Speaker 3: Yah, that's fine.
Speaker 5: I think you want to separate the investment decision, which
Speaker 5: might be a sure horizon, versus what drives the inside
Speaker 5: that gets you to that investment decision. Right. And so
Speaker 5: the reason you want to really understand the company is
Speaker 5: because that allows you to pick up on subtle patterns
Speaker 5: about what the likely misunderstandings about that company is from
Speaker 5: everybody else. Right, So I'll repeat, the way you make
Speaker 5: money is you have a view that is different from
Speaker 5: the other marginal participant, and the way you make money
Speaker 5: is you place the trade, and then over time people
Speaker 5: come to agree with you. Right. And it's either because
Speaker 5: they eventually see the same thing that you do, right,
Speaker 5: They see the same data, they do the same analysis.
Speaker 5: Maybe you got there because your data is better, your
Speaker 5: analysis is more sophisticated, et cetera. Or the firm tells you.
Speaker 5: The firm literally comes and says, here's our earnings and
Speaker 5: here's our revenue. And you turn out to be correct
Speaker 5: versus other folks. Right, So you need that catalyst, right,
Speaker 5: And so you're playing in the same firm over and
Speaker 5: over again. But the nature of the insight is what's changing, right.
Speaker 5: And so because you know of the firm that well,
Speaker 5: and because you've been following it for ten years and
Speaker 5: go to the conferences and talk to the management, et cetera,
Speaker 5: you are able to tell that g well, this quarter,
Speaker 5: my suspicion is that people are underestimating their earnings. Maybe
Speaker 5: the next quarter they're over in estiem many of the earnings. Right,
Speaker 5: And if I can repeat that process, my trades are
Speaker 5: short horizon. But it's not that I have a short
Speaker 5: horison view of the firm. In fact, if you if
Speaker 5: you're going to do this well, you should have a
Speaker 5: long view of what the firm is likely to do.
Speaker 5: In fact, some of your hippodicies might be, Hey, people
Speaker 5: are thinking that the XYZ product is going to be
Speaker 5: you know, enormously successful over the next five years of
Speaker 5: ten years aka long term view. But if you think
Speaker 5: that that's going to be slightly disappointing this quarter.
Speaker 3: Why hold it Like a company, like a video, everyone
Speaker 3: has a big tenure horizon corret so that's not that
Speaker 3: you're not going to gain an edge just knowing that
Speaker 3: AI is going to be bigger for the next time.
Speaker 5: Correct, the edge is going to be You might want
Speaker 5: to be long on average example and video, but if
Speaker 5: you think that they're going to miss those very high
Speaker 5: expectations and exporter, why are you holding it down? You
Speaker 5: could shuder now and then by again, you know after there.
Speaker 2: So one of the criticisms of multi strats and their
Speaker 2: phenomenal growth has been this idea that we're getting more
Speaker 2: crowding risk in the markets. And you brought up in
Speaker 2: Vidia just then, and to some extent that's kind of
Speaker 2: the perfect example of some of this. It feels like
Speaker 2: whenever in Vidia has a big move, now there's some
Speaker 2: talk about like oh there's a pod behind it. Yeah,
Speaker 2: that's right, or like some sort of factor is changing.
Speaker 2: Talk to us how you actually see the impact of
Speaker 2: the growth of multistrats and factor investing on the market.
Speaker 5: Yes, okay, So I'm going to separate this into two pieces.
Speaker 5: One is it how do you think about it as
Speaker 5: an individual manager? And then what impact that has in
Speaker 5: the because I think it's important to make that distinction, right,
Speaker 5: So on the first one, I think crowding is one
Speaker 5: of those things that you should manage rather than be
Speaker 5: worried about. Right. The analogy that we sometimes use is
Speaker 5: this idea of sitting at a poker table. Right, If
Speaker 5: there's the two of us playing poker, POD's not very big. Right.
Speaker 5: If three more people come in, I'm not worried about,
Speaker 5: Oh my bed's going to be the same I you.
Speaker 5: If I think I'm better than you and the three
Speaker 5: people who've shown up, having more people at the table
Speaker 5: is great, right, Meaning the way in which you make
Speaker 5: money again I'll repeat, which is you have a different
Speaker 5: view from the rest of the market participants and they
Speaker 5: come to agree with you. That looks like crowding. Remember,
Speaker 5: I come into a position before it's crowded, and the
Speaker 5: way I make money is it becomes crowded, and at
Speaker 5: some point I say, Okay, I've gotten paid for my view,
Speaker 5: and I rotate into the next thing, hopefully the next
Speaker 5: thing also early and whatever the idea is, right, And
Speaker 5: so crowding in a sense is the mechanical way in
Speaker 5: which you get paid from being early in an idea.
Speaker 4: Right.
Speaker 5: And so for a manager, an individual portfolio manager, or
Speaker 5: a firm like ours, we want to think about how
Speaker 5: do you manage the crowd So I'll give you an example.
Speaker 5: Let's say two perficle measures. They both have the same quote,
Speaker 5: crowding exposure right, measured in some way that we all
Speaker 5: agree is a good way of measuring. If I got
Speaker 5: there because I was early, and then I got paid
Speaker 5: slowly as people came to be. In my view, that
Speaker 5: is very different from somebody who's chasing the idea. Right,
Speaker 5: They weren't early, They just see it happening and then
Speaker 5: they chase. And is different because if there's a crowding online,
Speaker 5: we both might have some negative returns, but I'd likely
Speaker 5: have less negative returns because some of my ideas are new,
Speaker 5: some part of my portfolio is not ask crowded. And
Speaker 5: two I got paid on the way up, right, and
Speaker 5: so how you get there is super critical right now
Speaker 5: to market question. If there's more participants doing anything, whatever
Speaker 5: it is, the mean return of course comes down. That
Speaker 5: doesn't mean that the people who are at the high
Speaker 5: end of skill are affected by it. In fact, they
Speaker 5: might even make more money if there's enough people on
Speaker 5: the other side of their skill, if that makes sense, right.
Speaker 5: And the last thing that I would say is that
Speaker 5: being a multi strategy fund is a way of organizing yourself, right.
Speaker 5: It's a way of deciding that instead of running a
Speaker 5: traditional integrated, single decision maker kind of fund, I am
Speaker 5: going to think more carefully about how do I outcoupt capital,
Speaker 5: hy do this thing? There's talent, how do I manage
Speaker 5: all of these things that we talk about, the way
Speaker 5: people get paid and all the incentives. It's a way
Speaker 5: of organizing yourself. It's not an investment strategy. You could
Speaker 5: organize itself that way and have lots of different ways
Speaker 5: of investing. And it's the coincidence of the investment strategy
Speaker 5: being the same that drives crowding. It's not the way
Speaker 5: you're organizing yourself. So there's not obvious to me, and
Speaker 5: I'm not sure that the data supports the idea that
Speaker 5: somehow there's more crowding. In fact, the biggest crowding event
Speaker 5: that we've ever had was back in two thousand and seven,
Speaker 5: which is the Great crowding online.
Speaker 1: Right.
Speaker 5: Yeah, Crowding is a thing, no matter where it comes from. Right.
Speaker 5: So if I have a bunch of long only active managers,
Speaker 5: how liken video, that's just as mass crowding as you know,
Speaker 5: some multi strategy liking and video. Does that make sense? Like, yeah,
Speaker 5: different things.
Speaker 2: I think the concern is more that, like the emphasis
Speaker 2: on we talked about the short term horizon of some
Speaker 2: of the stuff, and you talked about the focus on
Speaker 2: the catalyst. I think the concern is that at turning points,
Speaker 2: maybe you introduce more volatility because everyone starts shortly, yeah, exactly, shortleash.
Speaker 3: Everyone knows these very tight stops they want to keep
Speaker 3: their job, and that dad creates a specific type of
Speaker 3: volatility because everyone the speed with which they have to
Speaker 3: cut positions, etc.
Speaker 5: Yeah. I don't disagree, but again, that's something that happens
Speaker 5: at the individual level. Right. So let's say you have
Speaker 5: you know, whatever your stop loss is. Some firms don't
Speaker 5: even have that. They do their risk control differently. That
Speaker 5: is specific to a particular strategy, right, And so whether
Speaker 5: or not that adds volatility depends on whether that strategy
Speaker 5: happens to be correlated with five, ten, fifteen others, right,
Speaker 5: and as not obvious why that should happen just because
Speaker 5: people have this view. Does that make sense? Right?
Speaker 4: Yeah?
Speaker 5: So let's say that there's one hundred people playing for
Speaker 5: the next earnings from I'll make it up. I don't
Speaker 5: know Bank of America, right, Like, they're going to report something,
Speaker 5: and there's a lot of people playing them. Of course,
Speaker 5: if everybody on of these hundred people that I'm describing
Speaker 5: is on one side of it, you may get a
Speaker 5: big ball move depending on what the results are. But
Speaker 5: it's not obvious why they would be all in the
Speaker 5: same side, right, just because they're organized aspopumps? Does that
Speaker 5: make sense? Yeah?
Speaker 4: Yeah.
Speaker 3: Let's talk about comp and making money. You mentioned very
Speaker 3: kindly that in theory you think you could mold me
Speaker 3: and Tracy into decent traders or a lesser PMS maybe
Speaker 3: anless that's fine, Okay, So Tracy and I are there
Speaker 3: and we seem to deliver something that resembles alpha over time.
Speaker 3: What's our paycheck? How is our paycheck derived?
Speaker 5: Yeah, So, typically you want to have an incentive for
Speaker 5: you to focus on the mechanics or your job, right,
Speaker 5: and so typically there's a trade off between making your
Speaker 5: compensation highly discussionary, I just decide because I like you
Speaker 5: or don't like it, whatever, versus exactly formulaic right, fifteen
Speaker 5: percent of your gross returns or whatever it is.
Speaker 4: Right.
Speaker 5: Typically, what you find is that the more you can
Speaker 5: separate the job to be about these forty names in
Speaker 5: the context of you know, some particular boundaries of risk
Speaker 5: and capital deployment and concentration rules, et cetera, it becomes
Speaker 5: easier to give that direct incentive, right. And so what
Speaker 5: you'll find is that most places end up in a
Speaker 5: circumstance where that incentive to be very focused on the
Speaker 5: thing you're good at tends to drive better outcomes. Right now,
Speaker 5: to be clear, there are trade offs on the other
Speaker 5: side business wise, Right, So this is something that allocators
Speaker 5: I suspect need to get better at really digging in.
Speaker 5: So let's say you have thirty six risk takers. Let's
Speaker 5: call them analyst right, and imagine three ways of potentially
Speaker 5: paying them. One way is you net everybody's returns fair first,
Speaker 5: and you know, some of them did well, some of
Speaker 5: them they're poorly, maybe even negative. You get some total
Speaker 5: return at the end across everybody, and then some fraction
Speaker 5: of that is everybody's comp and then you sort of
Speaker 5: paid discretionary. Right. It probably not as good from the
Speaker 5: firm's point of view because it makes it hard to
Speaker 5: have that sort of one to one incentive and really
Speaker 5: focusing on the thing you're good at. But to be clear,
Speaker 5: from the the allocator's point of view, it might be
Speaker 5: the best because you're only paying for the returns that
Speaker 5: were delivered in total.
Speaker 4: Right.
Speaker 5: Now, let's go to the oh, I say, yep. Now
Speaker 5: let's go to the other extreme, which is typical now
Speaker 5: with many platforms, which is eachrisk taker runs a small team,
Speaker 5: each annalyst you know, has like an associate that helps them,
Speaker 5: and each of them you pay, let's say the same
Speaker 5: fifteen percent of whatever the share is. So now you
Speaker 5: have this thing that people in the industry would called
Speaker 5: netting risk, right, which is you pay fifteen percent of
Speaker 5: the people who did well and the people who did poorly,
Speaker 5: it's not that you're getting money back, right, And so
Speaker 5: the total amount of compensation they're paying is larger than
Speaker 5: in the first case.
Speaker 4: Right.
Speaker 5: In fact, in this example, imagine this thirty six people.
Speaker 5: Let's say they each have THEO point seventy five that
Speaker 5: example that I've been using before. If that's what's happening,
Speaker 5: they pay, you pay about twenty five percent more in
Speaker 5: comp costs in this second case as compared to the
Speaker 5: first case. So if you say this is great because
Speaker 5: everybody has a direct incentive of what they're doing, that's
Speaker 5: not free. Right. It costs you literally twenty five percent
Speaker 5: more cost Right. And in a situation where you're passing
Speaker 5: through all this to your investors, your investors are worse
Speaker 5: off by a decent amount.
Speaker 2: Right.
Speaker 5: Now, imagine middle ground where you say, okay, I want
Speaker 5: one to one incentives with the thing you're really focused on,
Speaker 5: and so I'm going to put these thirty six people
Speaker 5: onto teams. Right, So I'm going to make teams of three, right,
Speaker 5: and within that team they net with each other. Right,
Speaker 5: So maybe one of them has a poor year or
Speaker 5: the other two do well, And now you pay the
Speaker 5: team that same share of fifteen percent within the team,
Speaker 5: there's maybe some you know, ability to have some discussion
Speaker 5: art u comp. Right, it's still more expensive than netting everybody,
Speaker 5: but it's only five percent five to six percent more expensive.
Speaker 5: So that version of the world gets you almost all
Speaker 5: of the benefit of that direct focus on your job
Speaker 5: with much less cost. Right, And so if you're an allocator,
Speaker 5: you should be asking this. Remember in this example, these
Speaker 5: are the same thirty six people with the same skill,
Speaker 5: with the same total capital matters, and from the allocator's
Speaker 5: point of view, it makes a huge difference which of
Speaker 5: these you're doing.
Speaker 3: Tracy, I find this to be so fascinating that you
Speaker 3: could basically have the same structure and that the math
Speaker 3: works out so differently just if you sort of change
Speaker 3: the size of the set where you do the netting
Speaker 3: like this is really interesting.
Speaker 2: Way. I have another money related question, but how much
Speaker 2: money would you give us as pms, Not in terms
Speaker 2: of direct comp but how would you decide how much
Speaker 2: we actually have to play around with? And then related
Speaker 2: to that one thing I'm always unclear on with multistrap firms,
Speaker 2: it seems like the size of the available capital pool
Speaker 2: is sometimes a draw for individual pms like oh, I
Speaker 2: get to play with I don't know like fifty million
Speaker 2: or I don't even know what a normal number is
Speaker 2: for them. But on the other hand, you sometimes see
Speaker 2: headlines about how you know, Citadel or Millennium have to
Speaker 2: limit new investor funds. So I'm wondering, like, how do
Speaker 2: you right size the available capital for trading?
Speaker 1: Yeah?
Speaker 5: Okay, so there's I think there's multiple questions in there.
Speaker 5: One is like a capital location, So how do I differentiate?
Speaker 5: Do I give you more than her advice versa?
Speaker 1: Right?
Speaker 5: So that's like whatever the amount I have, there's an
Speaker 5: allocation question, so we can get there in a second.
Speaker 5: And then there's also the is there such a thing
Speaker 5: as like an optimal amount for an individual person?
Speaker 4: Right?
Speaker 5: Let me start with the second one. The answer is
Speaker 5: generally yes, and I think would your previous I think
Speaker 5: it was Kapi who made this point that there's a
Speaker 5: human and sort of psychology aspect of how much money
Speaker 5: you can comfortably run, right, and so typically past a
Speaker 5: certain amount, Literally, the psychology of seeing however much you're
Speaker 5: making or losing every day gets really large and uncomfortable
Speaker 5: for a lot of people.
Speaker 2: Right, I get anxious just looking at my four oh
Speaker 2: one case.
Speaker 5: Yes, exactly that, and that to be clear, that's a thing.
Speaker 5: Right you Let's say you start somebody running, I'll make
Speaker 5: it up one hundred million dollars of just dollars, right,
Speaker 5: and they're you know, they're fifty of them are long,
Speaker 5: fifty short, and maybe every day they go out by
Speaker 5: you know, half a million. You know they're be done
Speaker 5: by half a million. Right, that's sort of the range.
Speaker 5: Now you make that ten x int space it might
Speaker 5: be literally identical, but the psychology off you walk into
Speaker 5: the morning and the market's open. Now you're down five
Speaker 5: million dollars. There comes a point where people where that's
Speaker 5: a thing.
Speaker 3: Right, Like when I like play poker, I wonder if,
Speaker 3: like it would be nice if they would just lie
Speaker 3: to me and say you're playing a one to two game,
Speaker 3: you're buying for two hundred, and then at the end
Speaker 3: they're like, oh, it turns out you're playing for two
Speaker 3: thousand because the chips are the safe.
Speaker 4: Yeah.
Speaker 5: And the psychology, the way the psychology plays is not
Speaker 5: just on the amount of money you can comfortably run
Speaker 5: and remember the bigger the amounts you have to worry
Speaker 5: about things other than your say, fundamental views. You have
Speaker 5: to worry more about tea costs and implementation questions and
Speaker 5: liquidity questions, and you know, how can do you get
Speaker 5: to play on smaller cap names where you maybe feel
Speaker 5: you have an edge, but now you can't really do
Speaker 5: as much of it. So there's all these sort of
Speaker 5: things that have to do with scale. The other thing
Speaker 5: that happens is a psychology and compensation.
Speaker 4: Right.
Speaker 5: It is not uncommon for folks to prefer I could
Speaker 5: give you a billion dollars and pay you fifteen percent
Speaker 5: of say your night returns, or maybe half a billion
Speaker 5: dollars and pay your thirty percent. Right, it's the economics
Speaker 5: are the same. Many people might prefer the latter rather
Speaker 5: than the former, right, So psychology does play a significant
Speaker 5: role in this. We tend to find that good perform
Speaker 5: managers can actually run, assuming they have a good team
Speaker 5: with them, in the billions of dollars, But it's not
Speaker 5: necessarily the most common situation. Most platforms find themselves running
Speaker 5: smaller teams with lots of littlecations. We then have all
Speaker 5: these netting issues, right, so you do want to think
Speaker 5: about that. The second question is, okay, how are big
Speaker 5: eat port IFOI? You might get how do you separate? Like,
Speaker 5: how do I give you more than other person?
Speaker 4: Right?
Speaker 5: The reality is you want to make your capital location
Speaker 5: based on your expectation of return.
Speaker 4: Right.
Speaker 5: Will you have good sharp ratio in the future. Right?
Speaker 5: The problem is you don't know the true sharp ratio.
Speaker 5: Most people are tempted to use some realize sharp rasio.
Speaker 5: What was your sharp ratio last year?
Speaker 1: Right?
Speaker 5: And the problem is there's a huge amount of noise
Speaker 5: in that.
Speaker 4: Right.
Speaker 5: And I find the intuition of this really interesting. So
Speaker 5: if you have a good basic way of thinking about it,
Speaker 5: let's say you cover forty names in your views. About
Speaker 5: these names, let's say I like this, I don't like this.
Speaker 5: Every day are correlated with actual returns by what one percent?
Speaker 5: So not a lot of predictability, like nine to nine
Speaker 5: percent of what's happening you don't know, but you have
Speaker 5: one percent predictability. If you do this and trade based
Speaker 5: on these views, you will have a sharp person of
Speaker 5: about one at the end of the year, which is
Speaker 5: pretty good for forty names, right, meaning little amount of productility.
Speaker 5: One percent in this case is what people call the ic.
Speaker 5: The correlation between your views and next day of returns.
Speaker 5: Get to a pretty good outcome at the end of
Speaker 5: the year. It also tells you that there's a huge
Speaker 5: amount of noise. Right, So if you think about let's
Speaker 5: say that we all three of us agree that you know,
Speaker 5: we have a crystable, and we know for a fact
Speaker 5: that there's a person that has one percent correlation between
Speaker 5: views and returns, and we observe a year worth of returns,
Speaker 5: and we observe that for one hundred years, the average
Speaker 5: sharp will be one, but some years will be low
Speaker 5: because you know of the ninety percent, you're not predicting.
Speaker 5: You might be unlucky some year and you end up
Speaker 5: with a SHARPO of zero. Some years you get really lucky,
Speaker 5: you end up with a sharp of two. So realize turns.
Speaker 5: Realize sharps have a huge amount of variation. So you
Speaker 5: don't know what the true sharp is. You only observe
Speaker 5: the real life sharp, and so if you make out
Speaker 5: locations based on the real life sharp, you're mostly allocating
Speaker 5: on noise, especially if you only do it over a
Speaker 5: short period of time. Right, And so the way you
Speaker 5: want to start is to say, look, I'm going to
Speaker 5: ignore the past returns and do equal risk. That's essentially
Speaker 5: the same as saying, I am going to assume that
Speaker 5: the two of you have the same I see the
Speaker 5: same sharp I don't because I don't know what it is. Right,
Speaker 5: It's sort of Abasian statistics kind of thing. Right, And
Speaker 5: then I deviate away from that benchmark of equal risk
Speaker 5: as I to learn not so much more about your returns,
Speaker 5: but what drives returns, so overy time I might be
Speaker 5: able to observe that. Actually, as it turns out, one
Speaker 5: of you is really good at the margin parts of
Speaker 5: thinking about earnings, right, and for kind where names where
Speaker 5: there's a lot of room to think about differences and
Speaker 5: views about margin, and you happen to do really well right,
Speaker 5: whereas somebody else might have high expertise on product questions, Right,
Speaker 5: will a product fly or not fly in a particular space?
Speaker 1: Right?
Speaker 5: And I collect data about the stuff. So let me
Speaker 5: give you an example. Let's say you tell me the
Speaker 5: reason I generate one percent correlation between my views and
Speaker 5: returns is because I'm good at predicting surprises, right, earning surprises, Okay,
Speaker 5: and you tell me that you can predict surprises at
Speaker 5: ten percent correlation. So every time you have a prediction
Speaker 5: for forty names, they are correlated ten percent with actual surprises.
Speaker 5: So this is not much better because if I collect
Speaker 5: data about your predictions of earnings, not returns, I can
Speaker 5: distinguish ten percent from zero much better than one percent
Speaker 5: from zero.
Speaker 4: Right.
Speaker 5: The second thing that is true is that I knew
Speaker 5: that returns are correlated with earning surprises by about ten percent,
Speaker 5: And to be clear that I can do with lots
Speaker 5: of data. I can go back in time and think
Speaker 5: about the correlation of returns and earning surprises for every style,
Speaker 5: going back in time for fifty years. Right, And these
Speaker 5: are transitive. So if you predict earnings by ten percent
Speaker 5: and returns are correlated with earning surprises by ten percent,
Speaker 5: you get the one percent that you're looking for. But
Speaker 5: I can look at your earnings and do much better
Speaker 5: analysis because those are ten percent correlated with actual earnings
Speaker 5: doesn't make sense. So as I get time, I can
Speaker 5: get to understand your investment the underlying things that drive
Speaker 5: those returns much better.
Speaker 3: This seems like a very big theme throughout this conversation
Speaker 3: that the more you can understand why things work correct,
Speaker 3: the better you are, the easier many other decisions become.
Speaker 3: And I have one last question for you say, we
Speaker 3: have some students. College students listen to odd lots from
Speaker 3: time to time. I'm a freshman in college. I'm interested
Speaker 3: in finance. It sounds like a fun career. I want
Speaker 3: to make a lot of money working for a multi
Speaker 3: strategy hedge fund one day. What's the best decision I
Speaker 3: could make right now as a freshman or sophomore in college.
Speaker 3: They would most likely open a future door for me
Speaker 3: for something of this career.
Speaker 5: Yeah, that's a that's a good question. You know, we
Speaker 5: run an internship program, so you get asked this thing
Speaker 5: all the time. I would say two things. Number one
Speaker 5: is you I think need to have a good mix
Speaker 5: of liking and being reasonably good at the I'm going
Speaker 5: to call it the data part of it. Right. These
Speaker 5: ares are all about do I understand the data that
Speaker 5: tells me something about this firms?
Speaker 4: Right?
Speaker 5: And so you know, whether it's you know, I cover
Speaker 5: consumer firms and I'm looking at kurk card data and
Speaker 5: you know, thinking about, you know, what is the color
Speaker 5: of the fall and how I might get you know,
Speaker 5: data about whose color is going to be the important one?
Speaker 5: And what story of am I running? And all these
Speaker 5: sorts of things. So there's a lot of data analysis
Speaker 5: that they have to do, and you have to be
Speaker 5: sort of both good at it and really like it
Speaker 5: because it becomes sort of your day to day.
Speaker 4: Right.
Speaker 5: The second thing is you have to be willing to
Speaker 5: understand that there's sort of a grind aspect of the job.
Speaker 1: Right.
Speaker 5: It sounds really exciting to think about predicting things and
Speaker 5: potentially making a lot of money, but the reality is
Speaker 5: that the data to day job can be a bit
Speaker 5: of a grind. Right. You're covering these forty names, and
Speaker 5: they are the same forty names every year, right, and
Speaker 5: you're listening to every conference call and listening to every
Speaker 5: airnings announcement, and you're looking for like tiny little bits
Speaker 5: of differences. It's like, well, you know, last Timmer around
Speaker 5: they describe the nature of the particular product that they're
Speaker 5: working on in this way. Now describing is slightly differently.
Speaker 5: I wonder if that means something about their strategy, and
Speaker 5: so is this sort of to partner uses the word
Speaker 5: of coal mining, right, it can be. It could be
Speaker 5: a bit of a grind right.
Speaker 2: Now in the minds of multi stress exactly right.
Speaker 5: It's not all the excitement of I show up in
Speaker 5: the morning and have an idea and now I make
Speaker 5: coup exactly.
Speaker 2: Yes, Wait, speaking of the grind and interns, is there
Speaker 2: a future where I know you spoke earlier about the
Speaker 2: importance of the human factor in a lot of this,
Speaker 2: but could you switch the emphasis to more AI.
Speaker 3: This other thing that I wasn't gonna get it?
Speaker 5: Yeah, because I'm thinking, I'm happy to talk about AI.
Speaker 2: Stock Want Funds were like the original users of machine learning,
Speaker 2: or one of the big original users, so it seems
Speaker 2: fairly natural for them to use more AI in order
Speaker 2: to spot potential patterns or potential catalysts for big moves.
Speaker 3: Tell us what's real and what's bs.
Speaker 5: There's always a mix. But I do want to say
Speaker 5: something before we get to a specifically, this sort of
Speaker 5: job is always a bit of an arms race, right,
Speaker 5: meaning this sort of thing that made you money, Let's
Speaker 5: say twenty years ago. Twenty years ago, you could have
Speaker 5: been an analyst that figured out that in order to
Speaker 5: understand particular, say retail firms, you could go look at
Speaker 5: footnotes about whether you know you owned or at least
Speaker 5: your retail space where you sold your T shirts or
Speaker 5: whatever it was, and that might have had some consequence, right,
Speaker 5: depending on how your finance and what that meant for
Speaker 5: you know, Etcaday early data stuff, Right, you don't do
Speaker 5: that now. And the reason you don't do that now
Speaker 5: is because that's all in the database that everybody can
Speaker 5: go mechanically look at it, right. And so there's this
Speaker 5: sort of sub get you need to become ever more
Speaker 5: sophisticated data and analytics wise, and AI is sort of
Speaker 5: one more step in that direction, right, So I don't
Speaker 5: think of it as something inherently different from this sort
Speaker 5: of constant evolution of always being more sophisticated and understanding
Speaker 5: the firms.
Speaker 4: Right.
Speaker 5: The one thing that I would say about AI is that,
Speaker 5: at least up until this point, if you think about
Speaker 5: how AI is trained, right, you feeded all this text
Speaker 5: essentially mostly from their Internet. And the job that it's
Speaker 5: trying to do is that it's trying to predict the
Speaker 5: most likely answer to a question, or the most likely
Speaker 5: thing that comes after some prompt. Right. That's essentially what
Speaker 5: you're doing. And what that means, by definition is that
Speaker 5: if you ask it, hey, what is different about company X?
Speaker 5: By definition, it's going to tell you what everybody else
Speaker 5: thinks is different about companyes, which means it's actually not
Speaker 5: the different thing. Aka, you're getting the consensus right, and
Speaker 5: so that could be quite useful in the way you
Speaker 5: think about doing data analysis as lots of ways. And
Speaker 5: we have a bunch of investment in AI work within
Speaker 5: the firm, but that is not the same as assuming
Speaker 5: that AI will have inside about the firm, because it's
Speaker 5: been trained on the average of things kind of by definition, right,
Speaker 5: And so the step of going from it helps me
Speaker 5: summarize or potentially, you know, kind of clarify what themes
Speaker 5: are people talking about. And there's lots of things that
Speaker 5: you might be able to do with it that is
Speaker 5: not quite the same as the jump to and therefore
Speaker 5: here's a difference in view versus everybody else's views. Does
Speaker 5: that make sense? Yeah?
Speaker 2: Absolutely, Dan, Thank you so much for coming on all thoughts.
Speaker 2: That was great, amazing You explained the maths perfectly.
Speaker 3: So Dan, Matt, Yeah, No, it was really great than like,
Speaker 3: I feel like a million questions we answered your very
Speaker 3: game to really work us through, work work through all
Speaker 3: of them with us. So appreciate you coming on.
Speaker 5: Thank you, Joe.
Speaker 2: That was fun.
Speaker 3: It was so fun.
Speaker 2: I like talking about maths and multi strap funds, DAN maths, Yeah,
Speaker 2: the DAN mats. So there are a few things to
Speaker 2: pick out of there. I really liked the emphasis, and
Speaker 2: this has come up before, but the idea that crowding
Speaker 2: in is not necessarily a bad thing for individual managers
Speaker 2: because what you're trying to do is identify that catalyst. Yeah,
Speaker 2: that will get everyone crowded.
Speaker 3: Crowding, crowding in how you get paid, Yeah, like you
Speaker 3: eventually you just want to be there before the crowding,
Speaker 3: But the crowding is ultimately what delivers the paycheck.
Speaker 2: Right now, does that maybe have a less desirable effect
Speaker 2: on the overall market? I mean I kind of take
Speaker 2: the point about, well, if you have a bunch of
Speaker 2: long only funds that are in something and then something
Speaker 2: bad happens, they'll all retreat. That that's like the same
Speaker 2: effect as multi strats crowding in. But it does feel
Speaker 2: to me, just observing the market in recent years, that
Speaker 2: you are getting these sort of shorter and sharper turning
Speaker 2: points or reactions.
Speaker 3: Totally, there are so many things that I took from
Speaker 3: that conversation. I thought that was fantastic, and all of
Speaker 3: our conversations about this topic have been good, but to
Speaker 3: talk to an actual founder of a fund though it
Speaker 3: was great. You know, there was the big conceptual thing
Speaker 3: that he kept coming back to, which is that the
Speaker 3: more you can know why something works, the better. I
Speaker 3: think I'm pretty good at my job of co hosting outlas.
Speaker 3: I think you are too.
Speaker 1: But I host.
Speaker 3: Yeah, but I do think and they're like, you know,
Speaker 3: I know other people are good at their jobs. But
Speaker 3: to be able to articulate why you are good at
Speaker 3: your jobs, and provably be able to articulate why you're
Speaker 3: good at your jobs, would you.
Speaker 2: Go why you didn't just get lucky?
Speaker 3: Yeah, why it's not lucky? Why you are able to
Speaker 3: identify something like, oh, I am very good at identifying
Speaker 3: earning surprises. Setting aside the question of am I good
Speaker 3: at picking stocks? That's a really interesting way to think
Speaker 3: about it, Like, Okay, we know that earning surprises are
Speaker 3: correlated to stock performance. If I could prove that I'm
Speaker 3: good at X, then I could probably prove that I'm
Speaker 3: good at stock selection. That is really interesting. I love
Speaker 3: like hearing about the math of like why you want
Speaker 3: to avoid correlation between managers and how powerful that effect
Speaker 3: is and how few pods you need to get optimal.
Speaker 3: So much good stuff. The part about compensation, yeah, super interesting.
Speaker 2: Well, I do think in general a good piece of
Speaker 2: life advice is identify your comparative advantage early on, right,
Speaker 2: and play up to it, Like figure out what you
Speaker 2: do well and why you do it well. That's a
Speaker 2: real good thing to do early in your career.
Speaker 4: All right.
Speaker 3: No, I figured out early in my career that my
Speaker 3: one competitive advantage in journalism was waking up at four
Speaker 3: am before everyone. And now I'm spending thousands of dollars
Speaker 3: a year on therapy to like allow myself to sleep
Speaker 3: in a little bit more. So there are some drawbacks
Speaker 3: depending on what thing you identify.
Speaker 2: All right, everyone stop asking Joe or stop telling Joe
Speaker 2: what he missed, because it's just compounding. That's this problem.
Speaker 4: All right.
Speaker 2: Shall we leave it there.
Speaker 3: Let's leave it there.
Speaker 2: This has been another episode of the au Thoughts podcast.
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 3: And I'm Joe Wisenthal. You can follow me at the Stalwart.
Speaker 3: Follow our producers Carmen Rodriguez at Carman Ermann Dashill, Bennett
Speaker 3: at Dashbot at kel Brooks at Kelbrooks. Thank you to
Speaker 3: our producer Moses ONMDAM. For more Oddlots content, go to
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