Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.
Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy, I still want to learn more about how multi
Speaker 2: strategy hedge funds work.
Speaker 3: I thought you were going to say, I still don't
Speaker 3: know anything about multi strategy. I feel like we're slowly
Speaker 3: getting there, and hopefully our listeners don't mind coming along
Speaker 3: with us for the ride. I feel like every time
Speaker 3: we have an episode on multi strategy hedge funds, or
Speaker 3: on the pod shops as they are sometimes called, we
Speaker 3: are deepening our understanding and we're sort of getting into
Speaker 3: more and more detail. And I feel confident that one day,
Speaker 3: after we've done like fifty episodes on this topic, we
Speaker 3: will get there.
Speaker 2: I do think it would take about fifty I think
Speaker 2: that's like an accurate number of what it would actually
Speaker 2: take to get there. But of course, most recently we
Speaker 2: had that episode with Giuseppe Pallioligo Gappy talking about some
Speaker 2: of the big ideas and sort of from a high
Speaker 2: level of how some of these funds actually work. They're
Speaker 2: very popular. They've done some of the big ones that
Speaker 2: people know, like the millenniums, like the Citadels have just
Speaker 2: had incredible runs. Really seems to be displacing a lot
Speaker 2: of the old style quant disrupting the sort of fund
Speaker 2: of funds idea that was popular. I have some sense,
Speaker 2: you know, you have all these managers and you give
Speaker 2: them very specific mandates and they have to really focus
Speaker 2: on that, and then if they're not too correlated with
Speaker 2: each other, you can get above market returns in theory
Speaker 2: and apparently in practice, but like how that actually works,
Speaker 2: I still really don't know.
Speaker 3: Well, Okay, so two things. Number One, everyone should definitely
Speaker 3: go and check out Gappy's book if you haven't already,
Speaker 3: Advanced Portfolio Management. A lot of the references that I'm
Speaker 3: about to throw out on this episode, anything that I
Speaker 3: say that might sound even remotely impressive or like I
Speaker 3: know what I'm talking about, has come up Gappy's book.
Speaker 3: And also I will say I've read that book going
Speaker 3: to and from work on the subway. It's pretty short,
Speaker 3: so I think I did it in like a week.
Speaker 3: And I have never gotten so many people like talking
Speaker 3: to me on the subway when they saw me pull
Speaker 3: out Advance Portfolio Management and they're like, what is.
Speaker 2: That that is very New York.
Speaker 3: Yes. And then secondly, the other thing I will say
Speaker 3: is we've been talking about multi strategy hedge funds. We
Speaker 3: want to learn more about them because they're this new
Speaker 3: thing on Wall Street that everyone seems very excited and
Speaker 3: interested in. But beyond that, there are recent events that
Speaker 3: make this an even more pressing topic. So we've seen
Speaker 3: some of the big winners in the market in recent
Speaker 3: months start to come down, So the big tech names
Speaker 3: things like Nvidia, we've seen small caps shoot up. A
Speaker 3: lot of people are talking about whether or not this
Speaker 3: is a factor rotation, and we'll get into what factors
Speaker 3: actually are. But I think the discussion that we're seeing
Speaker 3: right now, and I should caveat this with it is
Speaker 3: July eighteenth. So we've seen those big moves in the
Speaker 3: market very recently. The discussion that's happening now is how
Speaker 3: much does the I guess growth in factor investing feed
Speaker 3: into some of these moves, and also how does the
Speaker 3: risk models that go alongside this actually impact investor behavior
Speaker 3: and then also feed into these market moves. So is
Speaker 3: it the case that everyone's getting out of big tech
Speaker 3: because their risk models are telling them.
Speaker 2: Too totally and this is like a really important element
Speaker 2: for sort of understanding both how these investment vehicles work
Speaker 2: and the impact that they have on the market. Which
Speaker 2: is one of the things we know is that the
Speaker 2: various portfolio managers within these funds have very tight remits.
Speaker 2: It's like, your team is responsible for trading chip stocks,
Speaker 2: and your team is responsible for trading the short end
Speaker 2: of the Brazilian yield curve, and your team is responsible
Speaker 2: for international oil plays. And then we know that like
Speaker 2: and then you're not allowed to take any sector beta,
Speaker 2: and you're not allowed to take any market beta and
Speaker 2: all these things, and so you see fact you're neutral
Speaker 2: factor neutral, and then you know tight risk limits. So
Speaker 2: if something starts to go down, you don't want to
Speaker 2: lose your job, and you like get out of positions,
Speaker 2: and that can create interesting moves for the market. Anyway,
Speaker 2: suffice to say, there is much more to learn.
Speaker 3: Yes, well, the other thing, just one more thing, Yeah,
Speaker 3: the other other thing. The other other thing that I
Speaker 3: think is kind of funny now is remember whenever you
Speaker 3: had weird market moves. Yeah, like I guess it would
Speaker 3: have been fifteen years ago or something like that, it
Speaker 3: was always quant funds like the quant quake before two
Speaker 3: thousand and eight, and then it became CTA's and then
Speaker 3: it was risk parody, and now it's very much the
Speaker 3: pod shops that people point to when we start to
Speaker 3: see sketchiness in the market. So I think we should
Speaker 3: talk about, you know, what are the technicalities that are
Speaker 3: driving that pod shop behavior.
Speaker 2: Every time there's some big move in the market, someone
Speaker 2: tweets like, I hear a.
Speaker 4: Pod is blowing up. Yeah, Oh I hear some pods.
Speaker 2: Are blowing up. That's like, that's how to sound like
Speaker 2: an in guy on a finance twit.
Speaker 3: Little do they know the pod that's blowing up is off?
Speaker 2: If you don't a good one. If you don't know
Speaker 2: the pod that's blowing up, you're Anyway, we have the
Speaker 2: perfect guest. I'm very excited. We are going to be
Speaker 2: speaking with rich falk Wallace, who was previously a portfolio manager,
Speaker 2: who's at Citadel, who's at Viking, and now he is
Speaker 2: the CEO and co founder of Arcana, which builds models
Speaker 2: and software to help investors and hedge funds, et cetera
Speaker 2: actually track all of this stuff and actually track what
Speaker 2: kind of risks managers are taking and how they're actually
Speaker 2: performing relative to their benchmark or expectations. So we're gonna
Speaker 2: maybe understand a bit more of the technical aspects of
Speaker 2: all this stuff. So Rich, thank you so much for
Speaker 2: coming on.
Speaker 4: As thanks so much for having me. I appreciate it.
Speaker 2: Why don't we start with your best and obviously we're
Speaker 2: going to talk about your software company or Kenna and
Speaker 2: all that. But you were previously at a couple of
Speaker 2: these big funds. What did you do?
Speaker 4: Yeah, that's right. So I started my career on the
Speaker 4: buy side, started originally in investment banking out of college
Speaker 4: JP Morgan, and then worked at silver Point, which is
Speaker 4: like a large credit distressed hedge fund, very value oriented,
Speaker 4: none of that sort of risk model framework that gets
Speaker 4: deployed at the pods. And then after that was at
Speaker 4: Viking Global, which is I always describe the Tiger Cubs
Speaker 4: in some ways as like a hybrid between the sort
Speaker 4: of equity long short value orientation sort of philosophically and
Speaker 4: the multi manager systems. And then finally, most recently, was
Speaker 4: a portfolio manager at Citadel managed a global materials, natural
Speaker 4: resources and materials portfolio.
Speaker 3: I love this because when I think about silver Point,
Speaker 3: I think more sort of traditional value investing, and then
Speaker 3: you wind up doing metals at Citadel, which is a
Speaker 3: hedge fund that's known for being very quantitatively driven to
Speaker 3: better understand the pods. Now, talk to us about the
Speaker 3: differences between what you were doing at silver Point versus Citadel.
Speaker 4: Totally. Yeah, it's a great question. So the way that
Speaker 4: any value, super deep value oriented kind of fund works
Speaker 4: like a silver Point is that in the end, you
Speaker 4: do a ton of very deep research on the company,
Speaker 4: So you focus on what are the underlying fundamentals, what's
Speaker 4: the contract structure out many years in the future, what
Speaker 4: do the earnings look like, of course in the short term,
Speaker 4: but also in the long term, what's structurally happening competitively?
Speaker 4: You kind of go way down the rabbit hole. There's
Speaker 4: a lot more and we can go into that. And
Speaker 4: then as you kind of migrate sort of down the
Speaker 4: time horizon spectrum, at least from what a thesis looks
Speaker 4: like on a single stock, what you're kind of doing
Speaker 4: is thinking about where are the catalysts that change the
Speaker 4: market's perception of that long term. So, like I remember
Speaker 4: when I joined Viking, I remember asking the question just generally,
Speaker 4: like how much do you care about earnings. I think
Speaker 4: for anybody who's very value oriented, you kind of are
Speaker 4: concerned and about like, am I just going to be
Speaker 4: focused on the next data point, the next earnings and
Speaker 4: not sort of able to you know, see the forest
Speaker 4: for the trees and sort of care about, you know,
Speaker 4: what does this data mean for the long term? But
Speaker 4: the answer I got back in general, not specifically there,
Speaker 4: but is in that kind of framework it's or the
Speaker 4: way the question was answered to me was, Hey, the
Speaker 4: long term is a function a DCF is a function
Speaker 4: of years. Years are a function of quarters, and so
Speaker 4: therefore we care about the quarters. But what that tells
Speaker 4: you is that, like the answer is what about the
Speaker 4: short term, catalyst changes the perspective about the long term
Speaker 4: valuation of the company. And so I think what people
Speaker 4: sometimes looking from a far don't appreciate is the extent
Speaker 4: to which there's actually a little bit more of a
Speaker 4: convergence across styles from the underlying analyst workflow that like
Speaker 4: even a very long term investor to some extent is saying,
Speaker 4: even if I'm betting on the long term, the interim
Speaker 4: proof points illustrate the view of that long term and
Speaker 4: the short term guy says, well, I may get the
Speaker 4: number right in the short term, but that only is
Speaker 4: meaning to the change in the markets price if it
Speaker 4: tells you something about that long term. And so there's
Speaker 4: a little bit of like a yeah, and I think
Speaker 4: that convergence is happening more and more where people are
Speaker 4: kind of pushing towards that center actually, where everybody both
Speaker 4: cares about the short term data point and is looking
Speaker 4: to what that means about the long term. So but anyway,
Speaker 4: at the beginning of that process, at the silver point
Speaker 4: or any deep value type place, you're just really focused
Speaker 4: on that longer term story. You're less focused on the
Speaker 4: quarter or the catalyst than trying to understand sometimes things
Speaker 4: that And I was a junior analyst when I kind
Speaker 4: of started there. There was a first job out of banking,
Speaker 4: and you know, but you can be looking at like
Speaker 4: what does the rail contract look like in twenty twenty
Speaker 4: four and how does that step up? And you're like, man,
Speaker 4: does this matter to the stock. It's great training. It's
Speaker 4: a perfect place to kind of get that.
Speaker 3: You know.
Speaker 4: It's almost like private equity like where you're just sort
Speaker 4: of you know, looking through everything. But that's kind of
Speaker 4: how that started, all right.
Speaker 2: So then at Citadel you mentioned you covered materials, commodities,
Speaker 2: stuff like that. I guess two questions. When you come
Speaker 2: in the door there and you're told like, okay, this
Speaker 2: is what you do. What are you're told as your
Speaker 2: constraints and your specific remit and then also like how
Speaker 2: do you pick a stock?
Speaker 4: Yeah? Yeah, And I'll talk about this in a general sense,
Speaker 4: not specific set it up, but to talk about multimanagers
Speaker 4: in general, and our client base today at Arkana is
Speaker 4: about fifty to fifty split. I would say between people
Speaker 4: who I call like natives who come from the risk
Speaker 4: model system either any of the major pods or related
Speaker 4: and the other half can be like a deep value
Speaker 4: fund that says, hey, I don't want to limit myself
Speaker 4: to this stuff, but I see just like you guys
Speaker 4: are saying, this is an increasingly important part of markets
Speaker 4: and I want to be deep on it, educated whatever.
Speaker 4: So to answer your question on how multimanagers kind of pickstocks,
Speaker 4: run processes and think, not specific to any one place,
Speaker 4: but that sort of natives group in general. So at
Speaker 4: any of these places, the core contract is to say,
Speaker 4: the core difference, I guess is the other way to
Speaker 4: say it is versus a deep value place, it's about turnover.
Speaker 4: In the end, it's two things. It's risk limits and
Speaker 4: it's about how freak your book turns over. So at
Speaker 4: a deep value fund, the goal might be in sort
Speaker 4: of theory to have more than a year long average
Speaker 4: hold period. In practice it'll be often shorter than that,
Speaker 4: you know, nine months or whatever as bad idea cycloud
Speaker 4: or whatever. But at a multi manager those numbers can
Speaker 4: be anywhere from like ten to fifteen to even higher,
Speaker 4: meaning the entire book turns over ten to fifteen times
Speaker 4: in a year.
Speaker 3: Wow.
Speaker 4: Think about it simply like the average idea stays on
Speaker 4: for a month is the way to put it in
Speaker 4: the book. And so as you step into any of
Speaker 4: these places to your point A, you have a you know,
Speaker 4: there's a structure. There's an analyst, and then a portfolio manager.
Speaker 4: And the analyst generally has a single industry focused so
Speaker 4: it's like, hey, i am, as you said, chip stocks,
Speaker 4: or you know, somebody else might have software, or it's
Speaker 4: a sort of defined single universe. And then a portfolio
Speaker 4: manager will have a set of analysts below them who
Speaker 4: have typically very related coverage universes and will feed up
Speaker 4: into the portfolio manager. So that's like kind of the structure.
Speaker 4: Stockpicking kind of ends up being what we were talking
Speaker 4: about earlier. In the end, it's the analyst job to
Speaker 4: have a detailed model, of course, to have a view
Speaker 4: on earnings across their coverage universe, and that coverage universe
Speaker 4: for that analyst, by the way, can be and it
Speaker 4: varies by a multi manager, but it can be anywhere
Speaker 4: from like thirty names at the low end to like
Speaker 4: eighty names at the high end buy analyst. So there's
Speaker 4: a lot of process there.
Speaker 3: There are many differences between a retail investor and a
Speaker 3: multi strategy fund, but one of the key ones I
Speaker 3: think is maybe position sizing. So if you're a retail
Speaker 3: investor and you have a single stock thesis, I don't know,
Speaker 3: you want to buy in video or something, you buy
Speaker 3: in video, and you're probably making that decision based on
Speaker 3: how much cash you have in your Robinhood account or
Speaker 3: something like that. But if you're at a multi fund,
Speaker 3: it seems like a much more sophisticated process. So I
Speaker 3: guess I'm curious if you're at a podshop, how do
Speaker 3: you know how much to buy? How do you know
Speaker 3: how much to allocate to a single stock. And I
Speaker 3: guess another way of saying it is you're looking at
Speaker 3: that single stock on a risk adjusted basis, right, Like,
Speaker 3: that's what you want to get, right, the risk adjusted performance,
Speaker 3: not just the single stock performance.
Speaker 4: That's right. That's right. So there are two or three
Speaker 4: ways that gets implemented. So the first is constraints. So
Speaker 4: step one is dollar neutrality. I'm long as many dollars
Speaker 4: as i'm short. That's a simple limit, sort of. One
Speaker 4: level higher is beta neutrality relative to the overall market?
Speaker 4: Am I longer short? On a beta adjusted basis? Sort of?
Speaker 4: The third level is factor neutrality. I'm balanced against all
Speaker 4: of these sort of if you maybe simplified just slightly
Speaker 4: the subcomponents of beta, so instead of like, hey, I
Speaker 4: have a beta to the market, I actually have a
Speaker 4: beta to the basket of size large companies. I have
Speaker 4: a beta to the basket of companies with momentum. I
Speaker 4: have a basket to the beta.
Speaker 2: I see.
Speaker 3: So you decompose beta.
Speaker 4: Essentially, it's a decomposition. Essentially, when people talk about factors
Speaker 4: and factor neutrality, it's a decomposition of beta into its
Speaker 4: constituent parts. There's a lot of statistics that goes under
Speaker 4: the hood to make that orthogonal and precise and.
Speaker 3: Orthogonal.
Speaker 4: But at the sort of functional level, at the level
Speaker 4: that people at the stock picking level, at multi managers
Speaker 4: interact with the model, it's essentially just a decomposition of
Speaker 4: beta's and then you add up those exposures on each side,
Speaker 4: and you are limited essentially by the percent of your
Speaker 4: bets in a book in aggregate that are betting basically
Speaker 4: on factor type bets as compared to the percentage of
Speaker 4: your bets that are betting on the remainder term, the
Speaker 4: non factor component of any stock. So as you look
Speaker 4: at any stock, it fits within that broader portfolio that
Speaker 4: you're putting together.
Speaker 3: Okay, and then the second thing that you talked about
Speaker 3: earlier is this idea of turnover. So just to press
Speaker 3: on this point, how much do trading costs factor into
Speaker 3: investment decisions? And also position sizing, because as you just stated,
Speaker 3: you could theoretically size or arrange all of your positions
Speaker 3: to be factor neutral or neutral in terms of systematic risk.
Speaker 3: I guess, but I imagine in order to do that, you
Speaker 3: would have to be trading pretty much like constantly right,
Speaker 3: which would add to your execution costs. So does that
Speaker 3: come into play as well?
Speaker 4: Yeah, it does. In practice, the stock picker, portfolio manager
Speaker 4: and analyst doesn't flow in a complicated set of formulas
Speaker 4: to their decision around sort of how do I optimize
Speaker 4: trading costs? The engines operating at the multi managers do
Speaker 4: think a ton about how do I take the stock
Speaker 4: picks that a single portfolio does and then execute them
Speaker 4: in a optimal way a crossing some firms doing some
Speaker 4: prints don't cross each other's orders within the pod level
Speaker 4: about all of that, and then so the first level
Speaker 4: of how do people get limited is the constraints on
Speaker 4: what percent of my bets are in factor type bets
Speaker 4: versus non factor type bets. There are all also a
Speaker 4: bunch of like single position limits, So that's like one
Speaker 4: version is basically limiting the portfolio manager to have to
Speaker 4: sort of live pickstocks under this constraint. The other framework
Speaker 4: of how do you size positions to your sort of
Speaker 4: earlier question which comes around to this trading cost question
Speaker 4: is there are tools that are called optimizers that basically
Speaker 4: look at the expected return that each portfolio manager thinks
Speaker 4: they have in their book of stocks and tries to
Speaker 4: solve for the optimal balance of the expected return against
Speaker 4: the volatility of those stocks and the volatility the factor
Speaker 4: bets in the book, and it'll spit out an answer
Speaker 4: for you. That answer may not be exactly where you
Speaker 4: want to land, but in the most sophisticated places, that
Speaker 4: answer that the optimizer spitting out is including how much
Speaker 4: trading cost impacts the book. So it's sort of flowing
Speaker 4: that mathematically into a machine driven optimal book. But again
Speaker 4: that's sort of in the more science bucket. Of course
Speaker 4: there's art even underneath that statistics, but basically that's in
Speaker 4: the more sort of science bucket. Then the PORTFOLI manaddressed
Speaker 4: to say, Okay, the machine sort of took my expected returns,
Speaker 4: took the variance of those pieces and the trading costs
Speaker 4: into account. I gave me an answer. Does that actually
Speaker 4: still fit with my you know, fundamental bottoms at work?
Speaker 4: Back to hey, the contract of this company changes in
Speaker 4: twenty twy six, the earnings going to be this. Here's
Speaker 4: the positioning and set up and crowding of other pods,
Speaker 4: you know, playing the game you mentioned earlier. So there's
Speaker 4: then the sort of second level of art that goes
Speaker 4: to the top of that.
Speaker 2: Yeah, So I want to talk more about the speed
Speaker 2: of turnover because Okay, let's say you're like bullish on
Speaker 2: and video and videos had this big run and you're like,
Speaker 2: all right, but I don't want to have size exposure
Speaker 2: because it's going to be correlated to big caps. I
Speaker 2: don't want to have general market beta because probably if
Speaker 2: the market goes up and video is going to go up,
Speaker 2: and I don't have chip beta and all this stuff.
Speaker 2: So what you're trying to identify is just the Invidia
Speaker 2: specific idiosyncredit. That's exactly right, right, But why does that
Speaker 2: inherently lend itself when you're thinking about I mean, I
Speaker 2: feel like there must be some connection, But you're trying
Speaker 2: to strip out all of these different factors that you
Speaker 2: don't want to have exposure to. You're trying to find
Speaker 2: the idiosyncratic drivers of a specific name. What is it
Speaker 2: about that process that sort of inherently lends itself to
Speaker 2: shorthold periods.
Speaker 4: That's a great question, and as sort of deep one,
Speaker 4: and you might get different answers to that question from
Speaker 4: a few different people. I'll give you mine. The essential
Speaker 4: reality is that In order for this entire model to work,
Speaker 4: you have to have a great deal of diversification across
Speaker 4: idiosyncratic bets non factor bets. And the way to think
Speaker 4: about that is the core reason a lot of these
Speaker 4: models work, is that the residual return or idiosyncratic return
Speaker 4: is approximately normally distributed across a certain window. Meaning it's
Speaker 4: sort of, you know, like flipping a coin basically, And
Speaker 4: the intuition is, if you flip a thousand coins, obviously
Speaker 4: you'll center around whatever your hit rate is on that coin.
Speaker 4: If the coin is loaded fifty two percent, yeah, versus
Speaker 4: fifty As you flip three coins, it could be you know,
Speaker 4: the mean, the expected value of that is going to
Speaker 4: be you know, who knows, right, But as you flip
Speaker 4: a thousand coins or ten thousand coins, you will center
Speaker 4: around that mean. And so and that variance is effectively,
Speaker 4: if you think about things from a return standpoint, the
Speaker 4: sharp ratio, right is they're returned about it about the
Speaker 4: variance of the volatility of that return. And so as
Speaker 4: you have more and more bets, you shrink the variants
Speaker 4: relative to the return you're generating. And the more and
Speaker 4: more your bets are in idiosyncratic bets which are normally
Speaker 4: distributed unlike market bets you know, which are you know
Speaker 4: can be wild.
Speaker 2: Right, Wait, can you actually just explain that point, because
Speaker 2: that's a great answer. You're basically you have some assumption
Speaker 2: about returns, but there's going to be a lot of variants,
Speaker 2: so you want to make a lot of bets. That's
Speaker 2: exactly in order to achieve that. Why is it that
Speaker 2: idiosyncretic returns are normally distributed and such as you described totally?
Speaker 4: Yeah, so what you're actually solving for is you go
Speaker 4: down the factor model building a rabbit hole is cross sectional,
Speaker 4: normally distributed, meaning across the universe of stocks within a
Speaker 4: period of time. Okay, so that's kind of also what
Speaker 4: the model solves for, and it sort of solves for
Speaker 4: a combination effectively of what's the highest R squared meaning
Speaker 4: how much of the model explains what's happening across stock
Speaker 4: movements across different stocks in the market, and then the
Speaker 4: output of any regression within its period is going to
Speaker 4: produce that result of a normally distributed kind of residual term.
Speaker 4: But the key way that this model works is that
Speaker 4: it's normally distributed, not across time but across stocks within
Speaker 4: a given period. And so what that means is you're
Speaker 4: going to have you know, as many stocks that are
Speaker 4: on the residual basis that are outperforming in a period
Speaker 4: as that are underperforming on this residual basis. Whereas, of course,
Speaker 4: if you just bet on semis right in a month,
Speaker 4: and you just were long Semis within a period within
Speaker 4: a month, right, that's not going to be normally distributed,
Speaker 4: of course, Right, It's just if you're managing to a
Speaker 4: model that is cross sectionally approximately normally distributed within a month,
Speaker 4: Let's say you're going to get winners and losers, and
Speaker 4: you're going to center around that hit rate basically.
Speaker 3: Okay, I get that, you keep mentioning a month. What
Speaker 3: is like a normal or a reasonable time horizon that
Speaker 3: these models like typically operate.
Speaker 4: Yeah, they're sort of calibrated to so technically the model,
Speaker 4: the regression runs daily. Actually, but when you are building
Speaker 4: any of these models, people calibrate them to sort of
Speaker 4: optimize for like, the average whole period of a discretionary
Speaker 4: stock picker is not a day obviously, and so you
Speaker 4: try to calibrate the bias of these models to say,
Speaker 4: and people actually you can run multiple models, say hey,
Speaker 4: we're going to run one that's calibrated for a one
Speaker 4: month horizon or a six month horizon or whatever. And
Speaker 4: so you're trying to pick the calibration horizon that matches
Speaker 4: the investor that we're talking about. So I mentioned a
Speaker 4: month because a lot of the multi managers, let's say
Speaker 4: the average hole period ends up around a month, you know,
Speaker 4: twelve that's twelve times turns a year, but it could
Speaker 4: be high. It could be seventeen turns, it could be
Speaker 4: eight turns. They are managers who are in that range.
Speaker 2: Just to go back to the question of idea generation,
Speaker 2: you're going to hold a stock from month, maybe maybe
Speaker 2: a few weeks, maybe a little longer. Some analysts who's
Speaker 2: like monitoring all this stuff, what goes into it? Someone
Speaker 2: says to you, okay, like you're doing materials and or commodities. Yeah,
Speaker 2: and they say, suddenly you have a bullish view on
Speaker 2: exony something or some small shale player. What happened before that?
Speaker 2: Totally that led to that idea? Well, not just that
Speaker 2: they like the stock, but that they like the stock
Speaker 2: in a very short period of time.
Speaker 4: Yeah. So, and this is not always true, but as
Speaker 4: a sort of simplified rule of thumb. Typically the winners
Speaker 4: are going to be on longer than that month, okay,
Speaker 4: and you know, you realize you were wrong about something,
Speaker 4: then you cut that. And there's trading turnover as well.
Speaker 4: That's not pure idea turnover, if that makes sense, which
Speaker 4: is idea generation. So that's going to be a little
Speaker 4: slower too. But anyway, with with those caveats to your question, yeah,
Speaker 4: so there's in an ideal world you do a you
Speaker 4: sort of separate the idea generation process into two steps.
Speaker 1: Okay.
Speaker 4: The first is initiation, where you sort of learn about
Speaker 4: the stock, if that makes sense, And in that process
Speaker 4: you basically do all the things I mentioned that a
Speaker 4: core value oriented fund does in terms of thinking about, Okay,
Speaker 4: what's the long term of this, what's the secular trend
Speaker 4: within companies, who's gaining who's losing share. In order to
Speaker 4: do that, you do all the classic Warren Buffett stuff.
Speaker 4: I mean, you understand, you look at industry earning earnings
Speaker 4: and earned industry reports and filings and all of those
Speaker 4: kinds of things. You talk to experts also as part
Speaker 4: of that process. That could be any of the expert
Speaker 4: network calls somebody who were people who were executives, and
Speaker 4: that can inform that initiation and understanding of the industry
Speaker 4: as well. And some people spend you know, some analysts
Speaker 4: spend the majority of their time doing sort of initiation
Speaker 4: type work that sort of build a deep financial model
Speaker 4: that tries to build not just from like the high
Speaker 4: level revenue but to unit economics like okay, And by
Speaker 4: that I mean like, you know, if you're looking at
Speaker 4: a coffee shop, like, okay, how many cups of coffee
Speaker 4: do this? Hell, what's the price? How much is that
Speaker 4: going to change? What are the inputs to a cup
Speaker 4: of coffee? And just trying to get to that level
Speaker 4: of granularity on unit economics. Yes, and so that's kind
Speaker 4: of like the initiation process, and then ongoing coverage is
Speaker 4: a little bit more of Hey, I have a view
Speaker 4: from that initiation work on sort of long term relative
Speaker 4: winners and losers in a space. I have an understanding
Speaker 4: of uniit economics of each player and how each of
Speaker 4: those is kind of heading. And then the ongoing maintenance
Speaker 4: process is a lot to do with what data sets,
Speaker 4: what data points, what conversations from an industry conference standpoint
Speaker 4: or whatever can I do to understand more granularly how
Speaker 4: each of those unit economics points is changing. And then
Speaker 4: finally also like there's this question of crowding and positioning
Speaker 4: and understanding what everybody else thinks, that sort of weighing
Speaker 4: machine versus voting machine ben Gram classic analogy, but you
Speaker 4: sort of separate that process, have that secular view, and
Speaker 4: then you're trying to understand what data sets. So that
Speaker 4: could be like all data sets, It could be industry conferences,
Speaker 4: it could be talking to people in the industry through
Speaker 4: the supply chain. It could be you know, people always
Speaker 4: should be doing this but don't always actually in practice
Speaker 4: doing it. But your analyst should understand if they're covering
Speaker 4: an auto company, they should understand auto suppliers, and they
Speaker 4: should understand the downstream of that. So each of those
Speaker 4: sort of up and down the value chain. That's like
Speaker 4: a big I'd say in reality, a differentiator among analysts
Speaker 4: is how deep into the value chains you're seeing what's
Speaker 4: happening to inform you about the changing trends in those
Speaker 4: unit economics that you had a baseline view about at
Speaker 4: the beginning of the sort of initiation you understanding the industry.
Speaker 3: Convince me or you don't have to convince me. You
Speaker 3: could try to try to convince me, or you could
Speaker 3: agree with me. I don't know, convince me that this
Speaker 3: isn't just momentum trading with some added maths and maybe
Speaker 3: efficiencies coming from like centralized risk management and capital management systems.
Speaker 4: Okay, so on the convincing part. So momentum itself is
Speaker 4: a factor in every essentially commercial factor model, and so
Speaker 4: you're actually therefore, because you were limited constrained on your
Speaker 4: factor bets, you're constrained on how much like just momentum
Speaker 4: you can be long. Ever, so you're limited in your
Speaker 4: ability to be long, and momentum can have nuance like
Speaker 4: do you calculate momentum over a six month window a
Speaker 4: nine month window and what are the inputs to that,
Speaker 4: But in aggregate, you're actually limited in your ability to
Speaker 4: be long or short momentum at all. It's actually one
Speaker 4: of the most focused on factors within commercial factor models
Speaker 4: that everybody asks about all the time. So that's like
Speaker 4: point one to mention on momentum. The other is the
Speaker 4: way you described at the beginning was interesting too, because
Speaker 4: there's a concept of factor investing where you're betting on
Speaker 4: the factor, meaning you're finding cheap ways to be long
Speaker 4: momentum or cheap ways to be long the value factor
Speaker 4: or other pieces. And that's the kind of growing and
Speaker 4: that ties into the whole sort of growth of passive
Speaker 4: and all those things. What these risk models actually do
Speaker 4: in the multi managers essentially are the elimination of factor
Speaker 4: bet meaning it's the opposite. It's kind of a mirror
Speaker 4: image of that, where you're sort of eliminating the factor
Speaker 4: bets entirely and trying to find just the performance in
Speaker 4: the residual. That then leads to this question of like
Speaker 4: what factors exist inside the residual term that are not
Speaker 4: momentum And that's where you get the concept that you
Speaker 4: mentioned earlier, like a pods blowing up and what's positioning
Speaker 4: and crowding and nuances there, which something we spent a
Speaker 4: lot of time thinking about, Okay, like how do we
Speaker 4: mathematize how do we characterize that? And what gives information
Speaker 4: incrementally beyond? Okay, you've eliminated this sort of straightforward momentum topics.
Speaker 4: You've eliminated did value What within that residual can give
Speaker 4: you more and more insight beyond just like the core
Speaker 4: research work can we talked about earlier.
Speaker 3: I'm glad you mentioned the sort of off the shelf
Speaker 3: commercial factor models because this is something that came up
Speaker 3: in our conversation with Gappy as well. So in order
Speaker 3: to be factor neutral, you have to be able to
Speaker 3: identify the factors in the first place. And my understanding
Speaker 3: is that most of the pods will just purchase those
Speaker 3: models from a company like yours.
Speaker 4: Yeah, So what people do is is kind of a
Speaker 4: full spectrum of the way people implement a factor awareness
Speaker 4: or factor neutrality strategy. Some will buy a single model
Speaker 4: and sort of view that and then integrate that in
Speaker 4: whatever way they do. And at the other end of
Speaker 4: the spectrum, and there are funds, sort of the most
Speaker 4: heavily infrastructured funds, it'll buy several factor models and pick
Speaker 4: and choose different Hey, I think this factor is constructed appropriately.
Speaker 4: Here this factor is less well constructed by this model,
Speaker 4: and kind of put them together. And then there's sort
Speaker 4: of also a spectrum in terms of people software tooling
Speaker 4: that they how far down they hand into the organization
Speaker 4: a sort of sophisticated tool to let portfolio managers see
Speaker 4: what are my factor exposures. So like some places there's
Speaker 4: a total separation almost of church and state of you know,
Speaker 4: stock picking and risk management, and that is partly a function.
Speaker 4: There could be a philosophy element to that, and there
Speaker 4: could also just be a constraint. I mean, it takes
Speaker 4: engineers and time and money and focus to build all
Speaker 4: this stuff. So some places will have nothing in terms
Speaker 4: of tooling, and they'll just have a risk team that
Speaker 4: kind of looks at books and helps people understand their
Speaker 4: risks on a sort of shorter cycle meaning longer cycle,
Speaker 4: like it'll take oh, once a week, once a month, whatever,
Speaker 4: they'll get a report on their risks, or they'll check
Speaker 4: in et cetera, et cetera. And then at the far
Speaker 4: end you have funds that have like full software platforms
Speaker 4: that hand and to it portfolio manager like okay, if
Speaker 4: you change this, what happens to that? If you want
Speaker 4: to sort of see what the optimization math does for
Speaker 4: you instantly, can you see that? And so that's kind
Speaker 4: of the spectrum of what things do. And we sort
Speaker 4: of provide that software toolkit everything from the risk model
Speaker 4: as you mentioned, like the core underlying factors all the
Speaker 4: way up to the software infrastructure that lets you just
Speaker 4: play with it. Okay, if I had a billion dollars
Speaker 4: in video, what does this do to my risk numbers,
Speaker 4: that idio number of fact number? What is it do
Speaker 4: to each of my factor exposures? And then how does
Speaker 4: that change dynamically? And it'll also sort of like find
Speaker 4: hedges for you, Like what single stocks would optimally hedge
Speaker 4: this book in this way. Now, of course it's still
Speaker 4: on you to pick stocks, but it it'll source. Okay,
Speaker 4: I've got a whole universe of stocks. What single stocks
Speaker 4: would offset this in video? Or these five single stocks
Speaker 4: would offset that?
Speaker 2: Just to go back, and then I want to talk
Speaker 2: more about the software and what you sell, et cetera.
Speaker 2: But just to go back, one last question on the
Speaker 2: idea of like actually selecting a stock. You know, you
Speaker 2: mentioned maintenance, and the analyst really builds out a coverage
Speaker 2: universe and then they really to know the unit economics
Speaker 2: of the coffee shop or the company that makes you know,
Speaker 2: something for a car or whatever. But then what do
Speaker 2: they see to say and now we should buy it,
Speaker 2: Like what would be the signal that they're looking for
Speaker 2: in the market that say, you know, again on some
Speaker 2: short term period. This is really I've gotten to really
Speaker 2: know this company, but there's something about X right now
Speaker 2: that makes it a compelling buy for a short term period.
Speaker 4: Totally. The core idea is that you're looking for differential insight,
Speaker 4: meaning something that changes the perception of everybody else about
Speaker 4: the value of this company in a long term sense.
Speaker 4: So meaning I see if the market's perception is pick
Speaker 4: a coffee shop is going to grow, and people will
Speaker 4: the market it's, you know, whatever the market means. But
Speaker 4: typically the market is who is the marginal price cetter
Speaker 4: of a stock basically, and there's a perception there implicit
Speaker 4: in the price at a minimum about okay, how many
Speaker 4: units of coffee and what's the price of those coffee
Speaker 4: cup's going to be, and what's the underlying cost.
Speaker 2: You're waiting for moments in which you believe something is
Speaker 2: going to emerge. Yes, that will change the long term expectation.
Speaker 4: That will exactly, that will change the you know, And
Speaker 4: there are other situations like tactical things where hey it's
Speaker 4: so heavily shorted, Yeah that'll change slightly, And I'm really
Speaker 4: looking for a short term catalyst, or hey, look everybody's
Speaker 4: expecting this next all data print to mean something specific,
Speaker 4: and they're all positioned on one side. That's where crowding
Speaker 4: positioning comes into the equation. And everybody's position this way,
Speaker 4: and I think it's going to go the other way,
Speaker 4: and I've got a very tactical thing that is a
Speaker 4: part of the equation, but a much larger part of
Speaker 4: the equation are still catalyst driven, Like Okay, there's a
Speaker 4: data point that comes out, but it's a data point
Speaker 4: that indicates something about the overall perception of where this
Speaker 4: company is headed. And so like classic ones and software
Speaker 4: can be changes in churn direction and like where people
Speaker 4: can get smart on that is often like okay, there's
Speaker 4: an overall headline churn number, but then there's like like
Speaker 4: if it's an internet company or something like that, or
Speaker 4: subscriber company, and then you can go down the line
Speaker 4: like okay, if somebody's looking at churned by region and
Speaker 4: has some forward look on something that gives them insight
Speaker 4: to like, okay, churin is changing in this region, and
Speaker 4: this reading is small today, so it actually doesn't hit
Speaker 4: the headline churn number. Yeah, but that's actually structurally growing
Speaker 4: faster than every other region, and so the underlying churn
Speaker 4: rate that looks like it's this level is going to
Speaker 4: step up structurally because this smaller region is going to
Speaker 4: be a bigger part of the overall path. That's the
Speaker 4: kind of thing.
Speaker 2: At some point, by the way, we really need to
Speaker 2: do another I'm sure we've done on the past a
Speaker 2: deep episode on all data, because yeah, Walmart satellites of
Speaker 2: Walmart parking lots and like credit cards, I've heard about it,
Speaker 2: but it's like, I know, there's more to it, and
Speaker 2: there's you know, it's important. You mentioned the different shops
Speaker 2: have different software infrastructure, and the level at which it's
Speaker 2: on the managers different sometimes and the different which it's
Speaker 2: at the umbrella level, So like does that mean that,
Speaker 2: like does it happen where the at the very high
Speaker 2: end of risk management they look across and they say, wow,
Speaker 2: you know, in aggregate, our portfolio managers, maybe perhaps unintentionally
Speaker 2: or even within their remit, have built up a lot
Speaker 2: of implied exposure to momentum or implied exposure to rates,
Speaker 2: or implied exposure to value. And then what do they do,
Speaker 2: like tap people on the shoulder and say, like, how
Speaker 2: what happens?
Speaker 3: Then?
Speaker 4: Yeah, absolutely, so again this sort of a spectrum of
Speaker 4: people's technology and factor awareness risk systems, but at the
Speaker 4: sort of platonic ideal of that that you know exists
Speaker 4: in various forms. There's sort of a CIO level, there's
Speaker 4: a you know, COO and risk team level, there's the
Speaker 4: PM level. There's even an analyst level that sort of
Speaker 4: is monitoring each level of that. So like you'll put limits,
Speaker 4: as we talked about on the portfolio level, right on
Speaker 4: an aggregate risk basis, and then on an individual factor
Speaker 4: you'll say, okay, you can have more than blank percent
Speaker 4: of your variants in your book in a specific in
Speaker 4: any specific factor, So put those limits individually, and then
Speaker 4: exactly as you said, they roll it up just like
Speaker 4: you know, you just add up the line items. Essentially,
Speaker 4: all these models are structurally linear decomposit so they add
Speaker 4: up actually linearly, So like John's momentum exposure in dollar terms,
Speaker 4: here Jaill's exposure is there, and they add up. So
Speaker 4: you do see aggregate level CIO level kind of hey,
Speaker 4: we're net lung blank or whatever at that level, and
Speaker 4: it depends how teams structure their limits and how tightly
Speaker 4: they limit exposures at the portfolio level. But you will
Speaker 4: see aggriate exposures, and then there are ways to take
Speaker 4: like an ETF or a basket or a custom basket
Speaker 4: that will just limit out We'll just literally hedge that basket.
Speaker 4: And there's nuance even there, like, hey do I can
Speaker 4: I build a basket that hedges out that exposure but
Speaker 4: doesn't actually basically end up being short at the same
Speaker 4: stocks I'm long underlying the book, you can see how
Speaker 4: that can get into a whole rabbit hole of like
Speaker 4: sort of technical behind the scenes execution detail. But at
Speaker 4: the high level, you sort of roll up the exposures,
Speaker 4: you add them up, and you say, am I long
Speaker 4: or short one or two or three or all the factors,
Speaker 4: and let me balance those out at an aggurate level.
Speaker 3: So one thing that often comes up in discussions of
Speaker 3: risk management software that's been popularized on Wall Street, and
Speaker 3: I'm thinking especially you hear this a lot about and Aladdin,
Speaker 3: but this idea that if everyone's using the same risk
Speaker 3: management software, then is there a risk that you could
Speaker 3: get everyone like doing the same thing at the same time, so,
Speaker 3: for instance, a mass deleveraging event because everyone software is
Speaker 3: like based on a particular model and one thing happens
Speaker 3: and the model spits out and says, everyone needs to
Speaker 3: sell right now. Is that a risk? Is that like
Speaker 3: a realistic risk or is it the case that all
Speaker 3: of this off the shelf risk management software is so customizable,
Speaker 3: I guess, and there's still that discretionary factor for the
Speaker 3: PMS that you don't really get that hurting behavior.
Speaker 4: M So I'd say yes and no. I think in
Speaker 4: the no camp, the fact is that you're kind of
Speaker 4: eliminating those sources of exposure that are common. So you're
Speaker 4: kind of trying to focus people on residual bets. And
Speaker 4: you know, for example, that could be oversimplifying, but that
Speaker 4: could be long and short pepsi, or long pepsi and
Speaker 4: short coke, and that would equivalently neutralized factors. Let's assume
Speaker 4: they're kind of proxies for each other. And so kind
Speaker 4: of what the model lets you do is kind of,
Speaker 4: instead of having to be perfect pairs in the Alfred
Speaker 4: Sloan original hedge fund concept, where you have to in
Speaker 4: order to be factor neutral, you just have to find
Speaker 4: perfect comps, it kind of lets you pick non perfect
Speaker 4: comps but end up in a risk place that is
Speaker 4: similar to that where your only bet is on a
Speaker 4: single stock. But anyway, so like what the model is
Speaker 4: pushing you to is not any specific stock, right, It's
Speaker 4: telling you to pick which one of the stocks that
Speaker 4: don't have comparable factor exposures is more attractive. So that's
Speaker 4: one level. The second is there is leverage, and the
Speaker 4: leverage you're putting on is not leverage against beta. That's
Speaker 4: the distinction that I think people often allide is that
Speaker 4: when you think of like LTCM or maybe forgetting even LTCM,
Speaker 4: but any fund that takes very high leverage on a
Speaker 4: beta a directional bet that's beta on a factor, and
Speaker 4: the issue with that is many issues with that. If
Speaker 4: you're taking lots of leverage on a beta is there's
Speaker 4: just sort of that risk that it has a big
Speaker 4: draw down. The hope, I guess, or the sort of
Speaker 4: mathematical reality as you kind of pointed out that's actually
Speaker 4: been executed on is that when you're levering alpha, it's
Speaker 4: again it's sort of the quant fund world works this
Speaker 4: way too, is that what you're levering is just that
Speaker 4: residual term you're getting back to that coin flipping and
Speaker 4: you're finding a source of return that is normally distributed
Speaker 4: across stocks, and therefore if there is a big blow
Speaker 4: up in markets, actually typically the factors become more and
Speaker 4: more statistically significant, and so if you're neutral against those factors,
Speaker 4: the residual return remains a cross sexually normally distributed. So
Speaker 4: there's obviously a lot of detail under the hood, but
Speaker 4: the basic answer is that you're trying to find a
Speaker 4: type of return, and a diversified source type of return
Speaker 4: that doesn't have that risk in a blow up, So
Speaker 4: you're kind of levering alpha. That's the key kind of
Speaker 4: point versus beta. And the final yes answer to your
Speaker 4: question is that you are still levered. So notwithstanding everything
Speaker 4: you can do to sort of solve the mathematic piece
Speaker 4: of this equation, you still have some risk that the
Speaker 4: person providing you the leverage has a business problem or
Speaker 4: somebody who like whoever is providing that leverage to you,
Speaker 4: which is typically the banks. Basically that person for whatever
Speaker 4: reason needs to pull that leverage or whatever, and that
Speaker 4: it's almost a little bit even in the category of
Speaker 4: business risk that exists intrinsically with leverage. So that's kind
Speaker 4: of the the yes portion of the answer.
Speaker 2: I'd say this type of software, these models. They exist,
Speaker 2: they've existed for a while.
Speaker 4: That's right.
Speaker 2: When you started your company, are kinda what was the
Speaker 2: theory that there was need for more?
Speaker 4: Yeah, and it's what you mentioned at the beginning too,
Speaker 4: which is the sort of the theoretical beauty of these
Speaker 4: models and how it all works, and the normally distributed
Speaker 4: residuals and the sort of diversification of alpha and the
Speaker 4: levering of alpha. But what really has happened over you know,
Speaker 4: decades now is that that model has been proven to
Speaker 4: be at least have something. It may not be the
Speaker 4: only model that's viable to make attractive returns for investors,
Speaker 4: but at least that sort of you know, result has
Speaker 4: jumped from the sort of academic theory to realized practice.
Speaker 2: And yeah, and it also explains why we're seeing some
Speaker 2: pretty big launches in.
Speaker 4: This absolutely absolutely, Yeah, it certainly has jumped that gulf.
Speaker 4: And you know, look, in the quantitative world, it made
Speaker 4: that jump long ago. It's in the fundamental stock picking
Speaker 4: world that it made that and again, a few firms
Speaker 4: had been doing it for a long time, but it
Speaker 4: sort of made the most convincing leap over the last
Speaker 4: whatever five to ten years, where it just sort of
Speaker 4: decisively generated very attractive risk adjusted returns for investors and
Speaker 4: kind of proved that sort of synthesis, which is really
Speaker 4: what's happening between the sort of quant view of the
Speaker 4: world of factorization and finding idiosyncratic or residual performance within
Speaker 4: inside what's left over after the factors synthesized that risk
Speaker 4: and sort of quant perspective with that Warren Buffett's style
Speaker 4: fundamental type research and analysis and work, that synthesis was
Speaker 4: implemented by a few firms and now it's sort of
Speaker 4: proven itself to work in a lot of different ways.
Speaker 4: So that's what's happening. So from our angle, like what
Speaker 4: we have seen is just that wide range as I
Speaker 4: kind of mentioned earlier of execution of that, like how
Speaker 4: easy is it to actually have a system in place
Speaker 4: for a portfolio manager or analyst or cio. How sort
Speaker 4: of not only user friendly but sort of functional. How
Speaker 4: efficiently can it source new hedge ideas that balance out
Speaker 4: a specific factor exposure? How efficiently does it connect that
Speaker 4: risk perspective of where I'm long and short? To a
Speaker 4: topic you mentioned earlier, performance attribution, like where are my
Speaker 4: generating returns? What are my hit rates, What are my
Speaker 4: hit rates on residual versus on factor? What are my
Speaker 4: hit rates on earning season versus outside of earning season
Speaker 4: and on a residual basis, And how does that connect
Speaker 4: to my risk and my portfolio construction? And all of
Speaker 4: that is a lot of work, you know, it's a
Speaker 4: lot of painful kind of putting together the software and
Speaker 4: the risk and all the different elements together. And as
Speaker 4: I mentioned, what we see is some funds have done this,
Speaker 4: you know, at a level that is really excellent, and
Speaker 4: some funds, most funds, because they have to do the
Speaker 4: very hard work of stock picking. It's a very challenging job.
Speaker 4: You have to have incredible IQ allocated to that problem
Speaker 4: and effort. They have, Okay, systems of a sheet that
Speaker 4: gives some risk insight, but it doesn't have the sort
Speaker 4: of detailed input output experience of let me tweak this,
Speaker 4: let me see what happens there, let me understand how
Speaker 4: it connects. And so putting all those elements together is
Speaker 4: what we kind of hope to do.
Speaker 3: When did you actually found our.
Speaker 4: Camera a little over two years ago?
Speaker 2: Two years ago?
Speaker 3: Okay, so what's the difference between what clients ask for now?
Speaker 3: Versus what they were asking for two years ago. Because
Speaker 3: this is a rapidly evolving space.
Speaker 4: You know. I think in that sort of split that
Speaker 4: I mentioned of our client base that is kind of
Speaker 4: native to that risk world and the group that is
Speaker 4: sort of newer to it, I think the native group
Speaker 4: has this constant sort of question set of how do
Speaker 4: I make this again more functional? See more analysis more quickly,
Speaker 4: how everything relates to each piece? Can I see insights
Speaker 4: on crowding and how that relates to my book? And
Speaker 4: can I see all those different pieces. So that's kind
Speaker 4: of like a steady escalation in thoughtfulness. I would say,
Speaker 4: as you hand the portfolio manager tools on this factor,
Speaker 4: and you also sort of empower them because again and
Speaker 4: a lot of these organizations are set up where there's
Speaker 4: a risk side and a portfolio management function, and you know,
Speaker 4: the portfolio manager isn't necessarily the client of the risk
Speaker 4: in house at these places. But as there becomes this
Speaker 4: industry of people like us who are providing these tools,
Speaker 4: in a way we have to be a little more
Speaker 4: responsive to the portfolio manager says Okay, I see how
Speaker 4: that was built. Can I double click?
Speaker 2: I mean the portfolio manager isn't necessarily a client.
Speaker 4: So at a big multi manager, you have a risk
Speaker 4: division which kind of sits under the CIO almost, and
Speaker 4: you have the portfolio managers, and the portfolio managers aren't
Speaker 4: the client of the risk people. The risk people kind
Speaker 4: of work, if you want to put it that way,
Speaker 4: for the CIO, who says, you know, sort of it's
Speaker 4: kind of a limitter in some rice. It's kind of
Speaker 4: a constraint in a lot of cases. And the best
Speaker 4: places are doing it where it's completely synergistic, where you're
Speaker 4: using the risk tools and this factor awareness and all
Speaker 4: of the things you can do with that on offense,
Speaker 4: not just defense. So that is happening at a few places,
Speaker 4: but there's a whole other group of places where it's
Speaker 4: kind of, hey, this limits me, This isn't working with
Speaker 4: or for me. And so as this becomes a little
Speaker 4: bit more, you know of an industry or like, we
Speaker 4: do work for them, right, so we're hey, I want
Speaker 4: this additional feature that you know they're the client. Right.
Speaker 4: So there's that piece, but there's sort of this constantly
Speaker 4: escalating sort of demand for tooling and incremental insight and okay,
Speaker 4: let me click this, let me understand this across my
Speaker 4: whole universe, across the entire universe stocks, I could cover
Speaker 4: all those kinds of tools on the sort of the
Speaker 4: people who don't come necessarily out of the POD systems.
Speaker 4: The interesting thing is the extent to which people want
Speaker 4: to focus on. Okay, let me, how do I frame
Speaker 4: that system that factor awareness instead of in a market
Speaker 4: neutral context. But hey, I'm a long only or I'm
Speaker 4: a sort of directionally oriented value fund or whatever. How
Speaker 4: do I reorient the model shift it to be sort
Speaker 4: of true comparative to my benchmark? And so that's been
Speaker 4: an interesting evolution is the types of investors who are
Speaker 4: not structurally market neutral but still want all the insights
Speaker 4: from this where you can recalibrate the entire model against
Speaker 4: a benchmark. And so that's been one example.
Speaker 2: Rich Folk Wallace, thank you so much for coming on
Speaker 2: od lotch. That was a fantastic learned and now have
Speaker 2: like ten ideas for further episodes we have to do,
Speaker 2: which is always, as we say, the test of whether
Speaker 2: we had a.
Speaker 4: Good conversation or absolutely absolutely Thanks so much for having me.
Speaker 2: Really appreciate it, Tracy, I thought that was great. I
Speaker 2: really do have like there's like ten more episodes that
Speaker 2: we have to do now. But that was very illuminating
Speaker 2: on multiple levels, particularly about like what the job of
Speaker 2: the PM or the analyst actually is in these contexts. Yeah.
Speaker 3: Absolutely, And also I was thinking it kind of dovetailed,
Speaker 3: interestingly enough with the conversation we had recently about thematic investing, Yes,
Speaker 3: James Fankiland, where he was talking about like, okay, price
Speaker 3: is obviously a factor, but also you kind of want
Speaker 3: to identify the story that everyone's going to latch onto.
Speaker 3: And then Rich was talking about how when you're coming
Speaker 3: up with investment ideas, you're sort of trying to identify
Speaker 3: something that will change everyone's perception of the trajectory of
Speaker 3: a particular stock or investment totally.
Speaker 2: And I thought it was just like really interesting this
Speaker 2: idea that like, okay, like no one knows what's going
Speaker 2: to happen tomorrow, some major event could take place that
Speaker 2: causes the you know, the whole market to crash. I
Speaker 2: guess big events don't usually happen to cause the whole
Speaker 2: market to surge, Unfortunately, it's always the other way around,
Speaker 2: Like nobody knows what interest rates are going to do,
Speaker 2: and we know you know, a lot of stocks are
Speaker 2: tied to interest rates, and no one knows maybe some
Speaker 2: chip company will come out tomorrow that beats and video whatever.
Speaker 2: No one knows any of that stuff. And then this
Speaker 2: idea that if you can then strip out all of
Speaker 2: this and then identify the idiosyncratic drivers of a stock,
Speaker 2: and then those idiosyncratic drivers of stock almost inherently, some
Speaker 2: will be winners and some will be losers. Yeah, I
Speaker 2: could see why. Then the game is lots of bets
Speaker 2: over relatively short time periods. Like that that like really
Speaker 2: clicked to me in this conversation.
Speaker 3: Yes, that's the other thing that stood out to me,
Speaker 3: like the idea of diversification across those different bets. Like, yeah,
Speaker 3: I hadn't really, I guess, like when you think about
Speaker 3: hedge funds still, even though multi strategy funds are sort
Speaker 3: of where it's at SAE, Yeah, I still think about
Speaker 3: like that classic I don't know, Bill Ackman type thing
Speaker 3: where you make one big bet on something and that's
Speaker 3: your source of alpha. But again, the thing that's coming
Speaker 3: through in this conversation is really like the diversification aspect,
Speaker 3: the desire to be factor neutral and to lever the
Speaker 3: alpha instead of the beta.
Speaker 2: All kinds of interesting stuff there. I want to do
Speaker 2: more on what do I do more? I well, I
Speaker 2: definitely want to do more on al data because I
Speaker 2: feel like usually when that gets discussed, it's like this
Speaker 2: like very like sort of tired cliche ways, like I
Speaker 2: know everyone has a credit card day, but I want
Speaker 2: to understand more about that. I don't know, there's a
Speaker 2: lot more that we can also, just like the different models,
Speaker 2: Like I'm sort of fascinated that, like there's all of
Speaker 2: these different multistrad funds that exist, and the fact that
Speaker 2: they're not all the same is interesting to me. And
Speaker 2: the fact that like where the risk manager sits in
Speaker 2: the amount of tools and what they build in house
Speaker 2: and what they don't, and the degree of flexibility that
Speaker 2: pods get and you know, what analysts actually do and
Speaker 2: stuff like that. There's much more to do.
Speaker 3: I really want to do an episode on differences in
Speaker 3: compensation models.
Speaker 2: Oh yeah, at the pod shops.
Speaker 3: I think that would be really interesting because that would
Speaker 3: also feed into I assume investor behavior. Yeah all right, Well,
Speaker 3: now that we've come out of that with like ideas
Speaker 3: for ten more episodes, shall we leave it there.
Speaker 2: Let's leave it there.
Speaker 3: This has been another episode of the All Thoughts podcast.
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.
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