How the Hottest Hedge Funds on Wall Street Really Manage Risk

Multi-strategy hedge funds, also known as "pod shops," have become the hottest ticket on Wall Street. The business model is supposed to allow hedge funds to operate more efficiently. That includes deploying capital in a more productive manner and better managing risk. But how does risk management at some of the most sophisticated funds on Wall Street actually work? In this episode, we speak with Rich Falk-Wallace, formerly of Citadel and now the founder and CEO of Arcana, which provides risk management and portfolio software for multi-strat funds. We talk about how risk models are impacting investor behavior and wider markets, how multi-strat traders come up with their ideas, and the factors that go into sizing and evaluating their positions.

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Episode Transcript

Speaker 1 (00:02):
Bloomberg Audio Studios, Podcasts, Radio News.

Speaker 2 (00:18):
Hello and welcome to another episode of the Odd Lots podcast.

Speaker 3 (00:22):
I'm Joe Wisenthal and I'm Tracy Alloway.

Speaker 2 (00:24):
Tracy, I still want to learn more about how multi
strategy hedge funds work.

Speaker 3 (00:30):
I thought you were going to say, I still don't
know anything about multi strategy. I feel like we're slowly
getting there, and hopefully our listeners don't mind coming along
with us for the ride. I feel like every time
we have an episode on multi strategy hedge funds, or
on the pod shops as they are sometimes called, we
are deepening our understanding and we're sort of getting into

(00:52):
more and more detail. And I feel confident that one day,
after we've done like fifty episodes on this topic, we
will get there.

Speaker 2 (01:00):
I do think it would take about fifty I think
that's like an accurate number of what it would actually
take to get there. But of course, most recently we
had that episode with Giuseppe Pallioligo Gappy talking about some
of the big ideas and sort of from a high
level of how some of these funds actually work. They're
very popular. They've done some of the big ones that

(01:22):
people know, like the millenniums, like the Citadels have just
had incredible runs. Really seems to be displacing a lot
of the old style quant disrupting the sort of fund
of funds idea that was popular. I have some sense,
you know, you have all these managers and you give
them very specific mandates and they have to really focus
on that, and then if they're not too correlated with

(01:44):
each other, you can get above market returns in theory
and apparently in practice, but like how that actually works,
I still really don't know.

Speaker 3 (01:53):
Well, Okay, so two things. Number One, everyone should definitely
go and check out Gappy's book if you haven't already,
Advanced Portfolio Management. A lot of the references that I'm
about to throw out on this episode, anything that I
say that might sound even remotely impressive or like I
know what I'm talking about, has come up Gappy's book.

(02:14):
And also I will say I've read that book going
to and from work on the subway. It's pretty short,
so I think I did it in like a week.
And I have never gotten so many people like talking
to me on the subway when they saw me pull
out Advance Portfolio Management and they're like, what is.

Speaker 2 (02:30):
That that is very New York.

Speaker 3 (02:31):
Yes. And then secondly, the other thing I will say
is we've been talking about multi strategy hedge funds. We
want to learn more about them because they're this new
thing on Wall Street that everyone seems very excited and
interested in. But beyond that, there are recent events that
make this an even more pressing topic. So we've seen
some of the big winners in the market in recent

(02:53):
months start to come down, So the big tech names
things like Nvidia, we've seen small caps shoot up. A
lot of people are talking about whether or not this
is a factor rotation, and we'll get into what factors
actually are. But I think the discussion that we're seeing
right now, and I should caveat this with it is
July eighteenth. So we've seen those big moves in the

(03:15):
market very recently. The discussion that's happening now is how
much does the I guess growth in factor investing feed
into some of these moves, and also how does the
risk models that go alongside this actually impact investor behavior
and then also feed into these market moves. So is

(03:35):
it the case that everyone's getting out of big tech
because their risk models are telling them.

Speaker 2 (03:39):
Too totally and this is like a really important element
for sort of understanding both how these investment vehicles work
and the impact that they have on the market. Which
is one of the things we know is that the
various portfolio managers within these funds have very tight remits.
It's like, your team is responsible for trading chip stocks,

(04:00):
and your team is responsible for trading the short end
of the Brazilian yield curve, and your team is responsible
for international oil plays. And then we know that like
and then you're not allowed to take any sector beta,
and you're not allowed to take any market beta and
all these things, and so you see fact you're neutral
factor neutral, and then you know tight risk limits. So

(04:20):
if something starts to go down, you don't want to
lose your job, and you like get out of positions,
and that can create interesting moves for the market. Anyway,
suffice to say, there is much more to learn.

Speaker 3 (04:30):
Yes, well, the other thing, just one more thing, Yeah,
the other other thing. The other other thing that I
think is kind of funny now is remember whenever you
had weird market moves. Yeah, like I guess it would
have been fifteen years ago or something like that, it
was always quant funds like the quant quake before two
thousand and eight, and then it became CTA's and then
it was risk parody, and now it's very much the

(04:53):
pod shops that people point to when we start to
see sketchiness in the market. So I think we should
talk about, you know, what are the technicalities that are
driving that pod shop behavior.

Speaker 2 (05:02):
Every time there's some big move in the market, someone
tweets like, I hear a.

Speaker 4 (05:06):
Pod is blowing up. Yeah, Oh I hear some pods.

Speaker 2 (05:08):
Are blowing up. That's like, that's how to sound like
an in guy on a finance twit.

Speaker 3 (05:13):
Little do they know the pod that's blowing up is off?

Speaker 2 (05:16):
If you don't a good one. If you don't know
the pod that's blowing up, you're Anyway, we have the
perfect guest. I'm very excited. We are going to be
speaking with rich falk Wallace, who was previously a portfolio manager,
who's at Citadel, who's at Viking, and now he is
the CEO and co founder of Arcana, which builds models
and software to help investors and hedge funds, et cetera

(05:40):
actually track all of this stuff and actually track what
kind of risks managers are taking and how they're actually
performing relative to their benchmark or expectations. So we're gonna
maybe understand a bit more of the technical aspects of
all this stuff. So Rich, thank you so much for
coming on.

Speaker 4 (05:56):
As thanks so much for having me. I appreciate it.

Speaker 2 (05:58):
Why don't we start with your best and obviously we're
going to talk about your software company or Kenna and
all that. But you were previously at a couple of
these big funds. What did you do?

Speaker 4 (06:09):
Yeah, that's right. So I started my career on the
buy side, started originally in investment banking out of college
JP Morgan, and then worked at silver Point, which is
like a large credit distressed hedge fund, very value oriented,
none of that sort of risk model framework that gets
deployed at the pods. And then after that was at
Viking Global, which is I always describe the Tiger Cubs
in some ways as like a hybrid between the sort

(06:29):
of equity long short value orientation sort of philosophically and
the multi manager systems. And then finally, most recently, was
a portfolio manager at Citadel managed a global materials, natural
resources and materials portfolio.

Speaker 3 (06:43):
I love this because when I think about silver Point,
I think more sort of traditional value investing, and then
you wind up doing metals at Citadel, which is a
hedge fund that's known for being very quantitatively driven to
better understand the pods. Now, talk to us about the
differences between what you were doing at silver Point versus Citadel.

Speaker 4 (07:06):
Totally. Yeah, it's a great question. So the way that
any value, super deep value oriented kind of fund works
like a silver Point is that in the end, you
do a ton of very deep research on the company,
So you focus on what are the underlying fundamentals, what's
the contract structure out many years in the future, what
do the earnings look like, of course in the short term,

(07:27):
but also in the long term, what's structurally happening competitively?
You kind of go way down the rabbit hole. There's
a lot more and we can go into that. And
then as you kind of migrate sort of down the
time horizon spectrum, at least from what a thesis looks
like on a single stock, what you're kind of doing
is thinking about where are the catalysts that change the
market's perception of that long term. So, like I remember

(07:50):
when I joined Viking, I remember asking the question just generally,
like how much do you care about earnings. I think
for anybody who's very value oriented, you kind of are
concerned and about like, am I just going to be
focused on the next data point, the next earnings and
not sort of able to you know, see the forest
for the trees and sort of care about, you know,
what does this data mean for the long term? But
the answer I got back in general, not specifically there,

(08:14):
but is in that kind of framework it's or the
way the question was answered to me was, Hey, the
long term is a function a DCF is a function
of years. Years are a function of quarters, and so
therefore we care about the quarters. But what that tells
you is that, like the answer is what about the
short term, catalyst changes the perspective about the long term
valuation of the company. And so I think what people

(08:36):
sometimes looking from a far don't appreciate is the extent
to which there's actually a little bit more of a
convergence across styles from the underlying analyst workflow that like
even a very long term investor to some extent is saying,
even if I'm betting on the long term, the interim
proof points illustrate the view of that long term and
the short term guy says, well, I may get the

(08:57):
number right in the short term, but that only is
meaning to the change in the markets price if it
tells you something about that long term. And so there's
a little bit of like a yeah, and I think
that convergence is happening more and more where people are
kind of pushing towards that center actually, where everybody both
cares about the short term data point and is looking
to what that means about the long term. So but anyway,

(09:18):
at the beginning of that process, at the silver point
or any deep value type place, you're just really focused
on that longer term story. You're less focused on the
quarter or the catalyst than trying to understand sometimes things
that And I was a junior analyst when I kind
of started there. There was a first job out of banking,
and you know, but you can be looking at like
what does the rail contract look like in twenty twenty

(09:38):
four and how does that step up? And you're like, man,
does this matter to the stock. It's great training. It's
a perfect place to kind of get that.

Speaker 3 (09:44):
You know.

Speaker 4 (09:45):
It's almost like private equity like where you're just sort
of you know, looking through everything. But that's kind of
how that started, all right.

Speaker 2 (09:51):
So then at Citadel you mentioned you covered materials, commodities,
stuff like that. I guess two questions. When you come
in the door there and you're told like, okay, this
is what you do. What are you're told as your
constraints and your specific remit and then also like how
do you pick a stock?

Speaker 4 (10:10):
Yeah? Yeah, And I'll talk about this in a general sense,
not specific set it up, but to talk about multimanagers
in general, and our client base today at Arkana is
about fifty to fifty split. I would say between people
who I call like natives who come from the risk
model system either any of the major pods or related
and the other half can be like a deep value
fund that says, hey, I don't want to limit myself

(10:30):
to this stuff, but I see just like you guys
are saying, this is an increasingly important part of markets
and I want to be deep on it, educated whatever.
So to answer your question on how multimanagers kind of pickstocks,
run processes and think, not specific to any one place,
but that sort of natives group in general. So at
any of these places, the core contract is to say,

(10:51):
the core difference, I guess is the other way to
say it is versus a deep value place, it's about turnover.
In the end, it's two things. It's risk limits and
it's about how freak your book turns over. So at
a deep value fund, the goal might be in sort
of theory to have more than a year long average
hold period. In practice it'll be often shorter than that,
you know, nine months or whatever as bad idea cycloud

(11:13):
or whatever. But at a multi manager those numbers can
be anywhere from like ten to fifteen to even higher,
meaning the entire book turns over ten to fifteen times
in a year.

Speaker 3 (11:22):
Wow.

Speaker 4 (11:22):
Think about it simply like the average idea stays on
for a month is the way to put it in
the book. And so as you step into any of
these places to your point A, you have a you know,
there's a structure. There's an analyst, and then a portfolio manager.
And the analyst generally has a single industry focused so
it's like, hey, i am, as you said, chip stocks,
or you know, somebody else might have software, or it's
a sort of defined single universe. And then a portfolio

(11:45):
manager will have a set of analysts below them who
have typically very related coverage universes and will feed up
into the portfolio manager. So that's like kind of the structure.
Stockpicking kind of ends up being what we were talking
about earlier. In the end, it's the analyst job to
have a detailed model, of course, to have a view
on earnings across their coverage universe, and that coverage universe

(12:07):
for that analyst, by the way, can be and it
varies by a multi manager, but it can be anywhere
from like thirty names at the low end to like
eighty names at the high end buy analyst. So there's
a lot of process there.

Speaker 3 (12:32):
There are many differences between a retail investor and a
multi strategy fund, but one of the key ones I
think is maybe position sizing. So if you're a retail
investor and you have a single stock thesis, I don't know,
you want to buy in video or something, you buy
in video, and you're probably making that decision based on

(12:53):
how much cash you have in your Robinhood account or
something like that. But if you're at a multi fund,
it seems like a much more sophisticated process. So I
guess I'm curious if you're at a podshop, how do
you know how much to buy? How do you know
how much to allocate to a single stock. And I

(13:13):
guess another way of saying it is you're looking at
that single stock on a risk adjusted basis, right, Like,
that's what you want to get, right, the risk adjusted performance,
not just the single stock performance.

Speaker 4 (13:25):
That's right. That's right. So there are two or three
ways that gets implemented. So the first is constraints. So
step one is dollar neutrality. I'm long as many dollars
as i'm short. That's a simple limit, sort of. One
level higher is beta neutrality relative to the overall market?
Am I longer short? On a beta adjusted basis? Sort of?
The third level is factor neutrality. I'm balanced against all

(13:46):
of these sort of if you maybe simplified just slightly
the subcomponents of beta, so instead of like, hey, I
have a beta to the market, I actually have a
beta to the basket of size large companies. I have
a beta to the basket of companies with momentum. I
have a basket to the beta.

Speaker 2 (14:00):
I see.

Speaker 3 (14:01):
So you decompose beta.

Speaker 4 (14:02):
Essentially, it's a decomposition. Essentially, when people talk about factors
and factor neutrality, it's a decomposition of beta into its
constituent parts. There's a lot of statistics that goes under
the hood to make that orthogonal and precise and.

Speaker 3 (14:18):
Orthogonal.

Speaker 4 (14:19):
But at the sort of functional level, at the level
that people at the stock picking level, at multi managers
interact with the model, it's essentially just a decomposition of
beta's and then you add up those exposures on each side,
and you are limited essentially by the percent of your
bets in a book in aggregate that are betting basically
on factor type bets as compared to the percentage of

(14:43):
your bets that are betting on the remainder term, the
non factor component of any stock. So as you look
at any stock, it fits within that broader portfolio that
you're putting together.

Speaker 3 (14:53):
Okay, and then the second thing that you talked about
earlier is this idea of turnover. So just to press
on this point, how much do trading costs factor into
investment decisions? And also position sizing, because as you just stated,
you could theoretically size or arrange all of your positions

(15:15):
to be factor neutral or neutral in terms of systematic risk.
I guess, but I imagine in order to do that, you
would have to be trading pretty much like constantly right,
which would add to your execution costs. So does that
come into play as well?

Speaker 4 (15:31):
Yeah, it does. In practice, the stock picker, portfolio manager
and analyst doesn't flow in a complicated set of formulas
to their decision around sort of how do I optimize
trading costs? The engines operating at the multi managers do
think a ton about how do I take the stock
picks that a single portfolio does and then execute them

(15:53):
in a optimal way a crossing some firms doing some
prints don't cross each other's orders within the pod level
about all of that, and then so the first level
of how do people get limited is the constraints on
what percent of my bets are in factor type bets
versus non factor type bets. There are all also a
bunch of like single position limits, So that's like one
version is basically limiting the portfolio manager to have to

(16:15):
sort of live pickstocks under this constraint. The other framework
of how do you size positions to your sort of
earlier question which comes around to this trading cost question
is there are tools that are called optimizers that basically
look at the expected return that each portfolio manager thinks
they have in their book of stocks and tries to
solve for the optimal balance of the expected return against

(16:36):
the volatility of those stocks and the volatility the factor
bets in the book, and it'll spit out an answer
for you. That answer may not be exactly where you
want to land, but in the most sophisticated places, that
answer that the optimizer spitting out is including how much
trading cost impacts the book. So it's sort of flowing
that mathematically into a machine driven optimal book. But again

(16:57):
that's sort of in the more science bucket. Of course
there's art even underneath that statistics, but basically that's in
the more sort of science bucket. Then the PORTFOLI manaddressed
to say, Okay, the machine sort of took my expected returns,
took the variance of those pieces and the trading costs
into account. I gave me an answer. Does that actually
still fit with my you know, fundamental bottoms at work?
Back to hey, the contract of this company changes in
twenty twy six, the earnings going to be this. Here's

(17:18):
the positioning and set up and crowding of other pods,
you know, playing the game you mentioned earlier. So there's
then the sort of second level of art that goes
to the top of that.

Speaker 2 (17:26):
Yeah, So I want to talk more about the speed
of turnover because Okay, let's say you're like bullish on
and video and videos had this big run and you're like,
all right, but I don't want to have size exposure
because it's going to be correlated to big caps. I
don't want to have general market beta because probably if
the market goes up and video is going to go up,

(17:47):
and I don't have chip beta and all this stuff.
So what you're trying to identify is just the Invidia
specific idiosyncredit. That's exactly right, right, But why does that
inherently lend itself when you're thinking about I mean, I
feel like there must be some connection, But you're trying
to strip out all of these different factors that you
don't want to have exposure to. You're trying to find
the idiosyncratic drivers of a specific name. What is it

(18:10):
about that process that sort of inherently lends itself to
shorthold periods.

Speaker 4 (18:15):
That's a great question, and as sort of deep one,
and you might get different answers to that question from
a few different people. I'll give you mine. The essential
reality is that In order for this entire model to work,
you have to have a great deal of diversification across
idiosyncratic bets non factor bets. And the way to think

(18:36):
about that is the core reason a lot of these
models work, is that the residual return or idiosyncratic return
is approximately normally distributed across a certain window. Meaning it's
sort of, you know, like flipping a coin basically, And
the intuition is, if you flip a thousand coins, obviously
you'll center around whatever your hit rate is on that coin.

(18:56):
If the coin is loaded fifty two percent, yeah, versus
fifty As you flip three coins, it could be you know,
the mean, the expected value of that is going to
be you know, who knows, right, But as you flip
a thousand coins or ten thousand coins, you will center
around that mean. And so and that variance is effectively,
if you think about things from a return standpoint, the
sharp ratio, right is they're returned about it about the

(19:17):
variance of the volatility of that return. And so as
you have more and more bets, you shrink the variants
relative to the return you're generating. And the more and
more your bets are in idiosyncratic bets which are normally
distributed unlike market bets you know, which are you know
can be wild.

Speaker 2 (19:33):
Right, Wait, can you actually just explain that point, because
that's a great answer. You're basically you have some assumption
about returns, but there's going to be a lot of variants,
so you want to make a lot of bets. That's
exactly in order to achieve that. Why is it that
idiosyncretic returns are normally distributed and such as you described totally?

Speaker 4 (19:53):
Yeah, so what you're actually solving for is you go
down the factor model building a rabbit hole is cross sectional,
normally distributed, meaning across the universe of stocks within a
period of time. Okay, so that's kind of also what
the model solves for, and it sort of solves for
a combination effectively of what's the highest R squared meaning
how much of the model explains what's happening across stock

(20:15):
movements across different stocks in the market, and then the
output of any regression within its period is going to
produce that result of a normally distributed kind of residual term.
But the key way that this model works is that
it's normally distributed, not across time but across stocks within
a given period. And so what that means is you're
going to have you know, as many stocks that are

(20:37):
on the residual basis that are outperforming in a period
as that are underperforming on this residual basis. Whereas, of course,
if you just bet on semis right in a month,
and you just were long Semis within a period within
a month, right, that's not going to be normally distributed,
of course, Right, It's just if you're managing to a
model that is cross sectionally approximately normally distributed within a month,
Let's say you're going to get winners and losers, and

(20:58):
you're going to center around that hit rate basically.

Speaker 3 (21:01):
Okay, I get that, you keep mentioning a month. What
is like a normal or a reasonable time horizon that
these models like typically operate.

Speaker 4 (21:11):
Yeah, they're sort of calibrated to so technically the model,
the regression runs daily. Actually, but when you are building
any of these models, people calibrate them to sort of
optimize for like, the average whole period of a discretionary
stock picker is not a day obviously, and so you
try to calibrate the bias of these models to say,
and people actually you can run multiple models, say hey,

(21:32):
we're going to run one that's calibrated for a one
month horizon or a six month horizon or whatever. And
so you're trying to pick the calibration horizon that matches
the investor that we're talking about. So I mentioned a
month because a lot of the multi managers, let's say
the average hole period ends up around a month, you know,
twelve that's twelve times turns a year, but it could
be high. It could be seventeen turns, it could be
eight turns. They are managers who are in that range.

Speaker 2 (21:54):
Just to go back to the question of idea generation,
you're going to hold a stock from month, maybe maybe
a few weeks, maybe a little longer. Some analysts who's
like monitoring all this stuff, what goes into it? Someone
says to you, okay, like you're doing materials and or commodities. Yeah,
and they say, suddenly you have a bullish view on

(22:15):
exony something or some small shale player. What happened before that?
Totally that led to that idea? Well, not just that
they like the stock, but that they like the stock
in a very short period of time.

Speaker 4 (22:27):
Yeah. So, and this is not always true, but as
a sort of simplified rule of thumb. Typically the winners
are going to be on longer than that month, okay,
and you know, you realize you were wrong about something,
then you cut that. And there's trading turnover as well.
That's not pure idea turnover, if that makes sense, which
is idea generation. So that's going to be a little
slower too. But anyway, with with those caveats to your question, yeah,
so there's in an ideal world you do a you

(22:50):
sort of separate the idea generation process into two steps.

Speaker 1 (22:53):
Okay.

Speaker 4 (22:53):
The first is initiation, where you sort of learn about
the stock, if that makes sense, And in that process
you basically do all the things I mentioned that a
core value oriented fund does in terms of thinking about, Okay,
what's the long term of this, what's the secular trend
within companies, who's gaining who's losing share. In order to
do that, you do all the classic Warren Buffett stuff.
I mean, you understand, you look at industry earning earnings

(23:15):
and earned industry reports and filings and all of those
kinds of things. You talk to experts also as part
of that process. That could be any of the expert
network calls somebody who were people who were executives, and
that can inform that initiation and understanding of the industry
as well. And some people spend you know, some analysts
spend the majority of their time doing sort of initiation
type work that sort of build a deep financial model

(23:36):
that tries to build not just from like the high
level revenue but to unit economics like okay, And by
that I mean like, you know, if you're looking at
a coffee shop, like, okay, how many cups of coffee
do this? Hell, what's the price? How much is that
going to change? What are the inputs to a cup
of coffee? And just trying to get to that level
of granularity on unit economics. Yes, and so that's kind
of like the initiation process, and then ongoing coverage is

(23:57):
a little bit more of Hey, I have a view
from that initiation work on sort of long term relative
winners and losers in a space. I have an understanding
of uniit economics of each player and how each of
those is kind of heading. And then the ongoing maintenance
process is a lot to do with what data sets,
what data points, what conversations from an industry conference standpoint
or whatever can I do to understand more granularly how

(24:20):
each of those unit economics points is changing. And then
finally also like there's this question of crowding and positioning
and understanding what everybody else thinks, that sort of weighing
machine versus voting machine ben Gram classic analogy, but you
sort of separate that process, have that secular view, and
then you're trying to understand what data sets. So that
could be like all data sets, It could be industry conferences,

(24:41):
it could be talking to people in the industry through
the supply chain. It could be you know, people always
should be doing this but don't always actually in practice
doing it. But your analyst should understand if they're covering
an auto company, they should understand auto suppliers, and they
should understand the downstream of that. So each of those
sort of up and down the value chain. That's like
a big I'd say in reality, a differentiator among analysts

(25:02):
is how deep into the value chains you're seeing what's
happening to inform you about the changing trends in those
unit economics that you had a baseline view about at
the beginning of the sort of initiation you understanding the industry.

Speaker 3 (25:15):
Convince me or you don't have to convince me. You
could try to try to convince me, or you could
agree with me. I don't know, convince me that this
isn't just momentum trading with some added maths and maybe
efficiencies coming from like centralized risk management and capital management systems.

Speaker 4 (25:32):
Okay, so on the convincing part. So momentum itself is
a factor in every essentially commercial factor model, and so
you're actually therefore, because you were limited constrained on your
factor bets, you're constrained on how much like just momentum
you can be long. Ever, so you're limited in your
ability to be long, and momentum can have nuance like
do you calculate momentum over a six month window a

(25:53):
nine month window and what are the inputs to that,
But in aggregate, you're actually limited in your ability to
be long or short momentum at all. It's actually one
of the most focused on factors within commercial factor models
that everybody asks about all the time. So that's like
point one to mention on momentum. The other is the
way you described at the beginning was interesting too, because
there's a concept of factor investing where you're betting on

(26:15):
the factor, meaning you're finding cheap ways to be long
momentum or cheap ways to be long the value factor
or other pieces. And that's the kind of growing and
that ties into the whole sort of growth of passive
and all those things. What these risk models actually do
in the multi managers essentially are the elimination of factor
bet meaning it's the opposite. It's kind of a mirror
image of that, where you're sort of eliminating the factor
bets entirely and trying to find just the performance in

(26:37):
the residual. That then leads to this question of like
what factors exist inside the residual term that are not
momentum And that's where you get the concept that you
mentioned earlier, like a pods blowing up and what's positioning
and crowding and nuances there, which something we spent a
lot of time thinking about, Okay, like how do we
mathematize how do we characterize that? And what gives information
incrementally beyond? Okay, you've eliminated this sort of straightforward momentum topics.

(26:59):
You've eliminated did value What within that residual can give
you more and more insight beyond just like the core
research work can we talked about earlier.

Speaker 3 (27:22):
I'm glad you mentioned the sort of off the shelf
commercial factor models because this is something that came up
in our conversation with Gappy as well. So in order
to be factor neutral, you have to be able to
identify the factors in the first place. And my understanding
is that most of the pods will just purchase those
models from a company like yours.

Speaker 4 (27:44):
Yeah, So what people do is is kind of a
full spectrum of the way people implement a factor awareness
or factor neutrality strategy. Some will buy a single model
and sort of view that and then integrate that in
whatever way they do. And at the other end of
the spectrum, and there are funds, sort of the most
heavily infrastructured funds, it'll buy several factor models and pick

(28:05):
and choose different Hey, I think this factor is constructed appropriately.
Here this factor is less well constructed by this model,
and kind of put them together. And then there's sort
of also a spectrum in terms of people software tooling
that they how far down they hand into the organization
a sort of sophisticated tool to let portfolio managers see
what are my factor exposures. So like some places there's

(28:26):
a total separation almost of church and state of you know,
stock picking and risk management, and that is partly a function.
There could be a philosophy element to that, and there
could also just be a constraint. I mean, it takes
engineers and time and money and focus to build all
this stuff. So some places will have nothing in terms
of tooling, and they'll just have a risk team that
kind of looks at books and helps people understand their
risks on a sort of shorter cycle meaning longer cycle,

(28:49):
like it'll take oh, once a week, once a month, whatever,
they'll get a report on their risks, or they'll check
in et cetera, et cetera. And then at the far
end you have funds that have like full software platforms
that hand and to it portfolio manager like okay, if
you change this, what happens to that? If you want
to sort of see what the optimization math does for
you instantly, can you see that? And so that's kind

(29:10):
of the spectrum of what things do. And we sort
of provide that software toolkit everything from the risk model
as you mentioned, like the core underlying factors all the
way up to the software infrastructure that lets you just
play with it. Okay, if I had a billion dollars
in video, what does this do to my risk numbers,
that idio number of fact number? What is it do
to each of my factor exposures? And then how does
that change dynamically? And it'll also sort of like find

(29:32):
hedges for you, Like what single stocks would optimally hedge
this book in this way. Now, of course it's still
on you to pick stocks, but it it'll source. Okay,
I've got a whole universe of stocks. What single stocks
would offset this in video? Or these five single stocks
would offset that?

Speaker 2 (29:46):
Just to go back, and then I want to talk
more about the software and what you sell, et cetera.
But just to go back, one last question on the
idea of like actually selecting a stock. You know, you
mentioned maintenance, and the analyst really builds out a coverage
universe and then they really to know the unit economics
of the coffee shop or the company that makes you know,
something for a car or whatever. But then what do

(30:08):
they see to say and now we should buy it,
Like what would be the signal that they're looking for
in the market that say, you know, again on some
short term period. This is really I've gotten to really
know this company, but there's something about X right now
that makes it a compelling buy for a short term period.

Speaker 4 (30:25):
Totally. The core idea is that you're looking for differential insight,
meaning something that changes the perception of everybody else about
the value of this company in a long term sense.
So meaning I see if the market's perception is pick
a coffee shop is going to grow, and people will
the market it's, you know, whatever the market means. But
typically the market is who is the marginal price cetter

(30:46):
of a stock basically, and there's a perception there implicit
in the price at a minimum about okay, how many
units of coffee and what's the price of those coffee
cup's going to be, and what's the underlying cost.

Speaker 2 (30:57):
You're waiting for moments in which you believe something is
going to emerge. Yes, that will change the long term expectation.

Speaker 4 (31:06):
That will exactly, that will change the you know, And
there are other situations like tactical things where hey it's
so heavily shorted, Yeah that'll change slightly, And I'm really
looking for a short term catalyst, or hey, look everybody's
expecting this next all data print to mean something specific,
and they're all positioned on one side. That's where crowding
positioning comes into the equation. And everybody's position this way,
and I think it's going to go the other way,

(31:27):
and I've got a very tactical thing that is a
part of the equation, but a much larger part of
the equation are still catalyst driven, Like Okay, there's a
data point that comes out, but it's a data point
that indicates something about the overall perception of where this
company is headed. And so like classic ones and software
can be changes in churn direction and like where people
can get smart on that is often like okay, there's

(31:48):
an overall headline churn number, but then there's like like
if it's an internet company or something like that, or
subscriber company, and then you can go down the line
like okay, if somebody's looking at churned by region and
has some forward look on something that gives them insight
to like, okay, churin is changing in this region, and
this reading is small today, so it actually doesn't hit
the headline churn number. Yeah, but that's actually structurally growing
faster than every other region, and so the underlying churn

(32:11):
rate that looks like it's this level is going to
step up structurally because this smaller region is going to
be a bigger part of the overall path. That's the
kind of thing.

Speaker 2 (32:19):
At some point, by the way, we really need to
do another I'm sure we've done on the past a
deep episode on all data, because yeah, Walmart satellites of
Walmart parking lots and like credit cards, I've heard about it,
but it's like, I know, there's more to it, and
there's you know, it's important. You mentioned the different shops
have different software infrastructure, and the level at which it's

(32:42):
on the managers different sometimes and the different which it's
at the umbrella level, So like does that mean that,
like does it happen where the at the very high
end of risk management they look across and they say, wow,
you know, in aggregate, our portfolio managers, maybe perhaps unintentionally

(33:03):
or even within their remit, have built up a lot
of implied exposure to momentum or implied exposure to rates,
or implied exposure to value. And then what do they do,
like tap people on the shoulder and say, like, how
what happens?

Speaker 3 (33:17):
Then?

Speaker 4 (33:17):
Yeah, absolutely, so again this sort of a spectrum of
people's technology and factor awareness risk systems, but at the
sort of platonic ideal of that that you know exists
in various forms. There's sort of a CIO level, there's
a you know, COO and risk team level, there's the
PM level. There's even an analyst level that sort of
is monitoring each level of that. So like you'll put limits,

(33:38):
as we talked about on the portfolio level, right on
an aggregate risk basis, and then on an individual factor
you'll say, okay, you can have more than blank percent
of your variants in your book in a specific in
any specific factor, So put those limits individually, and then
exactly as you said, they roll it up just like
you know, you just add up the line items. Essentially,
all these models are structurally linear decomposit so they add

(34:00):
up actually linearly, So like John's momentum exposure in dollar terms,
here Jaill's exposure is there, and they add up. So
you do see aggregate level CIO level kind of hey,
we're net lung blank or whatever at that level, and
it depends how teams structure their limits and how tightly
they limit exposures at the portfolio level. But you will
see aggriate exposures, and then there are ways to take

(34:21):
like an ETF or a basket or a custom basket
that will just limit out We'll just literally hedge that basket.
And there's nuance even there, like, hey do I can
I build a basket that hedges out that exposure but
doesn't actually basically end up being short at the same
stocks I'm long underlying the book, you can see how
that can get into a whole rabbit hole of like
sort of technical behind the scenes execution detail. But at
the high level, you sort of roll up the exposures,

(34:42):
you add them up, and you say, am I long
or short one or two or three or all the factors,
and let me balance those out at an aggurate level.

Speaker 3 (34:49):
So one thing that often comes up in discussions of
risk management software that's been popularized on Wall Street, and
I'm thinking especially you hear this a lot about and Aladdin,
but this idea that if everyone's using the same risk
management software, then is there a risk that you could

(35:09):
get everyone like doing the same thing at the same time, so,
for instance, a mass deleveraging event because everyone software is
like based on a particular model and one thing happens
and the model spits out and says, everyone needs to
sell right now. Is that a risk? Is that like
a realistic risk or is it the case that all
of this off the shelf risk management software is so customizable,

(35:33):
I guess, and there's still that discretionary factor for the
PMS that you don't really get that hurting behavior.

Speaker 4 (35:40):
M So I'd say yes and no. I think in
the no camp, the fact is that you're kind of
eliminating those sources of exposure that are common. So you're
kind of trying to focus people on residual bets. And
you know, for example, that could be oversimplifying, but that
could be long and short pepsi, or long pepsi and

(36:01):
short coke, and that would equivalently neutralized factors. Let's assume
they're kind of proxies for each other. And so kind
of what the model lets you do is kind of,
instead of having to be perfect pairs in the Alfred
Sloan original hedge fund concept, where you have to in
order to be factor neutral, you just have to find
perfect comps, it kind of lets you pick non perfect
comps but end up in a risk place that is

(36:22):
similar to that where your only bet is on a
single stock. But anyway, so like what the model is
pushing you to is not any specific stock, right, It's
telling you to pick which one of the stocks that
don't have comparable factor exposures is more attractive. So that's
one level. The second is there is leverage, and the
leverage you're putting on is not leverage against beta. That's
the distinction that I think people often allide is that

(36:45):
when you think of like LTCM or maybe forgetting even LTCM,
but any fund that takes very high leverage on a
beta a directional bet that's beta on a factor, and
the issue with that is many issues with that. If
you're taking lots of leverage on a beta is there's
just sort of that risk that it has a big
draw down. The hope, I guess, or the sort of
mathematical reality as you kind of pointed out that's actually

(37:07):
been executed on is that when you're levering alpha, it's
again it's sort of the quant fund world works this
way too, is that what you're levering is just that
residual term you're getting back to that coin flipping and
you're finding a source of return that is normally distributed
across stocks, and therefore if there is a big blow
up in markets, actually typically the factors become more and

(37:30):
more statistically significant, and so if you're neutral against those factors,
the residual return remains a cross sexually normally distributed. So
there's obviously a lot of detail under the hood, but
the basic answer is that you're trying to find a
type of return, and a diversified source type of return
that doesn't have that risk in a blow up, So
you're kind of levering alpha. That's the key kind of
point versus beta. And the final yes answer to your

(37:52):
question is that you are still levered. So notwithstanding everything
you can do to sort of solve the mathematic piece
of this equation, you still have some risk that the
person providing you the leverage has a business problem or
somebody who like whoever is providing that leverage to you,
which is typically the banks. Basically that person for whatever
reason needs to pull that leverage or whatever, and that

(38:14):
it's almost a little bit even in the category of
business risk that exists intrinsically with leverage. So that's kind
of the the yes portion of the answer.

Speaker 2 (38:22):
I'd say this type of software, these models. They exist,
they've existed for a while.

Speaker 4 (38:27):
That's right.

Speaker 2 (38:28):
When you started your company, are kinda what was the
theory that there was need for more?

Speaker 4 (38:35):
Yeah, and it's what you mentioned at the beginning too,
which is the sort of the theoretical beauty of these
models and how it all works, and the normally distributed
residuals and the sort of diversification of alpha and the
levering of alpha. But what really has happened over you know,
decades now is that that model has been proven to
be at least have something. It may not be the
only model that's viable to make attractive returns for investors,

(38:57):
but at least that sort of you know, result has
jumped from the sort of academic theory to realized practice.

Speaker 2 (39:04):
And yeah, and it also explains why we're seeing some
pretty big launches in.

Speaker 4 (39:08):
This absolutely absolutely, Yeah, it certainly has jumped that gulf.
And you know, look, in the quantitative world, it made
that jump long ago. It's in the fundamental stock picking
world that it made that and again, a few firms
had been doing it for a long time, but it
sort of made the most convincing leap over the last
whatever five to ten years, where it just sort of
decisively generated very attractive risk adjusted returns for investors and

(39:31):
kind of proved that sort of synthesis, which is really
what's happening between the sort of quant view of the
world of factorization and finding idiosyncratic or residual performance within
inside what's left over after the factors synthesized that risk
and sort of quant perspective with that Warren Buffett's style
fundamental type research and analysis and work, that synthesis was

(39:52):
implemented by a few firms and now it's sort of
proven itself to work in a lot of different ways.
So that's what's happening. So from our angle, like what
we have seen is just that wide range as I
kind of mentioned earlier of execution of that, like how
easy is it to actually have a system in place
for a portfolio manager or analyst or cio. How sort
of not only user friendly but sort of functional. How

(40:13):
efficiently can it source new hedge ideas that balance out
a specific factor exposure? How efficiently does it connect that
risk perspective of where I'm long and short? To a
topic you mentioned earlier, performance attribution, like where are my
generating returns? What are my hit rates, What are my
hit rates on residual versus on factor? What are my
hit rates on earning season versus outside of earning season

(40:34):
and on a residual basis, And how does that connect
to my risk and my portfolio construction? And all of
that is a lot of work, you know, it's a
lot of painful kind of putting together the software and
the risk and all the different elements together. And as
I mentioned, what we see is some funds have done this,
you know, at a level that is really excellent, and
some funds, most funds, because they have to do the
very hard work of stock picking. It's a very challenging job.

(40:55):
You have to have incredible IQ allocated to that problem
and effort. They have, Okay, systems of a sheet that
gives some risk insight, but it doesn't have the sort
of detailed input output experience of let me tweak this,
let me see what happens there, let me understand how
it connects. And so putting all those elements together is
what we kind of hope to do.

Speaker 3 (41:11):
When did you actually found our.

Speaker 4 (41:13):
Camera a little over two years ago?

Speaker 2 (41:14):
Two years ago?

Speaker 3 (41:15):
Okay, so what's the difference between what clients ask for now?
Versus what they were asking for two years ago. Because
this is a rapidly evolving space.

Speaker 4 (41:26):
You know. I think in that sort of split that
I mentioned of our client base that is kind of
native to that risk world and the group that is
sort of newer to it, I think the native group
has this constant sort of question set of how do
I make this again more functional? See more analysis more quickly,
how everything relates to each piece? Can I see insights
on crowding and how that relates to my book? And

(41:47):
can I see all those different pieces. So that's kind
of like a steady escalation in thoughtfulness. I would say,
as you hand the portfolio manager tools on this factor,
and you also sort of empower them because again and
a lot of these organizations are set up where there's
a risk side and a portfolio management function, and you know,
the portfolio manager isn't necessarily the client of the risk

(42:07):
in house at these places. But as there becomes this
industry of people like us who are providing these tools,
in a way we have to be a little more
responsive to the portfolio manager says Okay, I see how
that was built. Can I double click?

Speaker 2 (42:19):
I mean the portfolio manager isn't necessarily a client.

Speaker 4 (42:22):
So at a big multi manager, you have a risk
division which kind of sits under the CIO almost, and
you have the portfolio managers, and the portfolio managers aren't
the client of the risk people. The risk people kind
of work, if you want to put it that way,
for the CIO, who says, you know, sort of it's
kind of a limitter in some rice. It's kind of
a constraint in a lot of cases. And the best
places are doing it where it's completely synergistic, where you're

(42:43):
using the risk tools and this factor awareness and all
of the things you can do with that on offense,
not just defense. So that is happening at a few places,
but there's a whole other group of places where it's
kind of, hey, this limits me, This isn't working with
or for me. And so as this becomes a little
bit more, you know of an industry or like, we
do work for them, right, so we're hey, I want
this additional feature that you know they're the client. Right.

(43:06):
So there's that piece, but there's sort of this constantly
escalating sort of demand for tooling and incremental insight and okay,
let me click this, let me understand this across my
whole universe, across the entire universe stocks, I could cover
all those kinds of tools on the sort of the
people who don't come necessarily out of the POD systems.
The interesting thing is the extent to which people want
to focus on. Okay, let me, how do I frame

(43:27):
that system that factor awareness instead of in a market
neutral context. But hey, I'm a long only or I'm
a sort of directionally oriented value fund or whatever. How
do I reorient the model shift it to be sort
of true comparative to my benchmark? And so that's been
an interesting evolution is the types of investors who are
not structurally market neutral but still want all the insights

(43:48):
from this where you can recalibrate the entire model against
a benchmark. And so that's been one example.

Speaker 2 (43:55):
Rich Folk Wallace, thank you so much for coming on
od lotch. That was a fantastic learned and now have
like ten ideas for further episodes we have to do,
which is always, as we say, the test of whether
we had a.

Speaker 4 (44:07):
Good conversation or absolutely absolutely Thanks so much for having me.

Speaker 2 (44:09):
Really appreciate it, Tracy, I thought that was great. I
really do have like there's like ten more episodes that
we have to do now. But that was very illuminating

(44:30):
on multiple levels, particularly about like what the job of
the PM or the analyst actually is in these contexts. Yeah.

Speaker 3 (44:39):
Absolutely, And also I was thinking it kind of dovetailed,
interestingly enough with the conversation we had recently about thematic investing, Yes,
James Fankiland, where he was talking about like, okay, price
is obviously a factor, but also you kind of want
to identify the story that everyone's going to latch onto.
And then Rich was talking about how when you're coming

(45:01):
up with investment ideas, you're sort of trying to identify
something that will change everyone's perception of the trajectory of
a particular stock or investment totally.

Speaker 2 (45:10):
And I thought it was just like really interesting this
idea that like, okay, like no one knows what's going
to happen tomorrow, some major event could take place that
causes the you know, the whole market to crash. I
guess big events don't usually happen to cause the whole
market to surge, Unfortunately, it's always the other way around,
Like nobody knows what interest rates are going to do,

(45:31):
and we know you know, a lot of stocks are
tied to interest rates, and no one knows maybe some
chip company will come out tomorrow that beats and video whatever.
No one knows any of that stuff. And then this
idea that if you can then strip out all of
this and then identify the idiosyncratic drivers of a stock,
and then those idiosyncratic drivers of stock almost inherently, some

(45:53):
will be winners and some will be losers. Yeah, I
could see why. Then the game is lots of bets
over relatively short time periods. Like that that like really
clicked to me in this conversation.

Speaker 3 (46:06):
Yes, that's the other thing that stood out to me,
like the idea of diversification across those different bets. Like, yeah,
I hadn't really, I guess, like when you think about
hedge funds still, even though multi strategy funds are sort
of where it's at SAE, Yeah, I still think about
like that classic I don't know, Bill Ackman type thing

(46:27):
where you make one big bet on something and that's
your source of alpha. But again, the thing that's coming
through in this conversation is really like the diversification aspect,
the desire to be factor neutral and to lever the
alpha instead of the beta.

Speaker 2 (46:44):
All kinds of interesting stuff there. I want to do
more on what do I do more? I well, I
definitely want to do more on al data because I
feel like usually when that gets discussed, it's like this
like very like sort of tired cliche ways, like I
know everyone has a credit card day, but I want
to understand more about that. I don't know, there's a
lot more that we can also, just like the different models,

(47:08):
Like I'm sort of fascinated that, like there's all of
these different multistrad funds that exist, and the fact that
they're not all the same is interesting to me. And
the fact that like where the risk manager sits in
the amount of tools and what they build in house
and what they don't, and the degree of flexibility that
pods get and you know, what analysts actually do and

(47:28):
stuff like that. There's much more to do.

Speaker 3 (47:30):
I really want to do an episode on differences in
compensation models.

Speaker 2 (47:34):
Oh yeah, at the pod shops.

Speaker 3 (47:35):
I think that would be really interesting because that would
also feed into I assume investor behavior. Yeah all right, Well,
now that we've come out of that with like ideas
for ten more episodes, shall we leave it there.

Speaker 2 (47:47):
Let's leave it there.

Speaker 3 (47:48):
This has been another episode of the All Thoughts podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.

Speaker 2 (47:54):
And I'm Joe Wisenthal. You can follow me at the Stalwart.
Follow our guest rich FULK Wallace. He's rich Folk Wallace.
Follow our producers Carmen Rodriguez at Carman Arman, Dashel Bennett
at dashbot in Kilbrooks at Kilbrooks. And thank you to
our producer Moses One. For more odd Lots content, go
to Bloomberg dot com slash odd Lots, where we have transcripts,

(48:15):
a blog and a newsletter. And if you want to
talk about all of these topics, including investing and markets,
you can do that twenty four to seven in the
odd Lots Discord Discord dot gg slash odd lots.

Speaker 3 (48:28):
And if you enjoy Adlots, if you like our ongoing
attempt to understand multi strategy funds, then please leave us
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if you are a Bloomberg subscriber, you can listen to
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(48:49):
in order to do that, just find the Bloomberg channel
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ind