The Math That Explains How Multi-Strategy Hedge Funds Make Money

Multi-strategy hedge funds are still all the rage on Wall Street, but what does it actually mean to be a pod shop and how are they being set up? On this episode, we speak with Dan Morillo, co-founder of Freestone Grove Partners and formerly a partner and head of equity quantitative research at Citadel (one of the most successful multi-strats out there.) While lots of people tend to talk about multi-strategy hedge funds as one big blob, he argues that there are important differences in their business models. We talk about how he identifies top portfolio managers, managing crowding risk, and the math behind compensation, scale and returns.

Previously:
How Hedge Funds Discover the Next Superstar Trader 
How to Succeed at Multi-Strategy Hedge Funds

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Episode Transcript

Speaker 1 (00:03):
Bloomberg Audio Studios, Podcasts, Radio News.

Speaker 2 (00:20):
Hello and welcome to another episode of the All Thoughts Podcast.
I'm Tracy Alloway.

Speaker 3 (00:24):
And I'm Joe Wysenthal.

Speaker 2 (00:26):
Joe, we're back on the multi strat beat.

Speaker 3 (00:28):
I love this beat. I think it's really interesting. There's
a lot we've learned, but there's a lot we haven't learned.
I love this beat. If you said we're going to
just do ten episodes about this, I'd be like, yeah,
that's fine.

Speaker 2 (00:39):
Well, yeah, I look forward to part six hundred and
seventy eight in our ongoing attempt to understand multi strat
hedge funds. But you know, we've been sort of learning
as we go along, and there are a bunch of
questions that I still have. One of them is there
seem to be a lot of different opinions and variation

(01:00):
pod shops right on how exactly they can be designed.

Speaker 3 (01:04):
Right, So there's different sort of structures that I understand.
There's different compensation structures. There's different degrees to which the
different pods so to speak, coordinate with each other. There's
different degrees to which they like centralize ideas and research.
So like I get that, there's still some big questions
in my mind, and I'll just say one of the

(01:25):
big ones right off the bat, which is, if you
have a bunch of teams doing a bunch of different
strategies and trading a bunch of things, why are the
returns good instead of average? Because in my intuition, if
you have a bunch of teams like, okay, you're diversifying
alpha across a bunch of things, but great, But then
you have a bunch, my gut intuition will be like,

(01:48):
you don't get great returns, you get average returns, right,
And yet many of them put up really impressive returns
year after year after year, And I don't think I
totally have a grasp of life.

Speaker 2 (01:57):
Well, yes, and this is a question that I have,
which is eventually the pod shop. Some of them are
getting very very big, right, and so if you have
one thousand pods working under your roof, that's a bit extreme.
But at some point aren't you just sort of replicating
the market and that alpha opportunity as you just described
kind of goes away. Well, on that note, I am

(02:19):
happy to say we have the perfect guests to discuss
all of this. So these sort of variations behind multistrap
funds and also the math that actually powers it. We're
going to be speaking with Dan Morillo. He is the
co founder of Freestone Grove Partners and also ex Citadel,
so again, the perfect person to be speaking to.

Speaker 4 (02:39):
Dan.

Speaker 2 (02:40):
Welcome to the show.

Speaker 5 (02:41):
Thank you, thank you for having me.

Speaker 2 (02:43):
I guess my first question is why are we talking
to you?

Speaker 3 (02:45):
Yeah, why are we talking?

Speaker 5 (02:47):
Well, you're probably in a better position to answer than me,
but I guess I'll tell you my background and hopefully
that helps a little bit. So I've been about twenty
five years now dating myself in the by side, on
the hedge fund by side in particular, and I grew
up on the quantitative side of the world. I thought
I was going to be a professor, and then I
realized that life is more exciting on the industry side
of things. And I've done a wide range of roles

(03:10):
in the quote quant side of the world, so everything
from you know, at some point I was the lead
of the global long short business at Parkley's Global Investors
before Black Crok required them. At Blackrok, I stuck around
for a bit. I at some point ran the research
group for I Shares. I also was one of the
founders of the model Solutions business there as you said,
I was etc.

Speaker 4 (03:28):
Where I had.

Speaker 5 (03:29):
Responsibility for the Equity Quantitariy Research Group that did a
lot of the stuff that you guys have talked about,
risk model stuff and the hedging stuff and all of
these sort of things. I also had responsibility for the
Center Book where a lot of that central stuff that
you also have talked about happens, and then most recently
have founded co founded that also does the pod long
short thing. So I'd like to think I have some expertise,

(03:51):
but I guess you'd tell me after you ask me
all these questions.

Speaker 3 (03:53):
I have a really rudimentary question, what does the word
quant mean in finance?

Speaker 5 (03:58):
Actually? So this is a good point, right, I think
it can mean lots of things. From my point of view.
The thing that has always been attracted to me about
the quant side of things is the idea that you
can be disciplined in how you make decisions.

Speaker 4 (04:10):
Right.

Speaker 5 (04:11):
You can be quantitative in the purely mathematical sense, like
you brand some code and there's lots of numbers, while
still not actually applying that much judgment. You can also
actually be quite disciplined and systematic without using a lot
of quant tools. Right. I think the right way of
doing quant is where you also mix these two together, right,
when you have the ability to bring in the judgment
that comes from understanding what the humans in the market

(04:32):
are doing, but to do certa in a way that
is repeatable and disciplined, and that tends to require quantitative
modeling tools, whether that's risk models, focusing, evaluation, attribution, all
of these sorts of things, right, And in fact, that's
the sort of thing that attracted me. That is sort
of a I guess a common threat through all of
these jobs that I mentioned that I've had is the
idea that you can do this this sort of systematic

(04:54):
modeling work not just with the numbers themselves, but also
with the humans that participate in the market. Are also
subject to analysis, right, whether you think about sentiment measurement
or the sort of questions you guys have asked in
this podcast, right, what is the right way to organize
a team? You know, how many teams should you have,
how should you pay them? What fees should you charge
with those? These are all subject to analysis, right. So

(05:15):
I like the idea that you can do the quant
thing on human behavior, right.

Speaker 2 (05:20):
Oh, this is exactly what I wanted to ask you about. Actually.
So if you go to Freestone's website, you can see
that there are two partners on the front page, and
you are the quantitative one, and you do have a
large number of quant researchers. What's the value added by
those quants to a fundamental equities fund?

Speaker 5 (05:40):
Yeah? I think the way you want to think about
it is that the insight that is associated with understanding
the mechanics of a firm, which is the fundamental in
this case for equities. You know, the job of the
PM analyst is to understand what drives revenue, earnings, margins.

Speaker 4 (05:55):
Et cetera.

Speaker 5 (05:56):
And in portucur what is likely to be surprising about
those next time they know earnings or over the next
couple of quarters. Right, the way you make money is
you have a view that is different from that of
the market and people come to agree with you. Right,
that's sort of success.

Speaker 4 (06:09):
Right.

Speaker 5 (06:10):
And in that effort, whether it's modeling the firms, whether
it's understanding what about that surprise is really surprising about
the firm versus something that's happening in the broader market.
The data that comes into all of this, right, alternative
data stuff. All of that requires a huge amount of
investment on the technology side, the analytics side, the forecasting side. Right.
It's no longer the case that you can be a
smart guy reading tank q's in ten case, as might

(06:31):
have been the case twenty five years ago, and just
kind of see what the surprise is going to be.
It requires a significant investment in being the most sophisticated
person at doing that job. And that's not a thing
you can do without all of that investment on the
quantitative tooling.

Speaker 4 (06:45):
Right.

Speaker 5 (06:45):
There's also all the behavioral stuff. Right. Humans have the
ability to really get into the detail of what the
firm is doing. Right. Many of the people who are
very good at there's are people who have been covering
the same firm literally for a decade. Right. They know
their CFO or the CEO, the product. You know, they've
visited the factories, and so they have this ability to
preak up on really subtle patterns. But they're also human, right,

(07:07):
and humans come with biases. Right. You project your own
patterns of sort of your view of the world into
what's happening on the ground, and so it is also
helpful to think about how do you become as disciplined
as possible in that process, right, So you think about
risk models, attribution questions, how can you tell luck versus skill? Right?
Most humans, if you do well, you tend to think
it's all about you. And if you do poorly, well,

(07:28):
there wasn't my fault, my fault, right, And so these
processes of how do you make sure that humans are
as discipline as possible again requires huge investment in that
quantitative analytical capability. So that's kind of what you bring
to the table.

Speaker 3 (07:41):
Right, So we'll get into how you go about measuring
the skill of your portfolio managers and breaking all these
things down, and we'll talk about that a lot. When
you founded Freestone Growth, you and your co founder Todd Barker,
you must think there's an opportunity there, right, You must
think there's like some opportunity out there to make money,

(08:02):
to have a fund that's different than something that already
exists on the market, that you bring something to the
table that you could structure a company in some way
that's advantageous. What is the sort of theory or thesis
behind Freestone Growth such that you wanted to build something new?

Speaker 5 (08:19):
Yeah, so you're correct, We do think we can compete
at the highest level of the industry, right, Otherwise we
would have started this time. The way in which we
think we can do this isn't some new magic thing, right, like, oh,
only we can do X, Y and Z.

Speaker 4 (08:31):
Right.

Speaker 5 (08:32):
A lot of how we and this is what we
tell our clients is that in having spent all this
time looking at what works and what doesn't in the
space I call it the multi strategy or MULTIPM space,
we have a view that you can sort of be
optimal around key business decisions, right, the number of analysts
and pms you have in your platform, the way you

(08:53):
organize them, the way you think about the incentives or
how they're compensated, the right mix of counnitata versus fundamental
in a way that sort of is the best of
what we've seen around. Right. So it's not so much oh,
there's this one thing that is massively different about us,
and instead lots of little things that we think you
can optimize in a way that many of the other
platforms for various reasons, haven't gotten to, particularly with the

(09:16):
advent of a lot of new ones. Right, where you
end up with business design that we happen to think
is not nearly as optimal as it could be. Right,
So it's sort of optimized the business as sort of
the pitch and then run each piece the best you can.
Does that make sense?

Speaker 3 (09:29):
Yeah?

Speaker 2 (09:29):
Well, on this note, so there's something that kept coming
up when we were preparing for this podcast. But people
keep talking about Dan's math. Can you put your professorial
hat on and explain to us what exactly is Dan's
math and how does it come into play when it
comes to designing and optimizing the size of your firm.

Speaker 4 (09:50):
Yeah?

Speaker 5 (09:51):
So, first, in my defense, I did not come up
with that. I believe it was actually somebody from BOOMEERG
that came up with that after some interview that they
did with us early on. But yeah, question. Look, the
point is that many of the things that you think about,
which range from how many people should you having a platform,
what sort of risk models should you run, what risks
should you take, how should you do capital location, these
are things that are subject to systematic analysis, right, and

(10:14):
so this idea of quote de math is that many
of these decisions you don't have to wave your hands around, right,
there's sort of reasonably clear answers about them, right, there's
a couple of ones that and we can chase down
whichever ones as you like. But one of the ones
that in my mind is the most important is there's
been this sort of press in the industry with this
idea that more is always better, Right. You want to

(10:35):
have more porfan managers, more as more assets like that
that scales a sort of underlying strength.

Speaker 4 (10:41):
Right.

Speaker 5 (10:42):
It actually goes back to your question around how come
do you get good results out of lots of people? Right?
And the answer is to listener, is actually not wrong.

Speaker 1 (10:50):
Right.

Speaker 5 (10:50):
There comes a point where adding more people actually doesn't
make any difference. Right, And so if you just allow
me two minutes to set up a little example. Right. So,
the way this business works is you're hiring individual risk
takers let's call them analysts. Right, So there's some potential
pool of people you can hire, and assuming you have
good hiring practices, you expect to hire people who have

(11:10):
some mean performance. Think of that as a sharp ratio.
Let's say that sharp ratio is point seventy five, right,
So have shot ratio point seventy five means that if
you take a risk of a dollar of risk, you
expect to januarate seventy five cents of per that amount
of risk that you deployed, right. And so you want
to think of performance in sharp ratio space, right, because
in different spaces people have different risk. Right. There's you know,

(11:33):
biotech names are riskier than say, bank names, and so
you want to adjust for that. So typically you want
to think in sharp ratio space. So you hire folks
you expect to have some mean distribution, some mean outcome. Right.
So I hire a person. I don't know what their
sharp ratio is going to be. I hope it's good.
And on avers I get people who are let's say
point seventy five right. Some people are going to be
better than that. Some people are going to be worse

(11:54):
than that. Maybe I end up needing to hire them, right,
But I get some distribution of them, right, and then
you give them capital and they run a couple over
the time. Right. And so the magic of the versification
is that you get a higher sharp ratio as you
add people. Right. If the correlation was exactly zero, then
the more people you add, essentially, the more your sharp
ratio increases. It increases where there's square root of end. Essentially,

(12:17):
if there's correlation. However, there's like a maximum limit of
how much your aggregator sharp ratio can be. Right. So
let's take a simple example. Let's say these point seventy
five people that you have on average, Let's say they're
correlated by ten percent, which most people will tell you
that sounds kind of low, not a lot of correlation.
Then there's a maximum limit of what your starbration can
be about two point three even if you have an

(12:38):
infinite number of people. So you're intuition that if you
add lots and lots of people, you add some gate
to some quote average return is correct, it's just what
is the scale of that average return, right, And so
if you add lots and lots of people, you get
to that sort of maximum level. And the thing that
really matters is the correlation, right, So it is incredibly
hard to get zero correlation like that just doesn't really happen.

Speaker 3 (13:00):
So, just to be clear what we're talking about when
you say correlation, you hire one PM and they trade semiconductors.
You hire another PM and they trade interest rates, or
maybe they trade banks or something like that. Yeah, but
because things in the market are generally correlated, you could
have these different people all around the world, and implicitly,

(13:22):
even though it looks like they have their own focus
on the market, they might all implicitly be making money
based on their read of the FED or something like that,
and thus their returns are correlated. And therefore, even if
they're really all really good at their jobs, that caps
the amount of firm wide sharp by virtue of the
fact that they're not really adding diversification.

Speaker 5 (13:42):
That is exactly correct. So, and it's as simple as
if you were to observe somebody's return literally every day, right,
and we observe the other persons return every day. You
can just computer correlation, put it in Excel computer correlation.
And if that number is low, you get more juice
out of adding more people. If that almost is hi,
you get less juice. To point, it matters enormously. So
in that example, that maximum is about two point four.

(14:05):
If your mean person is point seventy five, like with
an infinite number right at correlation of ten percent, Let's
say your correlation is actually twenty percent, right, you know,
it's obviously more, but it's still low in the grand
scheme of things, then that maximum number is only one
point six, right, So a little bit of correlation has
an enormous impact on how much you can deliver in
the end. Right, And more importantly, you get pretty close

(14:28):
to that maximum without a lot of people.

Speaker 4 (14:30):
Right.

Speaker 5 (14:30):
So, in the example of point seventy five, in a
correlation of ten percent, if I have forty five risk takers,
think of them as analysts. Let's say I put them
in teams of three. Right, PM team made out of
three risk takers. You know, there's not that many teams, right,
fifteen teams. That gives me about ninety five percent of
that ultimate maximum. Right, So I don't need to have

(14:51):
one hundred teams to get to my maximum. In fact,
there comes a point where it is actually more important.
Let's say you have a million dollars actually to spend on.
Something could be I hire another person, but something could
also be, Hey, I might produce a better piece of
software to help me manage that correlation. To teach people
to think about whatever their return is really independent of,

(15:12):
you know, for example, interest rates. As you highlighted them,
that actually might be a significantly better investment than adding
a team, because if I reduce my correlation by a
little bit, that actually gives me more juice than just
adding people. Right, And to look back to that original question,
what do we think might be different in terms of
how you set up your business. Is again that a
lot of people have gone from scale for scale, even

(15:32):
though you don't have to, at least not for performance reasons. Right.
There comes a point where you just kind of have
the right scale and you're better invest better off investing
in other things.

Speaker 4 (15:40):
Right.

Speaker 5 (15:41):
The reason people have gone for scale is because they
want to run more money. It's not because that gives
you more performance, right, at least a past a certain amount. Right.
And in fact, if you think about scale, scale comes
with lots of other issues. It comes with complexity. You
maybe end up with more management layers, You have to
worry a lot more about you know, offices and coordination
and you know management, etcetera. You might actually end up

(16:01):
reducing your performance. That that complexity costs money. Right, And
so one of the key things that we say to
our clients, just as an example, is we look to
cap our size so that we can run the right
number of people at the minimum complexity if possible, while
still delivering pretty much sad level of performance.

Speaker 4 (16:17):
Right.

Speaker 2 (16:33):
Why do hedge funds promise uncorrelated returns at all? Because
it feels to me, as you just said, it's very
hard to get correlation down to zero. But the pitch
to investors is always, here are a bunch of uncorrelated
returns that we can do over and over again. And
then what you see repeatedly is that when there is

(16:53):
a big event in the market, they all have drawdowns
at the same time. So why do they keep pitching
on correlated returns and why do you investors keep putting
money in them?

Speaker 5 (17:03):
Okay, so there seems to be two questions then there,
which is how come are they correlated even though they
claim not to be a number? One? And two is
that why is that even a thing in the first place? Right,
So let me start with the second one. The reality
is most correlation is driven by some common effect.

Speaker 4 (17:17):
Right.

Speaker 5 (17:18):
You know you've had guests here talking about risk models
where you think about sort of common factors, right. And
a key reason why if you're an allocator, say you're
a pension fund, you know, in university endowment, is that
you get paid for taking risk. Right. A lot of
the allocation is into things that are risky, and you
expect to get paid for taking that risk.

Speaker 1 (17:36):
Right.

Speaker 5 (17:36):
That's sort of in a sense, that's the function of
a big endowment or a big punch of fund. Right.
The thing is, most of the risks that pay you
those returns, whether that's you know, market as a whole,
whether it's you know, individual factors like momentum that you
can buy separately, you know interest rate risk, you know
inflation risk. All of these things you can allocate to
those for like essentially like a tenth of a cent

(17:57):
on the dollar, right. And so if you're going to
make an allocation to something else, you don't want that
allocation to be the same thing you already have at
essentially no fees.

Speaker 4 (18:07):
Right.

Speaker 5 (18:08):
So let's say you have a hedgehund who charges you,
I don't know two and twenty, but that hedge fund
has you know, typically a beta of like say fifty
percent on average. Right. Then half of the money you're
giving that hedge run is beta that you could buy
for essentially no fees.

Speaker 4 (18:24):
Right.

Speaker 5 (18:24):
And so the advantage of a hedgehund that is able
to in fact deliver on coliter rator risk is that
now you can make cleaner allocation.

Speaker 1 (18:31):
Right.

Speaker 5 (18:31):
You can say, Okay, this is my market risk, this
is my interest rate risk, this is my you know,
I don't know housing premium, whatever it is. However, you've
sort of decided to do your allocation, and then there's
a piece that boosts my returns because it is not
correlated to those other things, right, and so it is
the right objective if you will right, if you're an
allocator right. Then the question is whether people can actually
execute and delivering that you know that outcome right, which

(18:53):
is a somewhat separate question.

Speaker 3 (18:55):
I want to get into how you hire people at
Freestone Growth and why a talented PM would come to
Freestone Growth from somewhere else in the conversation, et cetera.
But before we get to that, I have to imagine
there's certain like information asymmetry challenges. You probably have a
limited visibility into not just a PM's returns, but exactly

(19:18):
how they achieved those returns, whether they achieve those returns
in a way that demonstrates their ability to actually extract
alpha rather than ride the various betas that you're trying
to extract out of them. I assume, if you're starting
a fund, do you think you're good at identifying the
people who will come to work for you? What information

(19:38):
do you have to use and when you're accumulating pms
or analysts, what is the basic process for identifying skill
before they show up on your door.

Speaker 5 (19:48):
That's a really good question, and obviously it's it's partly
a systematic process. But you know, like with like with
hiring for everything, it's a bit of an art too, right,
whether you're hiring a portfin manager or you know, quantitative research,
there's there's always a bit of an art associated.

Speaker 4 (20:00):
With it, right.

Speaker 5 (20:01):
The I think the key objective that you should have
is do you understand via what mechanism do they deliver
this skill that they claim to deliver it?

Speaker 4 (20:08):
Right?

Speaker 5 (20:09):
And so it's a good thing that you typically can't see,
you know, a good tracker over returns, because then you'd
be tended to based it on past returns, which is
not a good idea. If it's a bad idea, we
can talk about that separately. It forces us to think about, Okay,
if you claim that you can generate good returns via
what mechanism do you do that? Right? For a typical analyst,
at least inequities, it tends to be some form of

(20:30):
I understand what the surprises and fundamentals are going to be, right?
I can tell that this firm is going to, you know,
announce a billion dollars worth of revenue, whereas everybody else
is expecting is going to be nine hundred or whatever. Right,
And if that's a claim which tends to be the
common claim, right almost by definition in that job, you
can then sort of back into what sort of process
leads you there? Right, what sort of modeling capability you

(20:54):
could do?

Speaker 4 (20:54):
Right?

Speaker 3 (20:54):
Does this sort of get to what you were saying
in the beginning when I ask you, like, what is
the definition of quant Where it's not an enough to
just be able to math that out. There has to
be some ability to like have the human intuition understand
how these.

Speaker 5 (21:06):
Things are correct. Right, So just to use these examples, right,
Let's say you tell me I'm having an interview, I'm
interviewing you for an analyst, and you tell me I'm
great at knowing what the fundamentals are going to be, right,
And I say, okay, well, do you have a track
record of your own estimates? Right? So presumably for having
many names you covered, you knew you had an estimate
in your head about what their revenue is going to be,

(21:26):
what the margins are going to be what their earners
are going to be. I could ask you, okay, what
were those estimates back in time three days before the
company announced their know the results for all the names
are covered back many years, right, And to be clear,
I'm not necessarily looking for you to have them and
give them to me. But what processes did you used
to think about even understanding whether you have skill in
the first place? Right? And it is not uncommon to

(21:48):
have folks answer that question by saying, well, I don't
really know, because I keep my model saying Excel, right,
And I have a very complicated Excel model with all
the income say madlines and all the balance sheet lines
and all these things. And as the firm evolves, I
changed that model, right, I change the numbers, I change
my assumptions. I maybe even add in supract lines. You
add more complexity in the model. And keeping track of

(22:08):
what it was at every point in time is horse right.
You and I have to save the file every day,
and you have some database to figure out what it
was every day and change them and do some analysis.

Speaker 1 (22:16):
Right.

Speaker 5 (22:17):
And you want to talk to the people who understand
that that's the thing they should be doing, and have
made some effort to move in that direction, right, Meaning
there's an interest in being disciplined and understanding your own skill, right.
Just that is an auto significant difference between somebody who
just does it for so somebody who's interested in understanding
how they do it and how they improve. Right.

Speaker 2 (22:36):
So, on the flip side of identifying good portfolio managers,
how do good portfolio managers or why do good portfolio
managers want to come work for you? Because my impression
is there are giants in the multi strat world. You
used to work for one of them. They can pay
millions to a talented PM that they really want. How

(23:00):
do you compete with that kind of package? Is it autonomy?
Is it the culture of the firm? What is the
attraction for good traders?

Speaker 5 (23:09):
Yeah? So it's a mix of things. Let me give
you sort of what I think are the key things
that might make you want to talk to us, right
as opposed to stay at your big job, you know,
at one of the sort of big name platforms.

Speaker 4 (23:20):
Right.

Speaker 5 (23:21):
So number one, because of this drive to scale, what
has sended to happen at many of the platforms is
that if you are, say a tech portfilmer manager, you're
one of ten, potentially fifteen. Right, and remember you're competing
for your ability to have the resources necessary to do
that job really well. Right, So rundown the sort of

(23:41):
thing you need, right, You need corporate access. Right, So
you would like to have the ability to talk to CFO, CEO,
you know, even IR for the companies you cover, you know,
go to the conferences, do the non deal roadtro and
it doesn't matter how big you are. At some point,
the CFO of some firm is not going to talk
to a million managers, right, so they're going to say
to the big names, Okay, I'll give you two slots.

(24:04):
They're not going to give you fifteen slots just because
you have fifteen pms. In fact, they really don't want
to talk to you, right, Most companies don't prefer not
to talk to the investors. And so you end up
in a situation where you're competing for corporate access, you're
also competing for data science resources, quantitative resources, PORTOFOIO, construction
and risk management resources. Meaning as that scale happens, it
becomes ever harder to get what I would describe as

(24:24):
a truly integrated in sort of partner like relationship with
the resources that you have, right, And so it is
not a typical to find folks in the big platforms
who might like their job, might like the way they
get paid, but are actually frustrated about the fact that
it's a bit like being a small cog in a
big place. Right. So that's one aspect of it. The
second aspect of it is again the fact that the

(24:45):
firm is really large doesn't mean that you necessarily are
running any more money at a large place than you
would with us. In fact, our refile managers run likely
more money than they would run in most other places, right,
because yes, we're small, but we also have fewer people, right,
And so we're looking to run as large a scale
a team as you could with fewer teams, if that

(25:08):
makes sense. It's a distinction. And so from the perfimer
manager's point of view, that's actually not that different in
terms of how a risk you might get, but you
get better resources, more integrated platform on the technology, risk,
corporate access, etc. There's other things that have this flavor, right.
And remember, because most folks get paid out of some
share of the return that they can generate from that
amount of assets. It's not like your comp is going

(25:31):
to be terribly different. Right, if you run just as
much justice and your returns are good or better because
you get better resources, more integration, and a better platform,
it's not obvious why it's necessarily an unattractive platform. In fact,
we have found that we have hired folks that we're
performer managers, are other placers that came to be analysts
with us because they understand the benefit of all of

(25:52):
those things, right, as opposed to be one of I
don't know, five hundred analysts in some really large place
doesn't make sense.

Speaker 2 (25:58):
Wait, talk more about that, because I'm I'm curious. I
get the impression that a lot of multistrat firms or
podshops are always going after like the star portfolio managers
or people who have experience, and I'm curious, is their
scope for developing talent in house? For instance? Could you
hire me or Joe and train us to be a

(26:21):
really good portfolio manager. How much flexibility is there in
that career path.

Speaker 5 (26:26):
There's actually a decent amount of flexibility. So your preference
would be not to have to rely on imperfect information,
particularly if you have to promise somebody lots of things
in order to come to your platform. Right. So you
should have a preference to develop talent internally. The question
is what sort of culture insistence do you have to
make that happen, right, And I fact, I think you've
had guests on in the poscat on this podcast talking

(26:47):
about those training grounds, right, And so people understand that
you should have a preference to bring in people who
you can shape into who you think are going to
be the best analysm, the best portfolio manager in a
way that really matches with you know, your culture and
the way you pay and the way the systems work. Right.
Part of the problem though, is that humans are humans, right,
and so even if you train somebody, you can't guarantee
that they're going to stay with you, and vice versa.

(27:09):
You might, especially if you're really large and you have
to run lots of assets. In a sense, you're forced
into this turnover, right, because if you have to deploy
all those assets, and if somebody quits for whatever reason,
maybe they just have a personal thing they leave, not
because they're going somewhere else, you're sort of forcing into
this replacement process. And at some point, part of the
problem is you might not have the next person ready
to be promoted and therefore you've got to go outside,

(27:31):
right And I don't, to be entirely honest, I don't
think there's sertably different in this industry from any other
industry right where you need to hire very talented people
and there's a limited number of them, and you kind
of have to go through that mix of ingrown talent
hiring externally, you know, some mix of the two. And yes,
I could train you to be really good perfile managers.

Speaker 3 (27:49):
I want to get into soon, like actual how the
comp part, because it's nice to talk about access to
teams and you know, lean management and all that, but
you know it's finance will care about paychecks a lot.
But before we do, there's something you said, and it's
come up before and I still have a hard time
wrapping my head around it. So I'd like to hear
how you clarify it. When you talk about a PM

(28:11):
having access to a company's management team, that makes sense.

Speaker 5 (28:15):
I get it.

Speaker 3 (28:15):
Investing, you want to talk to the CFO or whatever,
the CIO or whatever the CEO, But you know we're
not talking Berkshire Hathaway here where you're holding a stock
for twenty five years and you really get to know it.
In fact, the sort of hold times for a stock
within one of the within a firm like yours supposedly
is extremely short, and sometimes maybe five days or ten

(28:36):
days or one quarter or something like that, in which
it's not intuitive to me that if I'm holding a
stock for twenty days, it's particularly important to say no
the management team the way Warren Buffett gets to know
a management team. Can you explain to me the importance
of that sort of insight into a company given the
short holding periods, given the high amount of actual training

(28:58):
that you do.

Speaker 5 (28:59):
Yeah, that's a really good question. I think it's just
like you're munging two things together that don't go together.

Speaker 1 (29:04):
Right.

Speaker 3 (29:04):
Yah, that's fine.

Speaker 5 (29:05):
I think you want to separate the investment decision, which
might be a sure horizon, versus what drives the inside
that gets you to that investment decision. Right. And so
the reason you want to really understand the company is
because that allows you to pick up on subtle patterns
about what the likely misunderstandings about that company is from
everybody else. Right, So I'll repeat, the way you make

(29:25):
money is you have a view that is different from
the other marginal participant, and the way you make money
is you place the trade, and then over time people
come to agree with you. Right. And it's either because
they eventually see the same thing that you do, right,
They see the same data, they do the same analysis.
Maybe you got there because your data is better, your
analysis is more sophisticated, et cetera. Or the firm tells you.

(29:45):
The firm literally comes and says, here's our earnings and
here's our revenue. And you turn out to be correct
versus other folks. Right, So you need that catalyst, right,
And so you're playing in the same firm over and
over again. But the nature of the insight is what's changing, right.
And so because you know of the firm that well,
and because you've been following it for ten years and
go to the conferences and talk to the management, et cetera,

(30:08):
you are able to tell that g well, this quarter,
my suspicion is that people are underestimating their earnings. Maybe
the next quarter they're over in estiem many of the earnings. Right,
And if I can repeat that process, my trades are
short horizon. But it's not that I have a short
horison view of the firm. In fact, if you if
you're going to do this well, you should have a
long view of what the firm is likely to do.
In fact, some of your hippodicies might be, Hey, people

(30:30):
are thinking that the XYZ product is going to be
you know, enormously successful over the next five years of
ten years aka long term view. But if you think
that that's going to be slightly disappointing this quarter.

Speaker 3 (30:43):
Why hold it Like a company, like a video, everyone
has a big tenure horizon corret so that's not that
you're not going to gain an edge just knowing that
AI is going to be bigger for the next time.

Speaker 5 (30:52):
Correct, the edge is going to be You might want
to be long on average example and video, but if
you think that they're going to miss those very high
expectations and exporter, why are you holding it down? You
could shuder now and then by again, you know after there.

Speaker 2 (31:21):
So one of the criticisms of multi strats and their
phenomenal growth has been this idea that we're getting more
crowding risk in the markets. And you brought up in
Vidia just then, and to some extent that's kind of
the perfect example of some of this. It feels like
whenever in Vidia has a big move, now there's some
talk about like oh there's a pod behind it. Yeah,

(31:42):
that's right, or like some sort of factor is changing.
Talk to us how you actually see the impact of
the growth of multistrats and factor investing on the market.

Speaker 5 (31:53):
Yes, okay, So I'm going to separate this into two pieces.
One is it how do you think about it as
an individual manager? And then what impact that has in
the because I think it's important to make that distinction, right,
So on the first one, I think crowding is one
of those things that you should manage rather than be
worried about. Right. The analogy that we sometimes use is
this idea of sitting at a poker table. Right, If
there's the two of us playing poker, POD's not very big. Right.

(32:16):
If three more people come in, I'm not worried about,
Oh my bed's going to be the same I you.
If I think I'm better than you and the three
people who've shown up, having more people at the table
is great, right, Meaning the way in which you make
money again I'll repeat, which is you have a different
view from the rest of the market participants and they
come to agree with you. That looks like crowding. Remember,
I come into a position before it's crowded, and the

(32:38):
way I make money is it becomes crowded, and at
some point I say, Okay, I've gotten paid for my view,
and I rotate into the next thing, hopefully the next
thing also early and whatever the idea is, right, And
so crowding in a sense is the mechanical way in
which you get paid from being early in an idea.

Speaker 4 (32:54):
Right.

Speaker 5 (32:55):
And so for a manager, an individual portfolio manager, or
a firm like ours, we want to think about how
do you manage the crowd So I'll give you an example.
Let's say two perficle measures. They both have the same quote,
crowding exposure right, measured in some way that we all
agree is a good way of measuring. If I got
there because I was early, and then I got paid
slowly as people came to be. In my view, that

(33:17):
is very different from somebody who's chasing the idea. Right,
They weren't early, They just see it happening and then
they chase. And is different because if there's a crowding online,
we both might have some negative returns, but I'd likely
have less negative returns because some of my ideas are new,
some part of my portfolio is not ask crowded. And
two I got paid on the way up, right, and
so how you get there is super critical right now

(33:38):
to market question. If there's more participants doing anything, whatever
it is, the mean return of course comes down. That
doesn't mean that the people who are at the high
end of skill are affected by it. In fact, they
might even make more money if there's enough people on
the other side of their skill, if that makes sense, right.
And the last thing that I would say is that
being a multi strategy fund is a way of organizing yourself, right.

(34:00):
It's a way of deciding that instead of running a
traditional integrated, single decision maker kind of fund, I am
going to think more carefully about how do I outcoupt capital,
hy do this thing? There's talent, how do I manage
all of these things that we talk about, the way
people get paid and all the incentives. It's a way
of organizing yourself. It's not an investment strategy. You could
organize itself that way and have lots of different ways

(34:22):
of investing. And it's the coincidence of the investment strategy
being the same that drives crowding. It's not the way
you're organizing yourself. So there's not obvious to me, and
I'm not sure that the data supports the idea that
somehow there's more crowding. In fact, the biggest crowding event
that we've ever had was back in two thousand and seven,
which is the Great crowding online.

Speaker 1 (34:41):
Right.

Speaker 5 (34:41):
Yeah, Crowding is a thing, no matter where it comes from. Right.
So if I have a bunch of long only active managers,
how liken video, that's just as mass crowding as you know,
some multi strategy liking and video. Does that make sense? Like, yeah,
different things.

Speaker 2 (34:53):
I think the concern is more that, like the emphasis
on we talked about the short term horizon of some
of the stuff, and you talked about the focus on
the catalyst. I think the concern is that at turning points,
maybe you introduce more volatility because everyone starts shortly, yeah, exactly, shortleash.

Speaker 3 (35:12):
Everyone knows these very tight stops they want to keep
their job, and that dad creates a specific type of
volatility because everyone the speed with which they have to
cut positions, etc.

Speaker 5 (35:23):
Yeah. I don't disagree, but again, that's something that happens
at the individual level. Right. So let's say you have
you know, whatever your stop loss is. Some firms don't
even have that. They do their risk control differently. That
is specific to a particular strategy, right, And so whether
or not that adds volatility depends on whether that strategy
happens to be correlated with five, ten, fifteen others, right,

(35:44):
and as not obvious why that should happen just because
people have this view. Does that make sense? Right?

Speaker 4 (35:49):
Yeah?

Speaker 5 (35:49):
So let's say that there's one hundred people playing for
the next earnings from I'll make it up. I don't
know Bank of America, right, Like, they're going to report something,
and there's a lot of people playing them. Of course,
if everybody on of these hundred people that I'm describing
is on one side of it, you may get a
big ball move depending on what the results are. But
it's not obvious why they would be all in the
same side, right, just because they're organized aspopumps? Does that

(36:12):
make sense? Yeah?

Speaker 4 (36:12):
Yeah.

Speaker 3 (36:13):
Let's talk about comp and making money. You mentioned very
kindly that in theory you think you could mold me
and Tracy into decent traders or a lesser PMS maybe
anless that's fine, Okay, So Tracy and I are there
and we seem to deliver something that resembles alpha over time.
What's our paycheck? How is our paycheck derived?

Speaker 5 (36:36):
Yeah, So, typically you want to have an incentive for
you to focus on the mechanics or your job, right,
and so typically there's a trade off between making your
compensation highly discussionary, I just decide because I like you
or don't like it, whatever, versus exactly formulaic right, fifteen
percent of your gross returns or whatever it is.

Speaker 4 (36:55):
Right.

Speaker 5 (36:55):
Typically, what you find is that the more you can
separate the job to be about these forty names in
the context of you know, some particular boundaries of risk
and capital deployment and concentration rules, et cetera, it becomes
easier to give that direct incentive, right. And so what
you'll find is that most places end up in a
circumstance where that incentive to be very focused on the

(37:17):
thing you're good at tends to drive better outcomes. Right now,
to be clear, there are trade offs on the other
side business wise, Right, So this is something that allocators
I suspect need to get better at really digging in.
So let's say you have thirty six risk takers. Let's
call them analyst right, and imagine three ways of potentially
paying them. One way is you net everybody's returns fair first,

(37:39):
and you know, some of them did well, some of
them they're poorly, maybe even negative. You get some total
return at the end across everybody, and then some fraction
of that is everybody's comp and then you sort of
paid discretionary. Right. It probably not as good from the
firm's point of view because it makes it hard to
have that sort of one to one incentive and really
focusing on the thing you're good at. But to be clear,

(37:59):
from the the allocator's point of view, it might be
the best because you're only paying for the returns that
were delivered in total.

Speaker 4 (38:05):
Right.

Speaker 5 (38:06):
Now, let's go to the oh, I say, yep. Now
let's go to the other extreme, which is typical now
with many platforms, which is eachrisk taker runs a small team,
each annalyst you know, has like an associate that helps them,
and each of them you pay, let's say the same
fifteen percent of whatever the share is. So now you
have this thing that people in the industry would called
netting risk, right, which is you pay fifteen percent of

(38:27):
the people who did well and the people who did poorly,
it's not that you're getting money back, right, And so
the total amount of compensation they're paying is larger than
in the first case.

Speaker 4 (38:37):
Right.

Speaker 5 (38:37):
In fact, in this example, imagine this thirty six people.
Let's say they each have THEO point seventy five that
example that I've been using before. If that's what's happening,
they pay, you pay about twenty five percent more in
comp costs in this second case as compared to the
first case. So if you say this is great because
everybody has a direct incentive of what they're doing, that's
not free. Right. It costs you literally twenty five percent

(38:58):
more cost Right. And in a situation where you're passing
through all this to your investors, your investors are worse
off by a decent amount.

Speaker 2 (39:06):
Right.

Speaker 5 (39:07):
Now, imagine middle ground where you say, okay, I want
one to one incentives with the thing you're really focused on,
and so I'm going to put these thirty six people
onto teams. Right, So I'm going to make teams of three, right,
and within that team they net with each other. Right,
So maybe one of them has a poor year or
the other two do well, And now you pay the
team that same share of fifteen percent within the team,

(39:28):
there's maybe some you know, ability to have some discussion
art u comp. Right, it's still more expensive than netting everybody,
but it's only five percent five to six percent more expensive.
So that version of the world gets you almost all
of the benefit of that direct focus on your job
with much less cost. Right, And so if you're an allocator,
you should be asking this. Remember in this example, these

(39:48):
are the same thirty six people with the same skill,
with the same total capital matters, and from the allocator's
point of view, it makes a huge difference which of
these you're doing.

Speaker 3 (39:56):
Tracy, I find this to be so fascinating that you
could basically have the same structure and that the math
works out so differently just if you sort of change
the size of the set where you do the netting
like this is really interesting.

Speaker 2 (40:11):
Way. I have another money related question, but how much
money would you give us as pms, Not in terms
of direct comp but how would you decide how much
we actually have to play around with? And then related
to that one thing I'm always unclear on with multistrap firms,
it seems like the size of the available capital pool
is sometimes a draw for individual pms like oh, I

(40:34):
get to play with I don't know like fifty million
or I don't even know what a normal number is
for them. But on the other hand, you sometimes see
headlines about how you know, Citadel or Millennium have to
limit new investor funds. So I'm wondering, like, how do
you right size the available capital for trading?

Speaker 1 (40:55):
Yeah?

Speaker 5 (40:55):
Okay, so there's I think there's multiple questions in there.
One is like a capital location, So how do I differentiate?
Do I give you more than her advice versa?

Speaker 1 (41:03):
Right?

Speaker 5 (41:03):
So that's like whatever the amount I have, there's an
allocation question, so we can get there in a second.
And then there's also the is there such a thing
as like an optimal amount for an individual person?

Speaker 4 (41:13):
Right?

Speaker 5 (41:13):
Let me start with the second one. The answer is
generally yes, and I think would your previous I think
it was Kapi who made this point that there's a
human and sort of psychology aspect of how much money
you can comfortably run, right, and so typically past a
certain amount, Literally, the psychology of seeing however much you're
making or losing every day gets really large and uncomfortable

(41:34):
for a lot of people.

Speaker 2 (41:35):
Right, I get anxious just looking at my four oh
one case.

Speaker 5 (41:38):
Yes, exactly that, and that to be clear, that's a thing.
Right you Let's say you start somebody running, I'll make
it up one hundred million dollars of just dollars, right,
and they're you know, they're fifty of them are long,
fifty short, and maybe every day they go out by
you know, half a million. You know they're be done
by half a million. Right, that's sort of the range.
Now you make that ten x int space it might

(42:01):
be literally identical, but the psychology off you walk into
the morning and the market's open. Now you're down five
million dollars. There comes a point where people where that's
a thing.

Speaker 3 (42:09):
Right, Like when I like play poker, I wonder if,
like it would be nice if they would just lie
to me and say you're playing a one to two game,
you're buying for two hundred, and then at the end
they're like, oh, it turns out you're playing for two
thousand because the chips are the safe.

Speaker 4 (42:21):
Yeah.

Speaker 5 (42:21):
And the psychology, the way the psychology plays is not
just on the amount of money you can comfortably run
and remember the bigger the amounts you have to worry
about things other than your say, fundamental views. You have
to worry more about tea costs and implementation questions and
liquidity questions, and you know, how can do you get
to play on smaller cap names where you maybe feel
you have an edge, but now you can't really do

(42:41):
as much of it. So there's all these sort of
things that have to do with scale. The other thing
that happens is a psychology and compensation.

Speaker 4 (42:47):
Right.

Speaker 5 (42:47):
It is not uncommon for folks to prefer I could
give you a billion dollars and pay you fifteen percent
of say your night returns, or maybe half a billion
dollars and pay your thirty percent. Right, it's the economics
are the same. Many people might prefer the latter rather
than the former, right, So psychology does play a significant
role in this. We tend to find that good perform

(43:08):
managers can actually run, assuming they have a good team
with them, in the billions of dollars, But it's not
necessarily the most common situation. Most platforms find themselves running
smaller teams with lots of littlecations. We then have all
these netting issues, right, so you do want to think
about that. The second question is, okay, how are big
eat port IFOI? You might get how do you separate? Like,

(43:29):
how do I give you more than other person?

Speaker 4 (43:31):
Right?

Speaker 5 (43:32):
The reality is you want to make your capital location
based on your expectation of return.

Speaker 4 (43:37):
Right.

Speaker 5 (43:37):
Will you have good sharp ratio in the future. Right?
The problem is you don't know the true sharp ratio.
Most people are tempted to use some realize sharp rasio.
What was your sharp ratio last year?

Speaker 1 (43:46):
Right?

Speaker 5 (43:47):
And the problem is there's a huge amount of noise
in that.

Speaker 4 (43:49):
Right.

Speaker 5 (43:50):
And I find the intuition of this really interesting. So
if you have a good basic way of thinking about it,
let's say you cover forty names in your views. About
these names, let's say I like this, I don't like this.
Every day are correlated with actual returns by what one percent?
So not a lot of predictability, like nine to nine
percent of what's happening you don't know, but you have
one percent predictability. If you do this and trade based

(44:12):
on these views, you will have a sharp person of
about one at the end of the year, which is
pretty good for forty names, right, meaning little amount of productility.
One percent in this case is what people call the ic.
The correlation between your views and next day of returns.
Get to a pretty good outcome at the end of
the year. It also tells you that there's a huge
amount of noise. Right, So if you think about let's

(44:32):
say that we all three of us agree that you know,
we have a crystable, and we know for a fact
that there's a person that has one percent correlation between
views and returns, and we observe a year worth of returns,
and we observe that for one hundred years, the average
sharp will be one, but some years will be low
because you know of the ninety percent, you're not predicting.

(44:53):
You might be unlucky some year and you end up
with a SHARPO of zero. Some years you get really lucky,
you end up with a sharp of two. So realize turns.
Realize sharps have a huge amount of variation. So you
don't know what the true sharp is. You only observe
the real life sharp, and so if you make out
locations based on the real life sharp, you're mostly allocating
on noise, especially if you only do it over a
short period of time. Right, And so the way you

(45:14):
want to start is to say, look, I'm going to
ignore the past returns and do equal risk. That's essentially
the same as saying, I am going to assume that
the two of you have the same I see the
same sharp I don't because I don't know what it is. Right,
It's sort of Abasian statistics kind of thing. Right, And
then I deviate away from that benchmark of equal risk
as I to learn not so much more about your returns,

(45:35):
but what drives returns, so overy time I might be
able to observe that. Actually, as it turns out, one
of you is really good at the margin parts of
thinking about earnings, right, and for kind where names where
there's a lot of room to think about differences and
views about margin, and you happen to do really well right,
whereas somebody else might have high expertise on product questions, Right,

(45:57):
will a product fly or not fly in a particular space?

Speaker 1 (46:00):
Right?

Speaker 5 (46:00):
And I collect data about the stuff. So let me
give you an example. Let's say you tell me the
reason I generate one percent correlation between my views and
returns is because I'm good at predicting surprises, right, earning surprises, Okay,
and you tell me that you can predict surprises at
ten percent correlation. So every time you have a prediction
for forty names, they are correlated ten percent with actual surprises.

(46:21):
So this is not much better because if I collect
data about your predictions of earnings, not returns, I can
distinguish ten percent from zero much better than one percent
from zero.

Speaker 4 (46:30):
Right.

Speaker 5 (46:31):
The second thing that is true is that I knew
that returns are correlated with earning surprises by about ten percent,
And to be clear that I can do with lots
of data. I can go back in time and think
about the correlation of returns and earning surprises for every style,
going back in time for fifty years. Right, And these
are transitive. So if you predict earnings by ten percent
and returns are correlated with earning surprises by ten percent,
you get the one percent that you're looking for. But

(46:53):
I can look at your earnings and do much better
analysis because those are ten percent correlated with actual earnings
doesn't make sense. So as I get time, I can
get to understand your investment the underlying things that drive
those returns much better.

Speaker 3 (47:05):
This seems like a very big theme throughout this conversation
that the more you can understand why things work correct,
the better you are, the easier many other decisions become.
And I have one last question for you say, we
have some students. College students listen to odd lots from
time to time. I'm a freshman in college. I'm interested

(47:26):
in finance. It sounds like a fun career. I want
to make a lot of money working for a multi
strategy hedge fund one day. What's the best decision I
could make right now as a freshman or sophomore in college.
They would most likely open a future door for me
for something of this career.

Speaker 5 (47:42):
Yeah, that's a that's a good question. You know, we
run an internship program, so you get asked this thing
all the time. I would say two things. Number one
is you I think need to have a good mix
of liking and being reasonably good at the I'm going
to call it the data part of it. Right. These
ares are all about do I understand the data that

(48:03):
tells me something about this firms?

Speaker 4 (48:04):
Right?

Speaker 5 (48:05):
And so you know, whether it's you know, I cover
consumer firms and I'm looking at kurk card data and
you know, thinking about, you know, what is the color
of the fall and how I might get you know,
data about whose color is going to be the important one?
And what story of am I running? And all these
sorts of things. So there's a lot of data analysis
that they have to do, and you have to be
sort of both good at it and really like it
because it becomes sort of your day to day.

Speaker 4 (48:25):
Right.

Speaker 5 (48:25):
The second thing is you have to be willing to
understand that there's sort of a grind aspect of the job.

Speaker 1 (48:32):
Right.

Speaker 5 (48:32):
It sounds really exciting to think about predicting things and
potentially making a lot of money, but the reality is
that the data to day job can be a bit
of a grind. Right. You're covering these forty names, and
they are the same forty names every year, right, and
you're listening to every conference call and listening to every
airnings announcement, and you're looking for like tiny little bits
of differences. It's like, well, you know, last Timmer around
they describe the nature of the particular product that they're

(48:54):
working on in this way. Now describing is slightly differently.
I wonder if that means something about their strategy, and
so is this sort of to partner uses the word
of coal mining, right, it can be. It could be
a bit of a grind right.

Speaker 2 (49:06):
Now in the minds of multi stress exactly right.

Speaker 5 (49:08):
It's not all the excitement of I show up in
the morning and have an idea and now I make
coup exactly.

Speaker 2 (49:14):
Yes, Wait, speaking of the grind and interns, is there
a future where I know you spoke earlier about the
importance of the human factor in a lot of this,
but could you switch the emphasis to more AI.

Speaker 3 (49:30):
This other thing that I wasn't gonna get it?

Speaker 5 (49:32):
Yeah, because I'm thinking, I'm happy to talk about AI.

Speaker 2 (49:35):
Stock Want Funds were like the original users of machine learning,
or one of the big original users, so it seems
fairly natural for them to use more AI in order
to spot potential patterns or potential catalysts for big moves.

Speaker 3 (49:49):
Tell us what's real and what's bs.

Speaker 5 (49:51):
There's always a mix. But I do want to say
something before we get to a specifically, this sort of
job is always a bit of an arms race, right,
meaning this sort of thing that made you money, Let's
say twenty years ago. Twenty years ago, you could have
been an analyst that figured out that in order to
understand particular, say retail firms, you could go look at

(50:12):
footnotes about whether you know you owned or at least
your retail space where you sold your T shirts or
whatever it was, and that might have had some consequence, right,
depending on how your finance and what that meant for
you know, Etcaday early data stuff, Right, you don't do
that now. And the reason you don't do that now
is because that's all in the database that everybody can
go mechanically look at it, right. And so there's this
sort of sub get you need to become ever more

(50:34):
sophisticated data and analytics wise, and AI is sort of
one more step in that direction, right, So I don't
think of it as something inherently different from this sort
of constant evolution of always being more sophisticated and understanding
the firms.

Speaker 4 (50:47):
Right.

Speaker 5 (50:48):
The one thing that I would say about AI is that,
at least up until this point, if you think about
how AI is trained, right, you feeded all this text
essentially mostly from their Internet. And the job that it's
trying to do is that it's trying to predict the
most likely answer to a question, or the most likely
thing that comes after some prompt. Right. That's essentially what

(51:09):
you're doing. And what that means, by definition is that
if you ask it, hey, what is different about company X?
By definition, it's going to tell you what everybody else
thinks is different about companyes, which means it's actually not
the different thing. Aka, you're getting the consensus right, and
so that could be quite useful in the way you
think about doing data analysis as lots of ways. And
we have a bunch of investment in AI work within

(51:31):
the firm, but that is not the same as assuming
that AI will have inside about the firm, because it's
been trained on the average of things kind of by definition, right,
And so the step of going from it helps me
summarize or potentially, you know, kind of clarify what themes
are people talking about. And there's lots of things that
you might be able to do with it that is

(51:52):
not quite the same as the jump to and therefore
here's a difference in view versus everybody else's views. Does
that make sense? Yeah?

Speaker 2 (52:00):
Absolutely, Dan, Thank you so much for coming on all thoughts.
That was great, amazing You explained the maths perfectly.

Speaker 3 (52:06):
So Dan, Matt, Yeah, No, it was really great than like,
I feel like a million questions we answered your very
game to really work us through, work work through all
of them with us. So appreciate you coming on.

Speaker 5 (52:17):
Thank you, Joe.

Speaker 2 (52:30):
That was fun.

Speaker 3 (52:31):
It was so fun.

Speaker 2 (52:32):
I like talking about maths and multi strap funds, DAN maths, Yeah,
the DAN mats. So there are a few things to
pick out of there. I really liked the emphasis, and
this has come up before, but the idea that crowding
in is not necessarily a bad thing for individual managers
because what you're trying to do is identify that catalyst. Yeah,

(52:53):
that will get everyone crowded.

Speaker 3 (52:55):
Crowding, crowding in how you get paid, Yeah, like you
eventually you just want to be there before the crowding,
But the crowding is ultimately what delivers the paycheck.

Speaker 2 (53:03):
Right now, does that maybe have a less desirable effect
on the overall market? I mean I kind of take
the point about, well, if you have a bunch of
long only funds that are in something and then something
bad happens, they'll all retreat. That that's like the same
effect as multi strats crowding in. But it does feel
to me, just observing the market in recent years, that

(53:25):
you are getting these sort of shorter and sharper turning
points or reactions.

Speaker 3 (53:30):
Totally, there are so many things that I took from
that conversation. I thought that was fantastic, and all of
our conversations about this topic have been good, but to
talk to an actual founder of a fund though it
was great. You know, there was the big conceptual thing
that he kept coming back to, which is that the
more you can know why something works, the better. I
think I'm pretty good at my job of co hosting outlas.

(53:52):
I think you are too.

Speaker 1 (53:54):
But I host.

Speaker 3 (53:54):
Yeah, but I do think and they're like, you know,
I know other people are good at their jobs. But
to be able to articulate why you are good at
your jobs, and provably be able to articulate why you're
good at your jobs, would you.

Speaker 2 (54:05):
Go why you didn't just get lucky?

Speaker 3 (54:07):
Yeah, why it's not lucky? Why you are able to
identify something like, oh, I am very good at identifying
earning surprises. Setting aside the question of am I good
at picking stocks? That's a really interesting way to think
about it, Like, Okay, we know that earning surprises are
correlated to stock performance. If I could prove that I'm
good at X, then I could probably prove that I'm

(54:28):
good at stock selection. That is really interesting. I love
like hearing about the math of like why you want
to avoid correlation between managers and how powerful that effect
is and how few pods you need to get optimal.
So much good stuff. The part about compensation, yeah, super interesting.

Speaker 2 (54:46):
Well, I do think in general a good piece of
life advice is identify your comparative advantage early on, right,
and play up to it, Like figure out what you
do well and why you do it well. That's a
real good thing to do early in your career.

Speaker 4 (55:02):
All right.

Speaker 3 (55:03):
No, I figured out early in my career that my
one competitive advantage in journalism was waking up at four
am before everyone. And now I'm spending thousands of dollars
a year on therapy to like allow myself to sleep
in a little bit more. So there are some drawbacks
depending on what thing you identify.

Speaker 2 (55:22):
All right, everyone stop asking Joe or stop telling Joe
what he missed, because it's just compounding. That's this problem.

Speaker 4 (55:29):
All right.

Speaker 2 (55:29):
Shall we leave it there.

Speaker 3 (55:30):
Let's leave it there.

Speaker 2 (55:31):
This has been another episode of the au Thoughts podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.

Speaker 3 (55:37):
And I'm Joe Wisenthal. You can follow me at the Stalwart.
Follow our producers Carmen Rodriguez at Carman Ermann Dashill, Bennett
at Dashbot at kel Brooks at Kelbrooks. Thank you to
our producer Moses ONMDAM. For more Oddlots content, go to
Bloomberg dot com slash odd Lots, where you have transcripts,
a blog, and a newsletter and you can chet about
all of these topics twenty four to seven in our

(55:57):
discord Discord dot ggs.

Speaker 2 (56:01):
And if you enjoy odd Lots, if you like our
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