November 28, 2025 • 54 mins
In an episode from July, Katie and Matt talk with Gappy Paleologo of Balyasny Asset Management about gardening leave, what makes a good quant researcher, factor models, the social function of hedge funds, AI and journalists as portfolio managers.
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Speaker 1 (00:02):
Bloomberg Audio Studios, podcasts, radio news, Happy Thanksgiving.
Speaker 2 (00:13):
I hope you're having a great holiday. We are obviously
not recording on this Thursday, so we're.
Speaker 1 (00:18):
Gonna rerun a favorite old episode. It's going to be great.
Speaker 2 (00:23):
You're going to enjoy it even more the second time. Bye.
Speaker 1 (00:30):
Wait gay palio logo or.
Speaker 2 (00:36):
I'm so excited for you to tackle that and not me.
Speaker 3 (00:38):
Good luck.
Speaker 2 (00:39):
I thought I had it down, but then I heard
you say it, and I feel like.
Speaker 1 (00:43):
When I first met you, I think I asked you
if you go by gappie because of your famous track
record of taking gardening leave, like having gaps in your
career as well.
Speaker 4 (00:58):
Okay I didn't remember. Yeah, that's a great excuse for
for a nickname. No, But the reason is when I
came to the States for grad school, and this was
a long time ago, in ninety five. So the first
thing that you did was set up an email account.
You still had the freedom to choose an Emil account.
(01:19):
Now they just give you your initials with the number,
and so my initials are gap Gapo, and of course
it was taken. So I said, okay, well Gappy, and
then everybody in grad school and then my wife was Italian,
everybody started to call me Gappy and that's stuck.
Speaker 3 (01:40):
And now at.
Speaker 4 (01:40):
Work they just have dispensed with my real name, like
on all systems, I'm just Gappy Paliologo.
Speaker 3 (01:46):
So I expect that that will be, you know.
Speaker 4 (01:49):
Prosecuted for tax evasion because on my tax forms there
is Gappy Paliologo or something like that.
Speaker 1 (01:55):
Well, hello, and welcome to the Stuff podcast.
Speaker 2 (01:58):
I'm at Livian and I'm Katie Greifeld.
Speaker 1 (02:01):
And we have a guest today, Gabby Paleo Logo, who
is now at pali Asney has been at most of
the other pig Catch funs and Hudsond River Trading. I
do want to start by talking about.
Speaker 2 (02:15):
Gardening there, Okay, Natural Purse.
Speaker 1 (02:19):
I think that we counted from your link. Your LinkedIn
is like famous for discussing your gardening leave in some detail,
and I think we counted three years of gardening leave.
Speaker 4 (02:30):
No, I think it's a bit like Okay, it's not precise.
Fifteen months from Citadel, one year Hudson River Trading, and
four months from Millennium. Okay, so pretty close, not terrible,
though a bit less than two years.
Speaker 1 (02:44):
From my perspective, it seems very fun. Did you enjoy
your three years of gardening?
Speaker 3 (02:50):
I do so.
Speaker 4 (02:52):
I try to keep myself busy, so I teach, typically
at some university. So the first time during my Seitaadel
two millennium, Guardian Leve I was teaching at Cornell and
in the HRT to Bam Guardian Leve was at NYU.
And I love teaching. And then what I do is
(03:13):
it helps me focus on stuff. Usually what I do
in you know, whenever I read a book or read
a paper that I like, I take notes, take notes
in lattech, and then I read, arrive or think about things,
and so that typically is the basis for my course material,
(03:34):
and then it becomes the basis for my books. I've
written a couple of books during my non competes.
Speaker 2 (03:39):
Interesting because thinking about gardening leave. Matt and I talk
about it all the time, because it's very alluring to me.
Gardening leave doesn't really exist in journalism. I love to
imagine what I would do. But one of the questions
I had for you was, you know, do you ever
have anxiety about losing your edge or falling behind? But
it sounds like teaching is one of the ways that.
Speaker 3 (04:00):
Yeah, I'm not particularly worried with that.
Speaker 4 (04:03):
I think that there is only a very specific subset
of quantitative researchers who are afraid of losing their edges.
And yeah, that's not been my case. I keep reading.
I try to stay up to date.
Speaker 1 (04:15):
To the feedback into the work. Like do you get
ideas or like deep in your understanding of techniques by
teaching and writing the books? Or are they just sort
of like.
Speaker 4 (04:26):
Extracurricular No, no, no, it's definitely I learn a lot
from writing the books.
Speaker 1 (04:31):
How long do you I get to hear your next job?
And yeah, generate more profits by of.
Speaker 3 (04:35):
Course, plenty more profits. Sell that to my employers.
Speaker 4 (04:38):
No, but I definitely I learn a lot from writing
from the first drafts, and then I rewrite and rewrite,
and I learn a lot from discarding material too. It's
very useful to discard material. It makes you really focus
on what matters and what doesn't. So I try to
give a narrative, like a logical connection between various topics,
(05:00):
and that is something that is possible only when you
write a book. I really do not like writing that.
Nobody I think likes writing, maybe except for you.
Speaker 1 (05:11):
I I think I understand that it's weird even among writers, but.
Speaker 4 (05:15):
It is very I find it very painful. I find
painful letting go of material, Yes, but I also like it.
You know, it's some kind of strange delayed gratification.
Speaker 1 (05:27):
I guess one theory that I have written is that
hedge funded quantitative research gardening leave is like a source
of like human flourishing, because you have all these like
highly trained people who haven't enforced the year of And
I've written that all the Hedge fund researchers should go
work at LM companies or like analytics departments of sports teams.
(05:52):
And I'm like, partially kidding and partially not. How true
is it for you? Like how much of like your
quantitative skills at this point are really just for investing in?
How much of it is like if you spent three months,
you know, consulting for a soccer team, you would be
(06:12):
able to tell them how to find better players.
Speaker 4 (06:17):
I'm not sure, so I'll say this right. I was
thinking a few days ago if there was a kind
of a common thread in my professional life because it
seems kind of random, And actually I think that there is,
because I think that I was about fourteen when I
realized that I had an aptitude for applied math. I
(06:38):
discovered physics, and I liked math, and I also liked
literature very much, so I loved reading. I read a lot.
I was not a very social animal. And then basically
since then, I've been doing the same thing in various forms. Right,
I did physics, I did applied math. I didn't do
(06:59):
applied math in finance. They did applied math in weird
things like optimization and logistics. So I have been doing
kind of the same thing over and over, which has
been writing and applying math to something. So I think
that I could do it. I would like to do it,
but I also think that it's not that simple to
(07:21):
go to a new field and say, oh, after three months,
I know soccer. No, there is a lot of specificity,
And the beauty of I think being a good applied
mathematician is that they start with the problems and with
the domain first, and that they're sufficiently mature from a
mathematical standpoint that they are not making too much of
(07:43):
an effort in using math. So I think the good
art of being an applied mathematician is to study persistently
the application. So no, I don't think that after three
months it would be good enough. But after a year,
you know, about a year of being fully in an application,
then you start getting a little bit better and then
(08:05):
the mass is not the problem, and then you start
doing some good work.
Speaker 1 (08:09):
You have a famous essay on like advice for quant
careers and you say that, like, the things that matter
the most are creativity and genuine interest in the problems
more than you know, math course power. Yeah, this is
a dumb question, but how does one develop? How does
one identify you know, creativity and interest in financial topics?
(08:33):
And is the obvious answer those are where the money is,
or like like why why did you fall in love
with finance as a topic? And is the answer because
that's what the money is.
Speaker 4 (08:42):
So first of all, I think that creativity is either
personality trait doesn't belong to You're not creative in finance,
you know, you're you're creative in in cooking, you're creative
in whatever. And it's a mix I guess of extraversion
open and as to experience, and I don't what else.
I'm not a psychologist, but I do believe that people
(09:03):
are genuinely creative, and in fact, you see it right
that sometimes you ask someone and you find out that, yes,
they like writing, they play some instrument, if badly, and
you know, and they paint and they do whatever. And
so I would say, if you go to finance because
that's where the money is, there's nothing wrong with that.
(09:24):
And in a way, that's my story. You know, I
was I was a researcher, and I wanted to have
more money and whatnot. But eventually you stay in finance,
or at least in my you know, little domain, because
you're genuinely curious about finding out stuff, right.
Speaker 1 (09:41):
So, like why are the problems like why do they
arisk curiosity? Like why are the problems of finance intrigue
you after years of doing it right? Like what's interesting
about those problems as opposed to other domains.
Speaker 4 (09:54):
It's really hard for me to say, Like I think
that I read once that a young songwriter asked Bob
Dylan how to become a good songwriter, and bub Bylan
just answered, well, what's going on? What do you mean,
what's going on? Yeah, what's going on? What's going on
in your life? Just you know, look around. So sometimes
I get these questions from investors or but you know,
(10:15):
how do you keep yourself interested. How do you find problems?
It's not a problem like the problems jump at you
like there are too many problems. There are too many
interesting problems. So if anything, the skill is in sorting
the problems in the right order. Right, That is where
maybe having some maturity in doing research kicks in. But
(10:36):
there are lots of problems, infinite problems, weird problems.
Speaker 1 (10:41):
What's your favorite problem right now?
Speaker 4 (10:43):
I don't like right now, what are we working on?
I mean, we are trying to understand how earnings are monetized? Right,
how do you make money in earnings? It's such a
basic thing in fundamental equities.
Speaker 1 (10:54):
And you mean, if you're like correct about predicting earnings?
Speaker 4 (10:58):
Yes, what are I mean? Without get into much into details,
but you know there what are the relevant variables? Imagine
that you had an oracle who told you what the
variables are? What would you do with that? What would
you do if you'd had all the information in the world, right,
and everything in your world here in existence? Would be
like an approximation problem.
Speaker 1 (11:18):
There's there's an incredible styled story of like the guys
hacked into I think like one of the newswire services
and got earnings releases early, like for hundreds of companies,
and they traded on this and they had like a
seventy percent success rate, which is great, but also like
it means that had a thirty percent, Like they traded
the wrong way, knowing earnings perfectly in advance. It's like
(11:40):
a good.
Speaker 4 (11:41):
Yeah, yes, so they had the racle on it. You know,
it's still hard, Yes, it's still very hard. Actually, shout
out to Victor Hagan, who wrote the paper about ten
years ago on this. He made a organize a simple
controlled experiment where he gave basically a biased coin where
you I think had a success rate of sixty percent
forty percent failure, and you some capital and you could
(12:01):
invest it over time on these informed predictions, and a
lot of subjects went bankrupt. Okay, now I think we
are better than that, but still there are lots of
problems related to trading around an event. For example, before we.
Speaker 2 (12:17):
Get too far away, you mentioned Bob Dylan. It actually
reminded me of another Bob Dylan quote which I'm going
to paraphrase poorly, but he basically said, when asked about
writing songs, do you think that you could write whatever
the work that was being referenced now, and he said,
I don't think so. It's like the words were in
the air and I just plucked them out. They were
(12:38):
just sort of hanging in the air and they came
to me. And it kind of also rang true with
what you were saying about you didn't go looking for problems.
They're just there necessarily. I actually want to go back
to applied math if it doesn't interrupt the course of
conversation too much. You tweeted on June twenty fourth that
there's no child prodigies when it comes to poetry, when
(12:58):
it comes to applied mathematics. And I'm not saying that.
You said that you were a prodigy, but you were
a child at fourteen. I mean, how how fourteen do
you realize that you have an aptitude for something like
applied mathematics.
Speaker 4 (13:13):
I don't want to flex about this stuff, No you should.
I think I'm honestly a little weird. I'm just a
little weird, I think, honestly.
Speaker 2 (13:20):
But I like prodigy weird or I did.
Speaker 4 (13:23):
Have my share of yeah, adults telling me that I
was good at this or that or you know, but yeah,
I mean, okay.
Speaker 3 (13:31):
I'm just a little bit atypical.
Speaker 4 (13:33):
Also, when I talk to investors, I think investors enjoying
my presence because I think I'm incredibly unfiltered for somebody
who's talking to.
Speaker 3 (13:43):
Them, so it's like fun for them.
Speaker 4 (13:45):
And I was very unfiltered when I talked to my
professors in school. Sometimes I corrected them stuff like this. Yeah,
I don't know, Honestly, I don't know.
Speaker 1 (13:56):
When you talk to like fundamental equity portfolio managers, like
how much like matrix algebras they're in your conversations like
how quantity are the fundamental pms or whatever.
Speaker 4 (14:09):
I don't think they're quantity, but I think that they're
very analytical. So I don't think that they would make
great mathematicians, but I think they would make very very
decent applied mathematicians. Actually, they tend to be very analytical.
They tend to be very process oriented. And they have
also additional qualities that actually mentioned in that essay, like
(14:31):
they have very little disposition effect, so that's part of
being analytical. They have no sound cost fallacy in them.
So even though they don't do a lot of math,
but they do some math. Okay, So first of all,
they're fluent in a sense in basic literacy, but I
think it's more their process that is closer to if
not a mathematical one, but more of a scientific one.
Speaker 2 (14:55):
And when it comes to being a quant does it
basically boil down to being good at math and being
interested in math? Are things such as statistics and physics?
I mean, do you need to have any finance or
economics background at all?
Speaker 4 (15:11):
So I think that having an economics background is not
necessarily a benefit, might even be a disadvantage actually, But
just based on very few samples that I have a
lot of very good, outstanding quantitative researchers actually come from
physics and specifically from astrophysics. That's the experience that I've
(15:32):
had in a couple of places.
Speaker 2 (15:34):
In broad brushstrokes, could you talk about why economics in
the small sample size you have, how could that possibly
be a detriment good?
Speaker 4 (15:45):
So I can answer the second question more easily. I
think that astrophysicists deal with large amounts of data, and
they deal with observational data, so they don't get to
do a lot of experiments, and that's good for finance. Right,
you deal with a lot of data, you need to
know how to have good agen for observational data, and
(16:05):
you need to have very good theory, like you need
to have very good instruments without being falling in love
with those instruments. Whereas I think economists, Okay, first of all,
my statement is purely empirical. Okay, so I'm just really
guessing on economists, and I'm going to be hated by
all economists or economists in finance, but I do have
(16:30):
my issues with their methods.
Speaker 3 (16:32):
Right.
Speaker 4 (16:32):
So, first of all, I think that there is an
original scene in economics, which is I think a lot
of economics is informed by a desire to be as
rigorous as mathematics, right, and so a lot of theoreticians
in economics are very deductive in their approach. If you
think of you know, the unrealistic assumptions behind the welfare
(16:55):
theorems or Rows impossibility theorem or whatnot, or just pick
up you know Samuelson textbooks, and I think this is
just rather very acxiomatic.
Speaker 3 (17:07):
Very deductive.
Speaker 4 (17:08):
Whereas physicists are very happy to think in terms of small,
idealized models that apply to a specific domain, and if
the model doesn't work out, they will discard and make
another one. The grand theory behind physical theories exists, like,
there are people who do this for a living, but many,
(17:28):
many good theoretical economists physicists starting the small and then
they expand the domain of their models. So economists tend
to maybe in a sense, fall in love with methods
too much, with techniques too much.
Speaker 1 (17:55):
We had clip Asness on the podcast a little while ago,
and my father, not a finance listened to the episode
and said, I still don't know what a quant is.
I just read skimmed your new book which is called
The Elements of Quantitative Investing, and as lays out the elements,
what is a quant like? What are the elements? Like?
(18:15):
What's the thing that makes someone a quant investor that,
like someone reading a slim book about the Elements of
quant investing needs to learn?
Speaker 4 (18:23):
Well, if I am being consistent with my book, investing
is really about problems and not about specific techniques or
anything like this. Right, So it's basically a way to
go through the whole investment process from let's say preparing
the ingredients to cooking to eating that is very processed driven. Ultimately,
(18:43):
you would imagine that one thing that you know quantu
investing has in common across multiple domains you know, if
you do futures, stocks, event based and whatnot. Is I
think the number of bets tends to be high in
systematic right, so you can be a very successful microeconomic
(19:04):
investor portfolio manager. And you you know, according to even
several statements by Buffett, you know, he made like ten
twelve very good bets. Okay, so that's great, and that's
not quantu investing. You know, you could put enough pms
making you know, twenty bets in their lives and you
will get a few that have let's say twelve thirteen right,
(19:26):
and they will be rich. We do not have that luxury, right.
We have to make millions of bets. You know, we
trade a portfolio with three thousand stocks sometimes in waves
of half an hour. You can't make a judgment on
all of these bets. So you need a method that
reduces the dimension of your problem to something that can
(19:46):
be treated in a systematic manner. I don't know if
that answers for you. You know that, but you know, basically,
basically the idea is, think about if you make a
lot of bets, you cannot bet individually. You have to
have some kind of view res take or some kind
of method around that.
Speaker 1 (20:01):
Right, and like to me, like the book sort of
you know the standard method I guess and quant investing
is you built a factor model of what drives your
universal investments. You're shaking your head.
Speaker 4 (20:13):
Yeah, I yes, and no. I think yes because the book,
you know, has maybe one hundred and fifty pages on
factor models.
Speaker 3 (20:19):
But also no, because maybe.
Speaker 4 (20:22):
In one hundred years from now, I suspect there will
be still something left. But you know, we might have
better techniques and not necessary factor models any longer. I
don't know, we.
Speaker 1 (20:32):
Don't want to go. Two attractions of that one is like,
are the better techniques something more neural netty unstructured?
Speaker 4 (20:40):
Who knows? Yeah, something like that. I mean there is,
there is a revolution every five years.
Speaker 1 (20:44):
So my other question is like I've never fully understood
like a factor model is like, here are some factors
that drive the returns of stocks, and then there's like
some residual idios and credit return There are clearly people
(21:04):
whose business is to identify factors and then invest in factors.
My impression is that at like the places that you work,
the business is the opposite of that is to pedge
out your factory risk as much as possible and to
get as much idiotsyncratic risk as possible. Is that right?
And like, like, how do you discriminate between like a
(21:26):
factory return and idiosyncratic return, Like what makes the thing
a factor as opposed to another thing.
Speaker 3 (21:30):
So that's a good question.
Speaker 4 (21:32):
So first, a lot of systematic investing is still about factors,
just not the factors that get published in the literature,
you know, not the factors that Cliff maybe was talking about.
And yet a lot of successful systematic investing is really
factor driven, see in.
Speaker 1 (21:48):
The sense that you have a model that has like
twenty factors and like ten are like value, and you
neutralize those and you try the other time kind.
Speaker 4 (21:56):
Of you do, and you do the rest. You have
other terms that matter. So that's one thing, but there
are two other things. There are sometimes sources of returns
that are factor like but not quite like factors. So
you may have a theme. For example, you may identify
a theme in the market that is not pervasive enough
(22:19):
or is alive only for a few months, but it
is there and it's not only affecting let's say two stocks, right,
So these brought themes can be invested on, but cannot
really model in the traditional way as a traditional factor model. Also,
there is a lot of good modeling in factors as
opposed to bad modeling. So it seems easy, but it's
(22:40):
not that easy. So there is a little bit of
craftsmanship in making these models. Okay, And then the third
thing is that there are also returns that have nothing
to do with factors, or almost nothing to do with factors.
So if you really know how a company works, and
you have a little bit of an edge in predicting
its future performance, and you can bet on it, and
(23:02):
you make enough bets and again you will make some
money if you repeat, and you know recycle. So even
discretionary investing in this sense has inherited a little bit
of the spirit of systematic investing.
Speaker 1 (23:15):
I think of that as like that apod job, but
like a baliosni Like you have discretionary investors who know
a lot about a company, make bets on the company,
and then someone like you tells them, these are your
factory exposers. You have to get those down to zero
that you're making pure bets on your idios and creditnowledge
of the company. Is that like you're kind.
Speaker 3 (23:36):
Of right, kind of right.
Speaker 4 (23:37):
Yeah, I think that at this point, it is very
interesting how the mind of professional portfolio managers has been
remolded in a factor based world, so that a modern
portfolio manager, discretion portfolio manager thinks in factors. You know,
so I don't even need to tell them, hey, this
(23:58):
is your exposure. They see their exposure, they have the
tools to see it, and they control it in real
time with minimal intervention from me. So what we do
is we have, you know, a good team that models
factors in a way that is suitable for the investment
universe and style in which they operate. That's very very
sophisticated and difficult, and portfolio managers use that and then
(24:22):
neutralize it's it's become like second.
Speaker 1 (24:25):
Nature, and they've internalized that their goal is to see
us and credit alpha rather than factors. That's right, I feelink.
A criticism that people sometimes have of like the pod
shop model is that, like, there's some universe of factors
that exist in commercial models and like are known in
the literature, and then portfolio managers have a set of
exposures to factors that are sort of encoded or unknown,
(24:48):
but like, ultimately, when you become really, really smart, you'll
know that like actually the bet they're making was some
you know, particular knowing the company really well means like
they had exposure to like some you know, person factor
in the CEO or something that eventually someone will be
able to write that down and it'll come out of
like being idiosyncratic and become a factor, and then I
(25:09):
don't know what happens.
Speaker 4 (25:11):
I think that there is some truth to that. There
is definitely some truth to that, in the sense that
sometimes for folume managers, especially in specific sectors, will use
some heuristics that you could call characteristics in a factor model,
but they are not in a factor model, and then.
Speaker 3 (25:28):
They trade that.
Speaker 4 (25:30):
However, it's also true that the decision that enters a
particular investment is usually not that simple as taking a
ration spreadsheet, so it's a bit more complicated than that.
You could still argue that there is a factor, right,
and what's the factor is ultimately the set of thess
that are highly correlated or relatively highly correlated across portfolio
(25:54):
managers across firms, Because if there is an expected return,
and if you have skill, and you have sufficient skill
to be close to the best possible portfolio, you have
to be also relatively close to other people approximating that
best possible portfolio. Right, So then it becomes a truism. Right,
there is a factor and that's the factor of investor,
(26:15):
of informed investors. So it's true.
Speaker 1 (26:18):
I think it is like there's like a scientific process
that everyone is pursuing. I hire the best people, and
they like do the best work to pursue that scientific process,
and so like they'll eventually converge on something that is
like truth. But that means buying all the same stocks.
Speaker 4 (26:34):
Yes, it's very difficult to get to that truth.
Speaker 3 (26:37):
Sure, but yeah it's not. Let's let's tale.
Speaker 1 (26:43):
About it would be weird if they weren't hurting among
the best.
Speaker 3 (26:45):
Yes, yes, but there is there is.
Speaker 4 (26:47):
And by the way, this brings to one of the
limitations of factor models, right, which is effectively a factor
model is a form of glorified regression over time. Right,
And behind a regression there is a bit of an
assumption to some extent, of independent observations over time. And
the market and hedge funds are not in dependent random variables.
(27:12):
They are super dependent random variables, and they are in
a sort of continuous in direct conversation through their portfolios
and sometimes the conversation gets really nasty when one hedge
fund is in a state of distress and all of
a sudden, or not even a hatch fund, it could
be also an institution investor and decide to liquidate part
of their portfolio. And then it becomes a process where
(27:33):
you have a lot of reflexivity and positive feedback and
everybody suffers. And in this case, factor models don't really
You can still identify, like if the system is running
a temperature with some characteristics, but they are not factors
in the traditional sense.
Speaker 2 (27:51):
I do want to talk about before we move too
far away, I do want to talk a little bit
about how and if factors can die, because you know,
we've talked a bit about identify fying factors. But when
do you decide that this doesn't work anymore. Necessarily that
the market has fundamentally changed and this worked maybe ten
(28:11):
years ago, maybe fifteen years ago, but maybe now it's devolved.
Speaker 4 (28:19):
Well, there is the good old reason, which is people
make mistakes in the sense that we think that there
is a factor and then we look back and there
is no factor. Right, So there are so many factors
that some of them have got to.
Speaker 3 (28:34):
Be a little bit redundant.
Speaker 4 (28:36):
So that's one reason, right, So just pure in a
sense research revisions.
Speaker 3 (28:43):
And then there is.
Speaker 4 (28:43):
Also the fact that there are two other things that
can happen. One is the moment that you tell people
that there is a factor. The factor comes into being
to some extent, right, so it's never black and white
that the factor did not exist. Maybe the factor did exist,
and then the moment you identify it, it becomes more existent,
(29:04):
like as you know speak, yeah, yeah, So ESG is
one case where the focal point that it became makes
into an investable theme.
Speaker 2 (29:15):
I thought that was just black rock pumping as.
Speaker 4 (29:17):
Possible, but you know, but everybody had to incorporate it
in some sense, right, so it became a major source
of revenue for the vendors, right. So that's that's one thing.
And then there is the adaptive nature of the market.
So things that before generated a priced return, So you
run some risk, you made some money, and then it
(29:39):
becomes table stakes, it becomes incorporated into factor models, it
becomes it becomes a smart and and then it becomes
so I think, you know, you could say definitely that
medium tomamentum worked much better. You could say that even
you know, short term reversal worked better. There were years
when short interest was great, and there are factors or
(30:03):
data sources that work well now and then maybe in
five years will become known and become part of the
I mean credit card data right for consumer. That was
like there were people who were making a lot of
money in two thousand and eleven through I don't know,
sixteen seventeen, and then it's become it's very hard to
make money.
Speaker 1 (30:23):
In that you said the market is a conversation among
catch funds. One thing that I think might be true
that I'm not entirely sure of, is like, to what
extent the market is a conversation among four hedge funds? Now?
Like to what extent is like the marginal price or
of every stock a portfolio manager at you know, one
of the places you've worked.
Speaker 4 (30:44):
It's a very good question. I don't really have the
answer to this. I'm not sure.
Speaker 1 (30:49):
It's it's like, what is the intuition at places like that?
Like is it like the market price is determined by
like the collective thought of like the top people at
the top hedge funds, Or is it like we are
a little bump on the market and we're trading against
the whole random universe.
Speaker 4 (31:04):
I mean, you'd like to think that the prices are
determined by the marginal informed investor, right, so by people
like us at the time horizon where we predict, right,
which is not the same as at the time a
horizon of alpha day. Right, that's a different player.
Speaker 1 (31:20):
What is your time arising?
Speaker 4 (31:21):
Like I think of it as well, it depends well, yes,
it depends. Within a hatch fund, you have a variety
of even within long shot equities, you know, you have
you know, portfolio managers who are very tactical, and so
they think in terms of they have strong daily or
intra day alpha, even though they're fully discretionary up to
pms that think easily in terms of months. Also depends
(31:42):
on the sector. So you know, financials typically probably monetizes
a little bit less on earnings and tends to have
a longer horizon. Banks are basically modeling giant balance sheets, right.
And then in a hedge fund you also have systematic
but even in systematic there are also of time scales,
and this cacophony makes the prices. I really don't know,
(32:04):
Like I said, another question is basically, are how inefficient
is the market, how incorrect the prices are within a
factor of two, like Black used to say, or I
don't know, Like I don't think that the market is
becoming so super efficient, but it's getting it seems to
be more efficient.
Speaker 1 (32:21):
I do you feel like that. You know, one of
the big stories is the rise of like these big
multi strategy hedgehs. Like you would hope. Maybe you wouldn't
hope because it's sort of the economic and just, but
like one might hope that like the rise of these
big multi strategy hedgehus and a lot of capital being
allocated to them would observably make the market more efficient.
Speaker 4 (32:43):
Yeah, I don't know if observably holds. I don't it's
really hard to Like, can you can you tell when
a bubble is forming?
Speaker 2 (32:53):
A lot of people would say that they can.
Speaker 4 (32:56):
Yeah, I can point you to a few papers. Yeah
that you know made all the wrong calls. I don't
want to shame academics in public.
Speaker 2 (33:07):
I do like the idea that the market is a
conversation between four hedge funds because I live in the
ETF world, and you know, the big thing is passive
is just distorting the market, and there's no price discovery anymore,
and it sounds like that's on the opposite end of
that spectrum.
Speaker 4 (33:24):
I didn't say I think exactly that. It's a conversation
between It's a beautiful thing to say though. It sounds
really cool. It sounds good podcasts.
Speaker 3 (33:36):
That's great.
Speaker 4 (33:37):
Yeah, But I think your question is whether the rise
of passive has made markets less efficient more of a statement.
I don't think.
Speaker 2 (33:44):
I was a bad podcaster and didn't actually ask a.
Speaker 4 (33:47):
Question, But okay, how do you know?
Speaker 2 (33:50):
How do I know that passive is destorying the market?
People on Twitter tell me so.
Speaker 4 (33:55):
Oh, okay, don't trust people one number one.
Speaker 3 (34:00):
Number one, No, I don't know.
Speaker 4 (34:03):
I mean the rise of passive has made index rebalancing
a weirder strategy, right, so where the margins have compressed,
but the size has become so big that you can
still make money in it, and periodic. It's a very
you know, cyclical strategy.
Speaker 3 (34:19):
So I don't know.
Speaker 1 (34:21):
So if you're an indexy balancing PM, do take like
eight vacation a year?
Speaker 4 (34:27):
And not the ones I know who probably listen to
this podcast, they work very hard.
Speaker 2 (34:37):
They want to.
Speaker 1 (34:41):
Indexes aren't paying rence all the time, planning more than.
Speaker 4 (34:44):
You would think Index three balancing is another you know,
poster child for a strategy that seems so simple that
everybody can talk about it, and then it's full of
nuances and it requires a lot of skill to trade effectively.
Speaker 1 (34:59):
I believe, just because like I thought a little bit
about like just like the sort of like accounting of
like you basically know how many index funds there are,
let's say, can predict what will come in and out
of the index, and like what the so like there's
like some mechanics around, like you know, figuring out the
market calves that will come in and whatever, but then
(35:19):
it feels like the unknown is like who else is
doing the rebalancing strategy? Is that right?
Speaker 4 (35:23):
I think you're mostly right, because I don't want to say,
because you know, out of respect for for the CMS,
did I know fair enough?
Speaker 1 (35:33):
Yess? Like, So we had Cliff Askiness on a few
weeks ago, and like, to me, Cliff Asness is like
a quantitative investor, like a systematic investor, but like what
he's doing is sort of recognizably what a sort of
traditional asset manager. He's like trying to find companies that
are undervalued.
Speaker 3 (35:52):
Right.
Speaker 1 (35:53):
He talked about it's like being a Grammar dot investor.
You know you want like valuation plus a catalyst, and
he's like, oh, were you know trading you value and
momentum and like you look at what eight or two
is maybe a little different, but there's like you know,
the hypercacy trading firms, Like you can model those as
like those are quantitative versions of like a voice market
maker fifty years ago, where they're like trying to keep
(36:13):
inventory flat and like trying to you know, make the
bid ask spread. So like those are like very traditional
economic functions that have been quantified, like turned into systematic
what's the intuition for like what a bally asthe or
a sedad doll or a millennium does? Like what business
are you in? Do you think? Like as a philosophical matter,
(36:39):
like one thing I think like I think about.
Speaker 4 (36:40):
Like you're asking from a social Yeah, that's kind of point.
Speaker 1 (36:44):
Like I think like so the index rebalancing like to
me feels like the sort of trade and I think
to something that was the sort of trade that like
an investment bank would have done twenty years ago, thirty
years ago, and like some of that function I think
has moved to like the big multimanagers. But like I wonder,
like from where you say, like how you see that
role in the financial markets of those firms.
Speaker 4 (37:04):
So at a very high level, we don't do anything
different than everybody else in the sense that what we
provide is always this, right, is we provide shifting time preferences,
which means we provide liquidity, We house risk for people
who don't want to hold it right now. And that's
what you do when you do indextre balancing, right, That's
(37:26):
what you do when you do merger ARB and when
you do the various subtypes of basis traits.
Speaker 1 (37:31):
Right.
Speaker 4 (37:31):
So we do provide liquidity, which is very important. And
then the second thing we again very high level, we
provide price discovery. Right, So we study the firms and
we think, okay, this is at the margin mispriced and
we're going to short it or we're going to invest
in it, and that's a beautiful thing. So we do
it at a different time scale, right. So you always
(37:52):
want to do things at the margin where you don't
have a lot of other participants, and at the margin
of the let's say a month three month investment horizon,
there are not that many participants, so in the words
of another hatch fund manager I cannot name, but it said,
once you know, we don't invest in securities, we dated them,
(38:13):
and so we are in the dating service. Not that
many people are doing it, and so we do it.
But I would say also this right, not at the
social level. I just want to answer at the like
my personal level. What we do. We are a massive
filter of talent, and the talent that we hire is
a massive filter of information. So it's like information squared.
Speaker 1 (38:34):
Maybe this is like a bad question, but like, do
you think that like long only asset managers are worse
than they were thirty years ago because that filter has
been so successful? In other words, like there are lots
of jobs you could have gotten in finance in nineteen
ninety but like, yeah, there's like a clear hierarchy now.
Speaker 4 (38:53):
I think that the market and the set of investors
has learned right, and I think the distinction between beten
HALFA has been useful for investors and so active investors
who are mostly long only, I think have suffered from
this distinction because the vast majority of them underperforms their
(39:16):
benchmarks and so there is no reason for them to exist.
And then what we do is we provide really uncorrelated
returns to the benchmarks to most factors, and investors want that, right,
So there is a future where active investors, long on investors,
(39:36):
asset managers will become even less influential, smaller.
Speaker 1 (39:41):
And also I think of that as like a customer
demand side, but also like a talent filter side, right.
Speaker 4 (39:46):
Yes, Yeah, And then the interesting thing is and then
there is also a process where the multimanager platforms are
able to make the business model of a single portfolio
manager that is not sustainabil and isolation working in this
kind of federated system. So why would you or how
could you survive as a single portfolio manager hedge fund nowadays?
(40:10):
It's really really difficult, But you can do it in
a multi manager platform provided that you have you know,
sufficient talent, sufficient edge.
Speaker 2 (40:18):
That's also where you can blame the passive influence on Twitter.
If you're a long Enchey manager, then you know it's
impossible to be the market now because you just have
this money constantly pouring in.
Speaker 3 (40:27):
Yeah, I a don't disagree.
Speaker 1 (40:30):
Yeah, one more question, like social role is just like
you've worked at most of the big pod jobs, but
you also work at HRT, Like what's the difference in
roles and like what they do all day? Because HRT
I think of as a classic like high frequency treating firm,
where I don't know they're exactly a market maker, but
they're certainly on the higher frequency side. And then like
the pod shops have a lower frequency and a you know,
(40:52):
they're not prop they're running hedgephnes. Like what's the cultural
and role and difference?
Speaker 4 (41:00):
Yeah, okay, So I briefly mentioned HRT in an interview
with The Financial Times, and my manager told me that,
you know, people at HIT were both annoyed and delighted
by what I've had said about about HRT. I think
HRT is a really special place, even in the in
the context of proper training firms. So I'm a little
(41:21):
bit hesitant to just be in them in as a representative, right,
So they're not representative because there is something in the
culture of HRT that is special. Okay, it's collaborative, it's
truly kind.
Speaker 3 (41:40):
Yeah. So I think it's a great place.
Speaker 4 (41:42):
To work, and it is fundamentally monolithic, so you have,
you know, sharing of ideas and you can work at
the intersection of these ideas.
Speaker 3 (41:53):
It's also a place.
Speaker 4 (41:54):
That is very tech oriented, so it's a bit of
a technology firm or in the financial space, and because
of that, it also attracts I think, the best technical
talent that I've ever worked with. It's just a pleasure
to work with great technologies. People were very competent in
that respect. So nothing against the hedge funds. I love
(42:16):
edge funds for different reasons. You know, I love BAM,
which is also very collaborative and it's an investment company,
but HRT has as a technical side to it and
also gain a cultural side to it.
Speaker 3 (42:29):
It's great.
Speaker 2 (42:45):
We didn't talk about ai AI.
Speaker 3 (42:49):
Yeah, of course you have to talk about it.
Speaker 1 (42:51):
So I have like three models of how investment works systematic.
Like one is like you have like some economic intuition
and you build a model of the stock market that
predicts prices. In other way is a sort of like
neural netty ai E way, where like you throw a
lot of data at a neural net and it build
its own model of how to predict stock prices. And
then the third model is like you get really good
(43:14):
at prompt engineering and you get a chat GPT and
you say what stocks will go up? But you ask
it in the right way, and then chat GPT tells
me what stocks will go up? How good is it? Okay,
I assume the third model no one uses, but like
someone uses.
Speaker 4 (43:29):
I think a lot of people use that, all right,
So first thing, like, okay, nobody knows anything, and anybody
saying the opposite, you know, should be heavily discounted. Okay,
so we agree on this, and so let's forget for
a second all the technical details of AI just from
a pure industrial organization standpoint, Right, So what's going to happen?
(43:55):
Consider AI just like another technology like Internet and whatnot. Right,
So you know, first of all, we're going to observe
economies of scale, So there's going to be concentration, and
there was going to be some kind of monopolistic competition.
Speaker 3 (44:09):
I was thinking about Bloomberg.
Speaker 4 (44:11):
Specifically, which could be I hope for you people to
be among the winners, because you have a good starting point, right,
you have lots of data, right, you have a customer base,
and maybe in the future we'll finally not see the
good old Bloomberg terminal, which has been kind of unchanged
since I remember it, and instead people will just prompt
(44:34):
Bloomberg to conduct very complex actions where it will act
on a sequence of keywords and connect them and give you,
like a much more valuable product for which Bloomberg will
charge twice as much as they do already. So this
is going to happen in one form or another. If
it's not Bloomber, somebody else will do it. Okay, But
(44:55):
the same thing applies to other areas of finance. So
maybe once upon a time, you know, a big sufficiently
big fund could build their own client for email. Right.
Of course, nobody builds a client for email anymore. Right,
So a lot of this stuff gets outsourced. We will
outsource at some point some of the functions that we
(45:17):
conduct internally using AI to other AI agents. It's perfectly fine.
So this will become a utility to some extent.
Speaker 1 (45:27):
Yes, functions in clid like well.
Speaker 4 (45:30):
Not stockpicking, not stock picking. I think that the functions
that we will see available are essentially like another self,
like another mathlevin, who can you know be a good
baseline for you?
Speaker 3 (45:44):
Okay?
Speaker 4 (45:45):
You could feed a post train an AI system with
all your gazillions of words, right, and that agent will
reproduce your sense of humor, your investigative style and everything Okay,
it's a good approximation. It's not going to be perfect,
but why not, right, So I would be very happy
to have a replica of myself that can answer most
(46:07):
simple questions. Now, I think that the decision to invest
in a particular stock is a very demanding cognitive function,
and I don't see that really being replicated very well.
But I think that this will be baselined to some extent.
Speaker 1 (46:21):
Is it a many kind of function because because it
exists in a competitive market, So like the sort of
like whatever the kind of function is, is going to
get like the baseline is always going to get higher
because like someone else will will have the same information
as you do or the same.
Speaker 4 (46:36):
Well, this is getting really in the highly speculative side
of you know things. I think that in order for
an AI agent to be good at this, they have
to be able to experience the world the same way
that an investor experiences it, and our inputs are much
more complex than just a string of text or YouTube videos.
Speaker 1 (46:58):
Right.
Speaker 4 (46:59):
We have a the world which comes from visually experiencing
the world, talking to humans, consuming the goods, right anything,
It's vastly more complex than the way an AI system
Right now, experiences the world and also influences the world.
So an investor has a fundamentally different experience of a company.
Speaker 3 (47:21):
Than an LM that has.
Speaker 4 (47:23):
An experience that is mediated by multiple layers of processing.
You know, they learn about a company through text that
is written by somebody. So I don't think that that's
in danger for the time being. But maybe, you know, again,
in five years, maybe we will have our glasses feeding
our experiences to AI agents. Who knows, right, But I
don't think that it's that close, And I don't think
(47:47):
AI is that smart also, so I think that having
a baseline system would be already pretty good.
Speaker 2 (47:52):
That's somewhat comforting that our experiences count for something, our
physical experience of the world.
Speaker 1 (47:58):
It's interesting. I always think of like the comparison is
like investing in self driving cars, or like investors do
a lot of things. Like one thing they do a
lot is sit at a desk and read computers and
like look at numbers, right, and like those things seem
like things that a computer can do well, whereas like
you know, drivers like have physical reflexes and like have
a you know, complicated field division. I always thought like
(48:22):
investing should be easier than self driving cars for a
computer and a master. But you and I think you're
learning this. Think of like investing as like the great
liberal art, where it's like you incorporate all of human
experience and so the AI can't.
Speaker 4 (48:34):
Really Okay, let's let's take the metaphor to you know,
extreme consequences. Imagine that you had a system that is
the equivalent of a perfect self driving car in investing.
Speaker 3 (48:44):
So now I'm.
Speaker 4 (48:45):
Giving you a machine, a box that is telling you
the long term value, if not the returns, right, because
the moment that the value is known, you immediately equilibrate
to that level.
Speaker 1 (48:57):
Right.
Speaker 4 (48:57):
So imagine that you know the true value of everything
because a box tells you so, and it's infallible.
Speaker 3 (49:02):
It's an oracle. Okay.
Speaker 4 (49:04):
Would you think that finance stops existing? I wouldn't say so, right,
So I think that a lot of arbitrush trades, you know,
would maybe change significantly, but every risk, right, every return
would be correctly priced by the risk of the agents
trading it. So there still would be trading because we
still have different preferences, but basically every risk could be priced.
(49:28):
There would be in a sense less alpha. But finance
will still exist. It's a lot of service provision like
liquidity provision. Yeah, and so the liquidity provision would still exist.
The informational services maybe will stop existing in the current.
Speaker 3 (49:43):
Form, but that's okay.
Speaker 4 (49:44):
I think that we'll all still be employed.
Speaker 2 (49:47):
Mm hmm.
Speaker 1 (49:48):
It's an interesting I think about it, because I do think,
like we talked about, like, one thing that the big
hedgehunds to do is things that have the flavor of
liquidity provisions basis trades and merger and whatever. Things that
like I think of as like something that a bank
would have done thirty years ago, and then now a
big hedge one does. And then another thing they do
has the flavor of information provision, where it's getting prices right.
(50:11):
Like to me, those things seem quite intellectually separate, but
I guess they feed each other in the sense that
the better you are prices, the better you can be
at liquidity provision. Is that sort of right? You didn't
know the value of the Yeah.
Speaker 4 (50:27):
I mean a short, short horizon. Liquidity provision and information
tend to be very closely rated, Like you know, a limit.
If you are good at crossing, even good at crossing,
you should be pretty good at adding okay, adding liquidity,
so you know, but I mean like you could make
you know, a profit by posting a lot of limit
(50:48):
orders and providing liquidity to the market, or crossing the
spread and making money with predicting the future prices. If
you're good at one, you're good at the other. Most likely,
right at that time scale, I think that this though
might I'm not sure because I haven't thought about this
very very carefully, but I think this might be coupled
at longer time scale, so you know, you're when you're out.
(51:11):
I'm not sure. And in any case, at that time
scale is really difficult for an AI or for a
human being anyone, Like, there are not that many hard data,
even the unstructured data are not that many. So it's
a very difficult problem. It's the coupled it's it's complicated.
So yeah, but I tend to believe at longer time
(51:32):
scales you have more or less liquidit provisioning and you know,
violations of law of one price on one side and
predicting on the other side.
Speaker 1 (51:43):
But you combine both.
Speaker 4 (51:45):
But you can combine both, and it's a very potent mix.
Speaker 1 (51:48):
Right, there's normally different people.
Speaker 4 (51:49):
It is right, very different people for sure, different, very different,
very different people, very different cultures.
Speaker 1 (51:56):
Yeah, can you summarize the difference in cultures between like I.
Speaker 4 (51:59):
Have, I guess, but well, as you said, people who
typically trade in ARB trades, if not historically, but also
historically come from banks. Right, whereas you still can see
long only portfolio managers being recycled and reformatted into long
short portfolio managers, you can have an excellent short specialist
(52:23):
becoming a long short portfolio manager, like it happened.
Speaker 1 (52:27):
I mean my sense is that like that, people on
the information version long short sider more academic and research oriented.
In the people on the ARB side are more.
Speaker 4 (52:40):
Yeah, I think you can actually have very good long
short portfolio managers who were journalists in their past lives.
Speaker 1 (52:47):
I've heard of some of these. I thought about it, No, just.
Speaker 4 (52:51):
Like real.
Speaker 2 (52:55):
Breaking news on your podcast.
Speaker 1 (52:58):
I've jumps that's better than podcasting. Not thought about it
in the sonth that I'd be good at it, just
in the sense that the money is good.
Speaker 2 (53:08):
You could be bad at it and paid really well
for a short amount of time.
Speaker 1 (53:12):
I don't know that that's true. Actually, they're they're an
excellent talent filter or so I hear.
Speaker 4 (53:19):
Yes, I think that you could interest a few huge funds.
They might be listening. On a note, Kathy, thanks for
coming on the pleasure, Thanks for having me.
Speaker 1 (53:43):
And that was the Money Stuff Podcast.
Speaker 2 (53:45):
I'm Matt Levian and I'm Katie Greifeld.
Speaker 1 (53:47):
You can find my work by subscribing to The Money
Stuff newsletter on Bloomberg dot com.
Speaker 2 (53:52):
And you can find me on Bloomberg TV. Every day
on Open Interest between nine to eleven am Eastern.
Speaker 1 (53:57):
We'd love to hear from you. You can send an
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Speaker 2 (54:04):
You can also subscribe to our show wherever you're listening
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people find the show.
Speaker 1 (54:10):
The Money Stuff Podcast is produced by Anna Maserakus and Moses.
Speaker 2 (54:13):
Onam Our theme music was composed by Blake Maples.
Speaker 1 (54:16):
Brandon Francis Newdhim is our executive producer.
Speaker 2 (54:19):
And Stage Ballman is Bloomberg's head of Podcasts.
Speaker 1 (54:21):
Thanks for listening to The Money Stuff Podcast. We'll be
back next week with more stuff
Bloomberg Audio Studios, podcasts, radio news, Happy Thanksgiving.
Speaker 2 (00:13):
I hope you're having a great holiday. We are obviously
not recording on this Thursday, so we're.
Speaker 1 (00:18):
Gonna rerun a favorite old episode. It's going to be great.
Speaker 2 (00:23):
You're going to enjoy it even more the second time. Bye.
Speaker 1 (00:30):
Wait gay palio logo or.
Speaker 2 (00:36):
I'm so excited for you to tackle that and not me.
Speaker 3 (00:38):
Good luck.
Speaker 2 (00:39):
I thought I had it down, but then I heard
you say it, and I feel like.
Speaker 1 (00:43):
When I first met you, I think I asked you
if you go by gappie because of your famous track
record of taking gardening leave, like having gaps in your
career as well.
Speaker 4 (00:58):
Okay I didn't remember. Yeah, that's a great excuse for
for a nickname. No, But the reason is when I
came to the States for grad school, and this was
a long time ago, in ninety five. So the first
thing that you did was set up an email account.
You still had the freedom to choose an Emil account.
(01:19):
Now they just give you your initials with the number,
and so my initials are gap Gapo, and of course
it was taken. So I said, okay, well Gappy, and
then everybody in grad school and then my wife was Italian,
everybody started to call me Gappy and that's stuck.
Speaker 3 (01:40):
And now at.
Speaker 4 (01:40):
Work they just have dispensed with my real name, like
on all systems, I'm just Gappy Paliologo.
Speaker 3 (01:46):
So I expect that that will be, you know.
Speaker 4 (01:49):
Prosecuted for tax evasion because on my tax forms there
is Gappy Paliologo or something like that.
Speaker 1 (01:55):
Well, hello, and welcome to the Stuff podcast.
Speaker 2 (01:58):
I'm at Livian and I'm Katie Greifeld.
Speaker 1 (02:01):
And we have a guest today, Gabby Paleo Logo, who
is now at pali Asney has been at most of
the other pig Catch funs and Hudsond River Trading. I
do want to start by talking about.
Speaker 2 (02:15):
Gardening there, Okay, Natural Purse.
Speaker 1 (02:19):
I think that we counted from your link. Your LinkedIn
is like famous for discussing your gardening leave in some detail,
and I think we counted three years of gardening leave.
Speaker 4 (02:30):
No, I think it's a bit like Okay, it's not precise.
Fifteen months from Citadel, one year Hudson River Trading, and
four months from Millennium. Okay, so pretty close, not terrible,
though a bit less than two years.
Speaker 1 (02:44):
From my perspective, it seems very fun. Did you enjoy
your three years of gardening?
Speaker 3 (02:50):
I do so.
Speaker 4 (02:52):
I try to keep myself busy, so I teach, typically
at some university. So the first time during my Seitaadel
two millennium, Guardian Leve I was teaching at Cornell and
in the HRT to Bam Guardian Leve was at NYU.
And I love teaching. And then what I do is
(03:13):
it helps me focus on stuff. Usually what I do
in you know, whenever I read a book or read
a paper that I like, I take notes, take notes
in lattech, and then I read, arrive or think about things,
and so that typically is the basis for my course material,
(03:34):
and then it becomes the basis for my books. I've
written a couple of books during my non competes.
Speaker 2 (03:39):
Interesting because thinking about gardening leave. Matt and I talk
about it all the time, because it's very alluring to me.
Gardening leave doesn't really exist in journalism. I love to
imagine what I would do. But one of the questions
I had for you was, you know, do you ever
have anxiety about losing your edge or falling behind? But
it sounds like teaching is one of the ways that.
Speaker 3 (04:00):
Yeah, I'm not particularly worried with that.
Speaker 4 (04:03):
I think that there is only a very specific subset
of quantitative researchers who are afraid of losing their edges.
And yeah, that's not been my case. I keep reading.
I try to stay up to date.
Speaker 1 (04:15):
To the feedback into the work. Like do you get
ideas or like deep in your understanding of techniques by
teaching and writing the books? Or are they just sort
of like.
Speaker 4 (04:26):
Extracurricular No, no, no, it's definitely I learn a lot
from writing the books.
Speaker 1 (04:31):
How long do you I get to hear your next job?
And yeah, generate more profits by of.
Speaker 3 (04:35):
Course, plenty more profits. Sell that to my employers.
Speaker 4 (04:38):
No, but I definitely I learn a lot from writing
from the first drafts, and then I rewrite and rewrite,
and I learn a lot from discarding material too. It's
very useful to discard material. It makes you really focus
on what matters and what doesn't. So I try to
give a narrative, like a logical connection between various topics,
(05:00):
and that is something that is possible only when you
write a book. I really do not like writing that.
Nobody I think likes writing, maybe except for you.
Speaker 1 (05:11):
I I think I understand that it's weird even among writers, but.
Speaker 4 (05:15):
It is very I find it very painful. I find
painful letting go of material, Yes, but I also like it.
You know, it's some kind of strange delayed gratification.
Speaker 1 (05:27):
I guess one theory that I have written is that
hedge funded quantitative research gardening leave is like a source
of like human flourishing, because you have all these like
highly trained people who haven't enforced the year of And
I've written that all the Hedge fund researchers should go
work at LM companies or like analytics departments of sports teams.
(05:52):
And I'm like, partially kidding and partially not. How true
is it for you? Like how much of like your
quantitative skills at this point are really just for investing in?
How much of it is like if you spent three months,
you know, consulting for a soccer team, you would be
(06:12):
able to tell them how to find better players.
Speaker 4 (06:17):
I'm not sure, so I'll say this right. I was
thinking a few days ago if there was a kind
of a common thread in my professional life because it
seems kind of random, And actually I think that there is,
because I think that I was about fourteen when I
realized that I had an aptitude for applied math. I
(06:38):
discovered physics, and I liked math, and I also liked
literature very much, so I loved reading. I read a lot.
I was not a very social animal. And then basically
since then, I've been doing the same thing in various forms. Right,
I did physics, I did applied math. I didn't do
(06:59):
applied math in finance. They did applied math in weird
things like optimization and logistics. So I have been doing
kind of the same thing over and over, which has
been writing and applying math to something. So I think
that I could do it. I would like to do it,
but I also think that it's not that simple to
(07:21):
go to a new field and say, oh, after three months,
I know soccer. No, there is a lot of specificity,
And the beauty of I think being a good applied
mathematician is that they start with the problems and with
the domain first, and that they're sufficiently mature from a
mathematical standpoint that they are not making too much of
(07:43):
an effort in using math. So I think the good
art of being an applied mathematician is to study persistently
the application. So no, I don't think that after three
months it would be good enough. But after a year,
you know, about a year of being fully in an application,
then you start getting a little bit better and then
(08:05):
the mass is not the problem, and then you start
doing some good work.
Speaker 1 (08:09):
You have a famous essay on like advice for quant
careers and you say that, like, the things that matter
the most are creativity and genuine interest in the problems
more than you know, math course power. Yeah, this is
a dumb question, but how does one develop? How does
one identify you know, creativity and interest in financial topics?
(08:33):
And is the obvious answer those are where the money is,
or like like why why did you fall in love
with finance as a topic? And is the answer because
that's what the money is.
Speaker 4 (08:42):
So first of all, I think that creativity is either
personality trait doesn't belong to You're not creative in finance,
you know, you're you're creative in in cooking, you're creative
in whatever. And it's a mix I guess of extraversion
open and as to experience, and I don't what else.
I'm not a psychologist, but I do believe that people
(09:03):
are genuinely creative, and in fact, you see it right
that sometimes you ask someone and you find out that, yes,
they like writing, they play some instrument, if badly, and
you know, and they paint and they do whatever. And
so I would say, if you go to finance because
that's where the money is, there's nothing wrong with that.
(09:24):
And in a way, that's my story. You know, I
was I was a researcher, and I wanted to have
more money and whatnot. But eventually you stay in finance,
or at least in my you know, little domain, because
you're genuinely curious about finding out stuff, right.
Speaker 1 (09:41):
So, like why are the problems like why do they
arisk curiosity? Like why are the problems of finance intrigue
you after years of doing it right? Like what's interesting
about those problems as opposed to other domains.
Speaker 4 (09:54):
It's really hard for me to say, Like I think
that I read once that a young songwriter asked Bob
Dylan how to become a good songwriter, and bub Bylan
just answered, well, what's going on? What do you mean,
what's going on? Yeah, what's going on? What's going on
in your life? Just you know, look around. So sometimes
I get these questions from investors or but you know,
(10:15):
how do you keep yourself interested. How do you find problems?
It's not a problem like the problems jump at you
like there are too many problems. There are too many
interesting problems. So if anything, the skill is in sorting
the problems in the right order. Right, That is where
maybe having some maturity in doing research kicks in. But
(10:36):
there are lots of problems, infinite problems, weird problems.
Speaker 1 (10:41):
What's your favorite problem right now?
Speaker 4 (10:43):
I don't like right now, what are we working on?
I mean, we are trying to understand how earnings are monetized? Right,
how do you make money in earnings? It's such a
basic thing in fundamental equities.
Speaker 1 (10:54):
And you mean, if you're like correct about predicting earnings?
Speaker 4 (10:58):
Yes, what are I mean? Without get into much into details,
but you know there what are the relevant variables? Imagine
that you had an oracle who told you what the
variables are? What would you do with that? What would
you do if you'd had all the information in the world, right,
and everything in your world here in existence? Would be
like an approximation problem.
Speaker 1 (11:18):
There's there's an incredible styled story of like the guys
hacked into I think like one of the newswire services
and got earnings releases early, like for hundreds of companies,
and they traded on this and they had like a
seventy percent success rate, which is great, but also like
it means that had a thirty percent, Like they traded
the wrong way, knowing earnings perfectly in advance. It's like
(11:40):
a good.
Speaker 4 (11:41):
Yeah, yes, so they had the racle on it. You know,
it's still hard, Yes, it's still very hard. Actually, shout
out to Victor Hagan, who wrote the paper about ten
years ago on this. He made a organize a simple
controlled experiment where he gave basically a biased coin where
you I think had a success rate of sixty percent
forty percent failure, and you some capital and you could
(12:01):
invest it over time on these informed predictions, and a
lot of subjects went bankrupt. Okay, now I think we
are better than that, but still there are lots of
problems related to trading around an event. For example, before we.
Speaker 2 (12:17):
Get too far away, you mentioned Bob Dylan. It actually
reminded me of another Bob Dylan quote which I'm going
to paraphrase poorly, but he basically said, when asked about
writing songs, do you think that you could write whatever
the work that was being referenced now, and he said,
I don't think so. It's like the words were in
the air and I just plucked them out. They were
(12:38):
just sort of hanging in the air and they came
to me. And it kind of also rang true with
what you were saying about you didn't go looking for problems.
They're just there necessarily. I actually want to go back
to applied math if it doesn't interrupt the course of
conversation too much. You tweeted on June twenty fourth that
there's no child prodigies when it comes to poetry, when
(12:58):
it comes to applied mathematics. And I'm not saying that.
You said that you were a prodigy, but you were
a child at fourteen. I mean, how how fourteen do
you realize that you have an aptitude for something like
applied mathematics.
Speaker 4 (13:13):
I don't want to flex about this stuff, No you should.
I think I'm honestly a little weird. I'm just a
little weird, I think, honestly.
Speaker 2 (13:20):
But I like prodigy weird or I did.
Speaker 4 (13:23):
Have my share of yeah, adults telling me that I
was good at this or that or you know, but yeah,
I mean, okay.
Speaker 3 (13:31):
I'm just a little bit atypical.
Speaker 4 (13:33):
Also, when I talk to investors, I think investors enjoying
my presence because I think I'm incredibly unfiltered for somebody
who's talking to.
Speaker 3 (13:43):
Them, so it's like fun for them.
Speaker 4 (13:45):
And I was very unfiltered when I talked to my
professors in school. Sometimes I corrected them stuff like this. Yeah,
I don't know, Honestly, I don't know.
Speaker 1 (13:56):
When you talk to like fundamental equity portfolio managers, like
how much like matrix algebras they're in your conversations like
how quantity are the fundamental pms or whatever.
Speaker 4 (14:09):
I don't think they're quantity, but I think that they're
very analytical. So I don't think that they would make
great mathematicians, but I think they would make very very
decent applied mathematicians. Actually, they tend to be very analytical.
They tend to be very process oriented. And they have
also additional qualities that actually mentioned in that essay, like
(14:31):
they have very little disposition effect, so that's part of
being analytical. They have no sound cost fallacy in them.
So even though they don't do a lot of math,
but they do some math. Okay, So first of all,
they're fluent in a sense in basic literacy, but I
think it's more their process that is closer to if
not a mathematical one, but more of a scientific one.
Speaker 2 (14:55):
And when it comes to being a quant does it
basically boil down to being good at math and being
interested in math? Are things such as statistics and physics?
I mean, do you need to have any finance or
economics background at all?
Speaker 4 (15:11):
So I think that having an economics background is not
necessarily a benefit, might even be a disadvantage actually, But
just based on very few samples that I have a
lot of very good, outstanding quantitative researchers actually come from
physics and specifically from astrophysics. That's the experience that I've
(15:32):
had in a couple of places.
Speaker 2 (15:34):
In broad brushstrokes, could you talk about why economics in
the small sample size you have, how could that possibly
be a detriment good?
Speaker 4 (15:45):
So I can answer the second question more easily. I
think that astrophysicists deal with large amounts of data, and
they deal with observational data, so they don't get to
do a lot of experiments, and that's good for finance. Right,
you deal with a lot of data, you need to
know how to have good agen for observational data, and
(16:05):
you need to have very good theory, like you need
to have very good instruments without being falling in love
with those instruments. Whereas I think economists, Okay, first of all,
my statement is purely empirical. Okay, so I'm just really
guessing on economists, and I'm going to be hated by
all economists or economists in finance, but I do have
(16:30):
my issues with their methods.
Speaker 3 (16:32):
Right.
Speaker 4 (16:32):
So, first of all, I think that there is an
original scene in economics, which is I think a lot
of economics is informed by a desire to be as
rigorous as mathematics, right, and so a lot of theoreticians
in economics are very deductive in their approach. If you
think of you know, the unrealistic assumptions behind the welfare
(16:55):
theorems or Rows impossibility theorem or whatnot, or just pick
up you know Samuelson textbooks, and I think this is
just rather very acxiomatic.
Speaker 3 (17:07):
Very deductive.
Speaker 4 (17:08):
Whereas physicists are very happy to think in terms of small,
idealized models that apply to a specific domain, and if
the model doesn't work out, they will discard and make
another one. The grand theory behind physical theories exists, like,
there are people who do this for a living, but many,
(17:28):
many good theoretical economists physicists starting the small and then
they expand the domain of their models. So economists tend
to maybe in a sense, fall in love with methods
too much, with techniques too much.
Speaker 1 (17:55):
We had clip Asness on the podcast a little while ago,
and my father, not a finance listened to the episode
and said, I still don't know what a quant is.
I just read skimmed your new book which is called
The Elements of Quantitative Investing, and as lays out the elements,
what is a quant like? What are the elements? Like?
(18:15):
What's the thing that makes someone a quant investor that,
like someone reading a slim book about the Elements of
quant investing needs to learn?
Speaker 4 (18:23):
Well, if I am being consistent with my book, investing
is really about problems and not about specific techniques or
anything like this. Right, So it's basically a way to
go through the whole investment process from let's say preparing
the ingredients to cooking to eating that is very processed driven. Ultimately,
(18:43):
you would imagine that one thing that you know quantu
investing has in common across multiple domains you know, if
you do futures, stocks, event based and whatnot. Is I
think the number of bets tends to be high in
systematic right, so you can be a very successful microeconomic
(19:04):
investor portfolio manager. And you you know, according to even
several statements by Buffett, you know, he made like ten
twelve very good bets. Okay, so that's great, and that's
not quantu investing. You know, you could put enough pms
making you know, twenty bets in their lives and you
will get a few that have let's say twelve thirteen right,
(19:26):
and they will be rich. We do not have that luxury, right.
We have to make millions of bets. You know, we
trade a portfolio with three thousand stocks sometimes in waves
of half an hour. You can't make a judgment on
all of these bets. So you need a method that
reduces the dimension of your problem to something that can
(19:46):
be treated in a systematic manner. I don't know if
that answers for you. You know that, but you know, basically,
basically the idea is, think about if you make a
lot of bets, you cannot bet individually. You have to
have some kind of view res take or some kind
of method around that.
Speaker 1 (20:01):
Right, and like to me, like the book sort of
you know the standard method I guess and quant investing
is you built a factor model of what drives your
universal investments. You're shaking your head.
Speaker 4 (20:13):
Yeah, I yes, and no. I think yes because the book,
you know, has maybe one hundred and fifty pages on
factor models.
Speaker 3 (20:19):
But also no, because maybe.
Speaker 4 (20:22):
In one hundred years from now, I suspect there will
be still something left. But you know, we might have
better techniques and not necessary factor models any longer. I
don't know, we.
Speaker 1 (20:32):
Don't want to go. Two attractions of that one is like,
are the better techniques something more neural netty unstructured?
Speaker 4 (20:40):
Who knows? Yeah, something like that. I mean there is,
there is a revolution every five years.
Speaker 1 (20:44):
So my other question is like I've never fully understood
like a factor model is like, here are some factors
that drive the returns of stocks, and then there's like
some residual idios and credit return There are clearly people
(21:04):
whose business is to identify factors and then invest in factors.
My impression is that at like the places that you work,
the business is the opposite of that is to pedge
out your factory risk as much as possible and to
get as much idiotsyncratic risk as possible. Is that right?
And like, like, how do you discriminate between like a
(21:26):
factory return and idiosyncratic return, Like what makes the thing
a factor as opposed to another thing.
Speaker 3 (21:30):
So that's a good question.
Speaker 4 (21:32):
So first, a lot of systematic investing is still about factors,
just not the factors that get published in the literature,
you know, not the factors that Cliff maybe was talking about.
And yet a lot of successful systematic investing is really
factor driven, see in.
Speaker 1 (21:48):
The sense that you have a model that has like
twenty factors and like ten are like value, and you
neutralize those and you try the other time kind.
Speaker 4 (21:56):
Of you do, and you do the rest. You have
other terms that matter. So that's one thing, but there
are two other things. There are sometimes sources of returns
that are factor like but not quite like factors. So
you may have a theme. For example, you may identify
a theme in the market that is not pervasive enough
(22:19):
or is alive only for a few months, but it
is there and it's not only affecting let's say two stocks, right,
So these brought themes can be invested on, but cannot
really model in the traditional way as a traditional factor model. Also,
there is a lot of good modeling in factors as
opposed to bad modeling. So it seems easy, but it's
(22:40):
not that easy. So there is a little bit of
craftsmanship in making these models. Okay, And then the third
thing is that there are also returns that have nothing
to do with factors, or almost nothing to do with factors.
So if you really know how a company works, and
you have a little bit of an edge in predicting
its future performance, and you can bet on it, and
(23:02):
you make enough bets and again you will make some
money if you repeat, and you know recycle. So even
discretionary investing in this sense has inherited a little bit
of the spirit of systematic investing.
Speaker 1 (23:15):
I think of that as like that apod job, but
like a baliosni Like you have discretionary investors who know
a lot about a company, make bets on the company,
and then someone like you tells them, these are your
factory exposers. You have to get those down to zero
that you're making pure bets on your idios and creditnowledge
of the company. Is that like you're kind.
Speaker 3 (23:36):
Of right, kind of right.
Speaker 4 (23:37):
Yeah, I think that at this point, it is very
interesting how the mind of professional portfolio managers has been
remolded in a factor based world, so that a modern
portfolio manager, discretion portfolio manager thinks in factors. You know,
so I don't even need to tell them, hey, this
(23:58):
is your exposure. They see their exposure, they have the
tools to see it, and they control it in real
time with minimal intervention from me. So what we do
is we have, you know, a good team that models
factors in a way that is suitable for the investment
universe and style in which they operate. That's very very
sophisticated and difficult, and portfolio managers use that and then
(24:22):
neutralize it's it's become like second.
Speaker 1 (24:25):
Nature, and they've internalized that their goal is to see
us and credit alpha rather than factors. That's right, I feelink.
A criticism that people sometimes have of like the pod
shop model is that, like, there's some universe of factors
that exist in commercial models and like are known in
the literature, and then portfolio managers have a set of
exposures to factors that are sort of encoded or unknown,
(24:48):
but like, ultimately, when you become really, really smart, you'll
know that like actually the bet they're making was some
you know, particular knowing the company really well means like
they had exposure to like some you know, person factor
in the CEO or something that eventually someone will be
able to write that down and it'll come out of
like being idiosyncratic and become a factor, and then I
(25:09):
don't know what happens.
Speaker 4 (25:11):
I think that there is some truth to that. There
is definitely some truth to that, in the sense that
sometimes for folume managers, especially in specific sectors, will use
some heuristics that you could call characteristics in a factor model,
but they are not in a factor model, and then.
Speaker 3 (25:28):
They trade that.
Speaker 4 (25:30):
However, it's also true that the decision that enters a
particular investment is usually not that simple as taking a
ration spreadsheet, so it's a bit more complicated than that.
You could still argue that there is a factor, right,
and what's the factor is ultimately the set of thess
that are highly correlated or relatively highly correlated across portfolio
(25:54):
managers across firms, Because if there is an expected return,
and if you have skill, and you have sufficient skill
to be close to the best possible portfolio, you have
to be also relatively close to other people approximating that
best possible portfolio. Right, So then it becomes a truism. Right,
there is a factor and that's the factor of investor,
(26:15):
of informed investors. So it's true.
Speaker 1 (26:18):
I think it is like there's like a scientific process
that everyone is pursuing. I hire the best people, and
they like do the best work to pursue that scientific process,
and so like they'll eventually converge on something that is
like truth. But that means buying all the same stocks.
Speaker 4 (26:34):
Yes, it's very difficult to get to that truth.
Speaker 3 (26:37):
Sure, but yeah it's not. Let's let's tale.
Speaker 1 (26:43):
About it would be weird if they weren't hurting among
the best.
Speaker 3 (26:45):
Yes, yes, but there is there is.
Speaker 4 (26:47):
And by the way, this brings to one of the
limitations of factor models, right, which is effectively a factor
model is a form of glorified regression over time. Right,
And behind a regression there is a bit of an
assumption to some extent, of independent observations over time. And
the market and hedge funds are not in dependent random variables.
(27:12):
They are super dependent random variables, and they are in
a sort of continuous in direct conversation through their portfolios
and sometimes the conversation gets really nasty when one hedge
fund is in a state of distress and all of
a sudden, or not even a hatch fund, it could
be also an institution investor and decide to liquidate part
of their portfolio. And then it becomes a process where
(27:33):
you have a lot of reflexivity and positive feedback and
everybody suffers. And in this case, factor models don't really
You can still identify, like if the system is running
a temperature with some characteristics, but they are not factors
in the traditional sense.
Speaker 2 (27:51):
I do want to talk about before we move too
far away, I do want to talk a little bit
about how and if factors can die, because you know,
we've talked a bit about identify fying factors. But when
do you decide that this doesn't work anymore. Necessarily that
the market has fundamentally changed and this worked maybe ten
(28:11):
years ago, maybe fifteen years ago, but maybe now it's devolved.
Speaker 4 (28:19):
Well, there is the good old reason, which is people
make mistakes in the sense that we think that there
is a factor and then we look back and there
is no factor. Right, So there are so many factors
that some of them have got to.
Speaker 3 (28:34):
Be a little bit redundant.
Speaker 4 (28:36):
So that's one reason, right, So just pure in a
sense research revisions.
Speaker 3 (28:43):
And then there is.
Speaker 4 (28:43):
Also the fact that there are two other things that
can happen. One is the moment that you tell people
that there is a factor. The factor comes into being
to some extent, right, so it's never black and white
that the factor did not exist. Maybe the factor did exist,
and then the moment you identify it, it becomes more existent,
(29:04):
like as you know speak, yeah, yeah, So ESG is
one case where the focal point that it became makes
into an investable theme.
Speaker 2 (29:15):
I thought that was just black rock pumping as.
Speaker 4 (29:17):
Possible, but you know, but everybody had to incorporate it
in some sense, right, so it became a major source
of revenue for the vendors, right. So that's that's one thing.
And then there is the adaptive nature of the market.
So things that before generated a priced return, So you
run some risk, you made some money, and then it
(29:39):
becomes table stakes, it becomes incorporated into factor models, it
becomes it becomes a smart and and then it becomes
so I think, you know, you could say definitely that
medium tomamentum worked much better. You could say that even
you know, short term reversal worked better. There were years
when short interest was great, and there are factors or
(30:03):
data sources that work well now and then maybe in
five years will become known and become part of the
I mean credit card data right for consumer. That was
like there were people who were making a lot of
money in two thousand and eleven through I don't know,
sixteen seventeen, and then it's become it's very hard to
make money.
Speaker 1 (30:23):
In that you said the market is a conversation among
catch funds. One thing that I think might be true
that I'm not entirely sure of, is like, to what
extent the market is a conversation among four hedge funds? Now?
Like to what extent is like the marginal price or
of every stock a portfolio manager at you know, one
of the places you've worked.
Speaker 4 (30:44):
It's a very good question. I don't really have the
answer to this. I'm not sure.
Speaker 1 (30:49):
It's it's like, what is the intuition at places like that?
Like is it like the market price is determined by
like the collective thought of like the top people at
the top hedge funds, Or is it like we are
a little bump on the market and we're trading against
the whole random universe.
Speaker 4 (31:04):
I mean, you'd like to think that the prices are
determined by the marginal informed investor, right, so by people
like us at the time horizon where we predict, right,
which is not the same as at the time a
horizon of alpha day. Right, that's a different player.
Speaker 1 (31:20):
What is your time arising?
Speaker 4 (31:21):
Like I think of it as well, it depends well, yes,
it depends. Within a hatch fund, you have a variety
of even within long shot equities, you know, you have
you know, portfolio managers who are very tactical, and so
they think in terms of they have strong daily or
intra day alpha, even though they're fully discretionary up to
pms that think easily in terms of months. Also depends
(31:42):
on the sector. So you know, financials typically probably monetizes
a little bit less on earnings and tends to have
a longer horizon. Banks are basically modeling giant balance sheets, right.
And then in a hedge fund you also have systematic
but even in systematic there are also of time scales,
and this cacophony makes the prices. I really don't know,
(32:04):
Like I said, another question is basically, are how inefficient
is the market, how incorrect the prices are within a
factor of two, like Black used to say, or I
don't know, Like I don't think that the market is
becoming so super efficient, but it's getting it seems to
be more efficient.
Speaker 1 (32:21):
I do you feel like that. You know, one of
the big stories is the rise of like these big
multi strategy hedgehs. Like you would hope. Maybe you wouldn't
hope because it's sort of the economic and just, but
like one might hope that like the rise of these
big multi strategy hedgehus and a lot of capital being
allocated to them would observably make the market more efficient.
Speaker 4 (32:43):
Yeah, I don't know if observably holds. I don't it's
really hard to Like, can you can you tell when
a bubble is forming?
Speaker 2 (32:53):
A lot of people would say that they can.
Speaker 4 (32:56):
Yeah, I can point you to a few papers. Yeah
that you know made all the wrong calls. I don't
want to shame academics in public.
Speaker 2 (33:07):
I do like the idea that the market is a
conversation between four hedge funds because I live in the
ETF world, and you know, the big thing is passive
is just distorting the market, and there's no price discovery anymore,
and it sounds like that's on the opposite end of
that spectrum.
Speaker 4 (33:24):
I didn't say I think exactly that. It's a conversation
between It's a beautiful thing to say though. It sounds
really cool. It sounds good podcasts.
Speaker 3 (33:36):
That's great.
Speaker 4 (33:37):
Yeah, But I think your question is whether the rise
of passive has made markets less efficient more of a statement.
I don't think.
Speaker 2 (33:44):
I was a bad podcaster and didn't actually ask a.
Speaker 4 (33:47):
Question, But okay, how do you know?
Speaker 2 (33:50):
How do I know that passive is destorying the market?
People on Twitter tell me so.
Speaker 4 (33:55):
Oh, okay, don't trust people one number one.
Speaker 3 (34:00):
Number one, No, I don't know.
Speaker 4 (34:03):
I mean the rise of passive has made index rebalancing
a weirder strategy, right, so where the margins have compressed,
but the size has become so big that you can
still make money in it, and periodic. It's a very
you know, cyclical strategy.
Speaker 3 (34:19):
So I don't know.
Speaker 1 (34:21):
So if you're an indexy balancing PM, do take like
eight vacation a year?
Speaker 4 (34:27):
And not the ones I know who probably listen to
this podcast, they work very hard.
Speaker 2 (34:37):
They want to.
Speaker 1 (34:41):
Indexes aren't paying rence all the time, planning more than.
Speaker 4 (34:44):
You would think Index three balancing is another you know,
poster child for a strategy that seems so simple that
everybody can talk about it, and then it's full of
nuances and it requires a lot of skill to trade effectively.
Speaker 1 (34:59):
I believe, just because like I thought a little bit
about like just like the sort of like accounting of
like you basically know how many index funds there are,
let's say, can predict what will come in and out
of the index, and like what the so like there's
like some mechanics around, like you know, figuring out the
market calves that will come in and whatever, but then
(35:19):
it feels like the unknown is like who else is
doing the rebalancing strategy? Is that right?
Speaker 4 (35:23):
I think you're mostly right, because I don't want to say,
because you know, out of respect for for the CMS,
did I know fair enough?
Speaker 1 (35:33):
Yess? Like, So we had Cliff Askiness on a few
weeks ago, and like, to me, Cliff Asness is like
a quantitative investor, like a systematic investor, but like what
he's doing is sort of recognizably what a sort of
traditional asset manager. He's like trying to find companies that
are undervalued.
Speaker 3 (35:52):
Right.
Speaker 1 (35:53):
He talked about it's like being a Grammar dot investor.
You know you want like valuation plus a catalyst, and
he's like, oh, were you know trading you value and
momentum and like you look at what eight or two
is maybe a little different, but there's like you know,
the hypercacy trading firms, Like you can model those as
like those are quantitative versions of like a voice market
maker fifty years ago, where they're like trying to keep
(36:13):
inventory flat and like trying to you know, make the
bid ask spread. So like those are like very traditional
economic functions that have been quantified, like turned into systematic
what's the intuition for like what a bally asthe or
a sedad doll or a millennium does? Like what business
are you in? Do you think? Like as a philosophical matter,
(36:39):
like one thing I think like I think about.
Speaker 4 (36:40):
Like you're asking from a social Yeah, that's kind of point.
Speaker 1 (36:44):
Like I think like so the index rebalancing like to
me feels like the sort of trade and I think
to something that was the sort of trade that like
an investment bank would have done twenty years ago, thirty
years ago, and like some of that function I think
has moved to like the big multimanagers. But like I wonder,
like from where you say, like how you see that
role in the financial markets of those firms.
Speaker 4 (37:04):
So at a very high level, we don't do anything
different than everybody else in the sense that what we
provide is always this, right, is we provide shifting time preferences,
which means we provide liquidity, We house risk for people
who don't want to hold it right now. And that's
what you do when you do indextre balancing, right, That's
(37:26):
what you do when you do merger ARB and when
you do the various subtypes of basis traits.
Speaker 1 (37:31):
Right.
Speaker 4 (37:31):
So we do provide liquidity, which is very important. And
then the second thing we again very high level, we
provide price discovery. Right, So we study the firms and
we think, okay, this is at the margin mispriced and
we're going to short it or we're going to invest
in it, and that's a beautiful thing. So we do
it at a different time scale, right. So you always
(37:52):
want to do things at the margin where you don't
have a lot of other participants, and at the margin
of the let's say a month three month investment horizon,
there are not that many participants, so in the words
of another hatch fund manager I cannot name, but it said,
once you know, we don't invest in securities, we dated them,
(38:13):
and so we are in the dating service. Not that
many people are doing it, and so we do it.
But I would say also this right, not at the
social level. I just want to answer at the like
my personal level. What we do. We are a massive
filter of talent, and the talent that we hire is
a massive filter of information. So it's like information squared.
Speaker 1 (38:34):
Maybe this is like a bad question, but like, do
you think that like long only asset managers are worse
than they were thirty years ago because that filter has
been so successful? In other words, like there are lots
of jobs you could have gotten in finance in nineteen
ninety but like, yeah, there's like a clear hierarchy now.
Speaker 4 (38:53):
I think that the market and the set of investors
has learned right, and I think the distinction between beten
HALFA has been useful for investors and so active investors
who are mostly long only, I think have suffered from
this distinction because the vast majority of them underperforms their
(39:16):
benchmarks and so there is no reason for them to exist.
And then what we do is we provide really uncorrelated
returns to the benchmarks to most factors, and investors want that, right,
So there is a future where active investors, long on investors,
(39:36):
asset managers will become even less influential, smaller.
Speaker 1 (39:41):
And also I think of that as like a customer
demand side, but also like a talent filter side, right.
Speaker 4 (39:46):
Yes, Yeah, And then the interesting thing is and then
there is also a process where the multimanager platforms are
able to make the business model of a single portfolio
manager that is not sustainabil and isolation working in this
kind of federated system. So why would you or how
could you survive as a single portfolio manager hedge fund nowadays?
(40:10):
It's really really difficult, But you can do it in
a multi manager platform provided that you have you know,
sufficient talent, sufficient edge.
Speaker 2 (40:18):
That's also where you can blame the passive influence on Twitter.
If you're a long Enchey manager, then you know it's
impossible to be the market now because you just have
this money constantly pouring in.
Speaker 3 (40:27):
Yeah, I a don't disagree.
Speaker 1 (40:30):
Yeah, one more question, like social role is just like
you've worked at most of the big pod jobs, but
you also work at HRT, Like what's the difference in
roles and like what they do all day? Because HRT
I think of as a classic like high frequency treating firm,
where I don't know they're exactly a market maker, but
they're certainly on the higher frequency side. And then like
the pod shops have a lower frequency and a you know,
(40:52):
they're not prop they're running hedgephnes. Like what's the cultural
and role and difference?
Speaker 4 (41:00):
Yeah, okay, So I briefly mentioned HRT in an interview
with The Financial Times, and my manager told me that,
you know, people at HIT were both annoyed and delighted
by what I've had said about about HRT. I think
HRT is a really special place, even in the in
the context of proper training firms. So I'm a little
(41:21):
bit hesitant to just be in them in as a representative, right,
So they're not representative because there is something in the
culture of HRT that is special. Okay, it's collaborative, it's
truly kind.
Speaker 3 (41:40):
Yeah. So I think it's a great place.
Speaker 4 (41:42):
To work, and it is fundamentally monolithic, so you have,
you know, sharing of ideas and you can work at
the intersection of these ideas.
Speaker 3 (41:53):
It's also a place.
Speaker 4 (41:54):
That is very tech oriented, so it's a bit of
a technology firm or in the financial space, and because
of that, it also attracts I think, the best technical
talent that I've ever worked with. It's just a pleasure
to work with great technologies. People were very competent in
that respect. So nothing against the hedge funds. I love
(42:16):
edge funds for different reasons. You know, I love BAM,
which is also very collaborative and it's an investment company,
but HRT has as a technical side to it and
also gain a cultural side to it.
Speaker 3 (42:29):
It's great.
Speaker 2 (42:45):
We didn't talk about ai AI.
Speaker 3 (42:49):
Yeah, of course you have to talk about it.
Speaker 1 (42:51):
So I have like three models of how investment works systematic.
Like one is like you have like some economic intuition
and you build a model of the stock market that
predicts prices. In other way is a sort of like
neural netty ai E way, where like you throw a
lot of data at a neural net and it build
its own model of how to predict stock prices. And
then the third model is like you get really good
(43:14):
at prompt engineering and you get a chat GPT and
you say what stocks will go up? But you ask
it in the right way, and then chat GPT tells
me what stocks will go up? How good is it? Okay,
I assume the third model no one uses, but like
someone uses.
Speaker 4 (43:29):
I think a lot of people use that, all right,
So first thing, like, okay, nobody knows anything, and anybody
saying the opposite, you know, should be heavily discounted. Okay,
so we agree on this, and so let's forget for
a second all the technical details of AI just from
a pure industrial organization standpoint, Right, So what's going to happen?
(43:55):
Consider AI just like another technology like Internet and whatnot. Right,
So you know, first of all, we're going to observe
economies of scale, So there's going to be concentration, and
there was going to be some kind of monopolistic competition.
Speaker 3 (44:09):
I was thinking about Bloomberg.
Speaker 4 (44:11):
Specifically, which could be I hope for you people to
be among the winners, because you have a good starting point, right,
you have lots of data, right, you have a customer base,
and maybe in the future we'll finally not see the
good old Bloomberg terminal, which has been kind of unchanged
since I remember it, and instead people will just prompt
(44:34):
Bloomberg to conduct very complex actions where it will act
on a sequence of keywords and connect them and give you,
like a much more valuable product for which Bloomberg will
charge twice as much as they do already. So this
is going to happen in one form or another. If
it's not Bloomber, somebody else will do it. Okay, But
(44:55):
the same thing applies to other areas of finance. So
maybe once upon a time, you know, a big sufficiently
big fund could build their own client for email. Right.
Of course, nobody builds a client for email anymore. Right,
So a lot of this stuff gets outsourced. We will
outsource at some point some of the functions that we
(45:17):
conduct internally using AI to other AI agents. It's perfectly fine.
So this will become a utility to some extent.
Speaker 1 (45:27):
Yes, functions in clid like well.
Speaker 4 (45:30):
Not stockpicking, not stock picking. I think that the functions
that we will see available are essentially like another self,
like another mathlevin, who can you know be a good
baseline for you?
Speaker 3 (45:44):
Okay?
Speaker 4 (45:45):
You could feed a post train an AI system with
all your gazillions of words, right, and that agent will
reproduce your sense of humor, your investigative style and everything Okay,
it's a good approximation. It's not going to be perfect,
but why not, right, So I would be very happy
to have a replica of myself that can answer most
(46:07):
simple questions. Now, I think that the decision to invest
in a particular stock is a very demanding cognitive function,
and I don't see that really being replicated very well.
But I think that this will be baselined to some extent.
Speaker 1 (46:21):
Is it a many kind of function because because it
exists in a competitive market, So like the sort of
like whatever the kind of function is, is going to
get like the baseline is always going to get higher
because like someone else will will have the same information
as you do or the same.
Speaker 4 (46:36):
Well, this is getting really in the highly speculative side
of you know things. I think that in order for
an AI agent to be good at this, they have
to be able to experience the world the same way
that an investor experiences it, and our inputs are much
more complex than just a string of text or YouTube videos.
Speaker 1 (46:58):
Right.
Speaker 4 (46:59):
We have a the world which comes from visually experiencing
the world, talking to humans, consuming the goods, right anything,
It's vastly more complex than the way an AI system
Right now, experiences the world and also influences the world.
So an investor has a fundamentally different experience of a company.
Speaker 3 (47:21):
Than an LM that has.
Speaker 4 (47:23):
An experience that is mediated by multiple layers of processing.
You know, they learn about a company through text that
is written by somebody. So I don't think that that's
in danger for the time being. But maybe, you know, again,
in five years, maybe we will have our glasses feeding
our experiences to AI agents. Who knows, right, But I
don't think that it's that close, And I don't think
(47:47):
AI is that smart also, so I think that having
a baseline system would be already pretty good.
Speaker 2 (47:52):
That's somewhat comforting that our experiences count for something, our
physical experience of the world.
Speaker 1 (47:58):
It's interesting. I always think of like the comparison is
like investing in self driving cars, or like investors do
a lot of things. Like one thing they do a
lot is sit at a desk and read computers and
like look at numbers, right, and like those things seem
like things that a computer can do well, whereas like
you know, drivers like have physical reflexes and like have
a you know, complicated field division. I always thought like
(48:22):
investing should be easier than self driving cars for a
computer and a master. But you and I think you're
learning this. Think of like investing as like the great
liberal art, where it's like you incorporate all of human
experience and so the AI can't.
Speaker 4 (48:34):
Really Okay, let's let's take the metaphor to you know,
extreme consequences. Imagine that you had a system that is
the equivalent of a perfect self driving car in investing.
Speaker 3 (48:44):
So now I'm.
Speaker 4 (48:45):
Giving you a machine, a box that is telling you
the long term value, if not the returns, right, because
the moment that the value is known, you immediately equilibrate
to that level.
Speaker 1 (48:57):
Right.
Speaker 4 (48:57):
So imagine that you know the true value of everything
because a box tells you so, and it's infallible.
Speaker 3 (49:02):
It's an oracle. Okay.
Speaker 4 (49:04):
Would you think that finance stops existing? I wouldn't say so, right,
So I think that a lot of arbitrush trades, you know,
would maybe change significantly, but every risk, right, every return
would be correctly priced by the risk of the agents
trading it. So there still would be trading because we
still have different preferences, but basically every risk could be priced.
(49:28):
There would be in a sense less alpha. But finance
will still exist. It's a lot of service provision like
liquidity provision. Yeah, and so the liquidity provision would still exist.
The informational services maybe will stop existing in the current.
Speaker 3 (49:43):
Form, but that's okay.
Speaker 4 (49:44):
I think that we'll all still be employed.
Speaker 2 (49:47):
Mm hmm.
Speaker 1 (49:48):
It's an interesting I think about it, because I do think,
like we talked about, like, one thing that the big
hedgehunds to do is things that have the flavor of
liquidity provisions basis trades and merger and whatever. Things that
like I think of as like something that a bank
would have done thirty years ago, and then now a
big hedge one does. And then another thing they do
has the flavor of information provision, where it's getting prices right.
(50:11):
Like to me, those things seem quite intellectually separate, but
I guess they feed each other in the sense that
the better you are prices, the better you can be
at liquidity provision. Is that sort of right? You didn't
know the value of the Yeah.
Speaker 4 (50:27):
I mean a short, short horizon. Liquidity provision and information
tend to be very closely rated, Like you know, a limit.
If you are good at crossing, even good at crossing,
you should be pretty good at adding okay, adding liquidity,
so you know, but I mean like you could make
you know, a profit by posting a lot of limit
(50:48):
orders and providing liquidity to the market, or crossing the
spread and making money with predicting the future prices. If
you're good at one, you're good at the other. Most likely,
right at that time scale, I think that this though
might I'm not sure because I haven't thought about this
very very carefully, but I think this might be coupled
at longer time scale, so you know, you're when you're out.
(51:11):
I'm not sure. And in any case, at that time
scale is really difficult for an AI or for a
human being anyone, Like, there are not that many hard data,
even the unstructured data are not that many. So it's
a very difficult problem. It's the coupled it's it's complicated.
So yeah, but I tend to believe at longer time
(51:32):
scales you have more or less liquidit provisioning and you know,
violations of law of one price on one side and
predicting on the other side.
Speaker 1 (51:43):
But you combine both.
Speaker 4 (51:45):
But you can combine both, and it's a very potent mix.
Speaker 1 (51:48):
Right, there's normally different people.
Speaker 4 (51:49):
It is right, very different people for sure, different, very different,
very different people, very different cultures.
Speaker 1 (51:56):
Yeah, can you summarize the difference in cultures between like I.
Speaker 4 (51:59):
Have, I guess, but well, as you said, people who
typically trade in ARB trades, if not historically, but also
historically come from banks. Right, whereas you still can see
long only portfolio managers being recycled and reformatted into long
short portfolio managers, you can have an excellent short specialist
(52:23):
becoming a long short portfolio manager, like it happened.
Speaker 1 (52:27):
I mean my sense is that like that, people on
the information version long short sider more academic and research oriented.
In the people on the ARB side are more.
Speaker 4 (52:40):
Yeah, I think you can actually have very good long
short portfolio managers who were journalists in their past lives.
Speaker 1 (52:47):
I've heard of some of these. I thought about it, No, just.
Speaker 4 (52:51):
Like real.
Speaker 2 (52:55):
Breaking news on your podcast.
Speaker 1 (52:58):
I've jumps that's better than podcasting. Not thought about it
in the sonth that I'd be good at it, just
in the sense that the money is good.
Speaker 2 (53:08):
You could be bad at it and paid really well
for a short amount of time.
Speaker 1 (53:12):
I don't know that that's true. Actually, they're they're an
excellent talent filter or so I hear.
Speaker 4 (53:19):
Yes, I think that you could interest a few huge funds.
They might be listening. On a note, Kathy, thanks for
coming on the pleasure, Thanks for having me.
Speaker 1 (53:43):
And that was the Money Stuff Podcast.
Speaker 2 (53:45):
I'm Matt Levian and I'm Katie Greifeld.
Speaker 1 (53:47):
You can find my work by subscribing to The Money
Stuff newsletter on Bloomberg dot com.
Speaker 2 (53:52):
And you can find me on Bloomberg TV. Every day
on Open Interest between nine to eleven am Eastern.
Speaker 1 (53:57):
We'd love to hear from you. You can send an
email Moneypot at Bloomberg dot net, ask us a question
and we might answer it on air.
Speaker 2 (54:04):
You can also subscribe to our show wherever you're listening
right now and leave us a review. It helps more
people find the show.
Speaker 1 (54:10):
The Money Stuff Podcast is produced by Anna Maserakus and Moses.
Speaker 2 (54:13):
Onam Our theme music was composed by Blake Maples.
Speaker 1 (54:16):
Brandon Francis Newdhim is our executive producer.
Speaker 2 (54:19):
And Stage Ballman is Bloomberg's head of Podcasts.
Speaker 1 (54:21):
Thanks for listening to The Money Stuff Podcast. We'll be
back next week with more stuff
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