Cliff Asness

Matt and Katie talk with their guest, Cliff Asness of AQR, about momentum, meme stocks, value, market timing, publishing, academics, explaining factors, how much to charge for alpha, private assets and machine learning.

You can also watch this episode on YouTube. 

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Episode Transcript

Speaker 1 (00:02):
Bloomberg Audio Studios, podcasts, radio news.

Speaker 2 (00:08):
I was hoping to be like one of those like
clips on TikTok you see.

Speaker 1 (00:11):
Fake that's the thing you look like you're on a
podcast pads on home.

Speaker 2 (00:14):
Now it just looks like, you know.

Speaker 3 (00:16):
Instead of we just randomly got together the.

Speaker 2 (00:18):
Chat and microphonest.

Speaker 3 (00:20):
It is like with Bloomberg podcasts behind us.

Speaker 1 (00:24):
Yeah right, I guess we've conveyed podcasts efficiently with all
of the Bloomberg podcasts.

Speaker 2 (00:28):
Joe.

Speaker 4 (00:29):
Part of the reason this has to be on video
is because Matt shaved. Matt has had a beard for
the past I don't know that.

Speaker 1 (00:35):
I've had a winter beard from like Christmas break through memorild.

Speaker 5 (00:39):
That I shaved over COVID for the first time in
about thirty years. Okay, and my kids freaked out. Yeah,
they were like, you're an alien.

Speaker 1 (00:47):
Yeah, my kids didn't care that much. But one of
my sons said, it's a different daddy.

Speaker 5 (00:52):
Well, you also have hair on the top of your
head when you shave the beard and you don't have
any hair, Suddenly you're mister clean.

Speaker 1 (00:59):
You just does mister clean.

Speaker 2 (01:02):
No, I don't think so, mister clean. You know clean,
Come on, I think that's a fair point.

Speaker 1 (01:13):
It's the Money Stuff podcast. We have a guest, Cliff
has runs a q R. Thanks for coming in, Thanks
for having me. I always like to ask how to
my managers, like what do you do for a living?
Like what is it like economic function of like the
business that you run.

Speaker 3 (01:30):
Okay, those are to me slightly different questions.

Speaker 1 (01:33):
Right. One sounds like it's about you. One sounds like
it's about AQR.

Speaker 5 (01:36):
I'm more interested in even if it's about a q R.
What you do for a living is And people might
not like this phraseology, but you're trying to predict what
happens to securities. You're trying to buy ones that go
up either in the absolute or more than some benchmark,
and and sell ones that do the opposite. The broader question,

(01:57):
which I think is behind what you're saying, is what
do you do for the world by doing that? And
they overlap, but they're not exactly the same thing. Something
can have a net positive effect on the world even
if you're not waking up. And I'll admit this, I
don't think most people in their jobs waked up and
always think that way, and certainly not active managers just

(02:17):
go I am just making the world a better place.

Speaker 3 (02:19):
They're thinking, is in Nvidia undervalued? Is it overvalued? The
things that I think you do for the world.

Speaker 5 (02:26):
Is first, take the other side of positions other people
disagree with you on or don't want to bear. That
can take two forms. That can mean one of you
is biased and wrong.

Speaker 3 (02:41):
You hope it's not you.

Speaker 5 (02:43):
But in that case, what you do for the world
is you move prices back towards not necessarily all the
way too in the abstract, the correct price. That's hard
to define, actually, but something is mispriced and you take
the other side of that.

Speaker 3 (02:55):
It could be a risk premium. Other people don't want
to bear in a lot of strategies necessarily that they're
making an error.

Speaker 5 (03:01):
If a merger's announced and three quarters of the pop
you should get if it closes happens, a lot of
people might not want to stick around for that last quarter.

Speaker 3 (03:09):
And if you're willing to.

Speaker 5 (03:11):
Take the other side of that, maybe you get paid
a little for doing that, and that is a service
to the market. People want to get out and you're helping.
That's kind of the positive side, But again it's not
what you're thinking about.

Speaker 3 (03:23):
When you do the trade.

Speaker 5 (03:25):
There are positions active managers will take that are not
about that.

Speaker 3 (03:30):
Let me put it this way.

Speaker 5 (03:31):
If what you're trying to do is predict returns, you
can predict returns because the price.

Speaker 3 (03:37):
Is moving towards truth.

Speaker 5 (03:40):
But you can also make money if you predict the
price moves further away from truth.

Speaker 3 (03:44):
You know, if you're a.

Speaker 5 (03:45):
Momentum memestock investor. And doesn't mean you can't get that right,
you know. I think of that a little more as
trading than investing. But they all come together, and it
even gets complicated within some famous quantitative factors. One famous
quant factor is the momentum factor.

Speaker 1 (04:00):
I asked the finance professor I should I have asked?
And he said, you should ask him if momentum trading
makes markets more or less efficient.

Speaker 3 (04:08):
We don't fully know, but I can tell you the framework.
There are two.

Speaker 5 (04:12):
Competing explanations in academia and a general world that cares
about these things. For why momentum on average.

Speaker 3 (04:20):
Works, there's always a third that it will never.

Speaker 5 (04:22):
Work again and it was just random and it was
just luck. But if it's true, why does it work?
I actually think these can both coexist, so it's not
truly embarrassing. But it sounds embarrassing that the two major
explanations one is under reaction and the other is over reaction.
When you've narrowed it down to two things that at.

Speaker 3 (04:40):
Least feel like the opposites, you should feel a little shame.

Speaker 1 (04:42):
For a second, there's the situation just like there's a
correct price and momentum is trading from below to above the.

Speaker 3 (04:48):
Credit friends, Well, I'll give you two scenarios.

Speaker 5 (04:51):
Information comes out and people have a behavioral bias that
behavioral psychologists would call anchoring an adjustment.

Speaker 3 (04:58):
They move towards all the way to the.

Speaker 5 (05:00):
New information, and I think that fits a lot of
intuition in the short run that can make momentum work.
If you're following fundamentals or prices, good news comes out
or the price moves. If good news comes out, on average,
the price goes up but not enough. If the price moves,
it may on average be responding to good news, and

(05:21):
simply by observing the price move, you can say, Okay,
sometimes it's wrong, but on average it doesn't quite move enough.
If that's the reason, And I think the weight of
the opinion in academia I believe is towards this underreaction explanation. Then,
even though you're trading on momentum, you're still moving the
price towards kind of truth or equilibrium or something. But

(05:42):
the flip side is overreaction. Do you think of that
more as just your classic positive feedback loop. Someone's buying
something just for you know, fomo. It's been going up
in there, and well that could be a negative reason,
or they're just predicting more people buy it because of Fomo.
In that sense, if you buy some weird meme coin,
you could do that for a rational reason, not that

(06:04):
you are a long term holder, but you just believe
it's going to keep going.

Speaker 1 (06:07):
So did you make money on GameStop?

Speaker 3 (06:10):
This the god's honest truth.

Speaker 5 (06:11):
You won't believe me. I have no idea if we
were long or short game stop during the whole thing.

Speaker 2 (06:15):
You never went back and check.

Speaker 5 (06:17):
No, I never did. I did have an episode where
I mentioned on perhaps a different TV network that we
were short, AMC.

Speaker 2 (06:23):
What does your Twitter look like after that?

Speaker 5 (06:25):
It was very ugly. Yeah, I certainly knew of that world,
even though we're quants. I watch the markets all day,
even even if I don't do anything about it. It's
like the old joke about the weather. Everyone talks about it,
but nobody does anything about it.

Speaker 2 (06:36):
Best thing to talk about.

Speaker 5 (06:37):
So it's not like everything that went on with GameStop
Melvin Capitol. You know, I'm watching it every day, but
we take relevant tiny positions in every stock. There was
nothing weird in our P and L. And yes, I
was not even curious. It probably wasn't even in our
universe at that point of things.

Speaker 3 (06:54):
We trade.

Speaker 5 (06:55):
But then I'm going on this other network it's allowed
and in kind of a pre call of what are
we going to talk about?

Speaker 3 (07:05):
You guys know, you.

Speaker 5 (07:05):
Don't want to get on there and have absolutely nothing
to talk about. You want to have some not necessarily
the answers worked out, but agreed upon topics. They're like
everyone in this segment gives us some longs and shorts,
but I'm saying, as a quant that's kind of silly.
They're not indicative, and we kind of made a deal
where they'd let me briefly explain that doesn't make a

(07:26):
whole lot of sense for quantum, but it might be fun.
And my way of saying it was, if I give
you a few names long and a few names short,
you could look in six months later and think we
had a fantastic year or a terrible year and be
terribly wrong in either direction because they're tiny. So I
went through an AMC was I think I'm accidentally doing
it again.

Speaker 2 (07:45):
We'll keep going hopefully.

Speaker 3 (07:46):
They don't listen to you, guys.

Speaker 5 (07:49):
But it was bad on every single thing in our
model practically, which is hard to do. It was expensive, unprofitable,
high beta. They were issuing shares, not buying backshit. There
are more examples, and so I said that, but then
I added that were only short twelve basis points, so
the crazy people could be right and it doesn't really matter.

(08:12):
I discovered two things. They're not going to like a
short period, and crazy people don't always like being called crazy. Yeah,
I had to discover that from myself. So my Twitter
got ugly for a while. You may have noticed. I've
gotten I think a fair amount better at this, But
I used to be pretty bad about responding to ugly,
which you learn your lesson on that.

Speaker 2 (08:30):
You always feed the trolls.

Speaker 3 (08:32):
Less so than I used to. At least I think
I'm better.

Speaker 5 (08:35):
Maybe I'm wrong, but I became public enemy number three
to the meme.

Speaker 3 (08:39):
Stock crowd for a while, I did.

Speaker 5 (08:41):
Not really Yes, Kenny Ken Griffin was number one. I
don't believe Ken did this, but it's the whole poll,
the Bible. And oddly enough, Gary Gensler was clearly number two. Yeah,
because they thought he was covering for the manipulators and
the naked shorts and whatnot. I've met both, I know,
and a little better than Gary. I don't think there
are any two people on Earth less likely to be

(09:04):
in cahoots than those two. I think they're on the
opposite side of most issues. But that was the theory.
But both Ken and Gary are too smart to respond
to them on Twitter, so I certainly became the most
actively engaged, and then I never did that again before today,
when I've accidentally done it.

Speaker 4 (09:21):
Before we get back to stuff that matters, can I
just on AMC, I tweeted, when did June two come out?

Speaker 2 (09:27):
It was like last summer.

Speaker 3 (09:28):
Yeah, year ago.

Speaker 2 (09:28):
I tweeted that I.

Speaker 4 (09:29):
Fell asleep during Dune two, and it reawakened that crowd,
at least on my Twitter, because I've the same.

Speaker 3 (09:35):
Crowds crowd because he's dissing a movie.

Speaker 5 (09:40):
Don't understand you have to like every movie or else
here anti America, And then there were.

Speaker 4 (09:46):
A lot of conspiracy theories about Bloomberg reporter lies about
falling asleep in.

Speaker 2 (09:51):
Doom too because she hates AMC. It would be, but
it wasn't to.

Speaker 5 (09:55):
Oh, yeah, you do innocuous that I was obnoxious, so
I'd kind of deserved it.

Speaker 3 (10:00):
You didn't deserve Thank you for saying that you didn't
deserve it.

Speaker 1 (10:03):
I haven't sent. That's not doing what I was good.

Speaker 2 (10:05):
That's all too. It's pretty good.

Speaker 3 (10:07):
No you didn't, actually I didn't.

Speaker 5 (10:09):
You were asleep, well no, not the whole time, not
the whole.

Speaker 1 (10:12):
Time, but also like you and and they were like,
give me some shorts, and you gave them some lungs
and shorts. Would you have known that or did you
have to be like I got it.

Speaker 3 (10:22):
I would not have known that, right, So you don't.

Speaker 5 (10:24):
Know, you're ar It would have been better for me
if we didn't have the call, because then I could
have just said I don't know. I rarely know individual stocks,
and if I do know, I'm probably not happy I know.
And even then it's like we lost twenty BIPs on
that today, which is a giant number for us to
lose on one stock, and even then I probably don't
notice twenty bit.

Speaker 1 (10:44):
If someone comes to you and says that's not.

Speaker 3 (10:46):
One, I might be told about it.

Speaker 5 (10:49):
For us, it's whether these seven hundred and fifty stocks be
these seven hundred and fifty stocks. Yeah, I don't memorize
fifteen hundred stocks. Now, we take a fair amount of risks,
some funds more, some funds designed to be less. And
that's about the size of these two positions. So when
I say small, I'm not saying we're not both taking
risk and trying to generate pretty decent returns. But if

(11:10):
you just think about it, a quant is playing the odds.
They're saying, affirm a company with these characteristics. And this
can be old school factor quants from the nineteen nineties,
these can be modern machine learning. But with these characteristics
tend to beat these characteristics. If that's all you know,
and it is all we know, why on earth would
you take a lot of risk in any one company.

Speaker 3 (11:31):
AMC really could have done well.

Speaker 5 (11:33):
It could have been bad on every single thing that
on average doesn't work, and it could be a special
situation that we don't understand. Something could be good on
everything and the CEO can embezzle all the money. We
don't want to take a lot of risk on any
one thing because we have no insight in that it's
risk for no return.

Speaker 1 (11:50):
One thing you've written is that like over time, the
quant like factor model has moved closer to being what
Gramm and dot investors do. Like are you like an
abstract like Metagram and Dada investors? I like the way
to think of what you do.

Speaker 3 (12:04):
I think it's moved closer.

Speaker 5 (12:05):
There are still differences and maybe some of the like
momentum if it's overreaction, if you're riding momentum. I don't
think a Graham and DoD manager does that. So I
don't want to push the analogy. But this came out
very very early in my career. This is like thirty
years ago. This is like my Goldman Sax days. I
started hearing a lot of active stock pickers, some I'm

(12:26):
still still friends with one guy in particular. I was
laughing at them, and I was telling my friend, you
all say the same thing. You all say you're looking
for valuation plus a catalyst. It's like a I don't
know if everyone says it, but I've heard it many
times and I'm making fun of him, and at some
point he looks at me and goes, you do value
on momentum, and it was a gotcha. He won that

(12:48):
round because I'm like, okay, I see your point. So
even back then, you can think of those two together.

Speaker 3 (12:54):
We literally add them up.

Speaker 5 (12:55):
But if you think of him as this holistic system,
we're looking for cheap things that are starting to get
better in price or fundamentals over time. And now I'm
I'm not talking the really modern stuff, alternative data, machine learning.
I'm talking just classic quant stuff that's been academia and
then imports over to applied.

Speaker 3 (13:13):
Profitability is a factor.

Speaker 5 (13:15):
Robert nov Mark's wrote a great paper that we all
incorporated where also equal give evaluation and momentum.

Speaker 3 (13:22):
If a company is more profitable on some.

Speaker 5 (13:25):
Famous scale's gross profitability ROA Roe, that's some degree a
positive low beta investing. Two of my colleagues, Andrea Frazini
and los A. Peterson, resurrecting stuff Fisher Black did, and
they're very good about saying Fisher did it first. That
lower beta stocks. If you're famously a capital asset pricing
model person, they're supposed to sell on average underperform higher

(13:48):
beta stocks. That's the main output of that model. It
doesn't work. I mean, it's one of the largest empirical
failures ever. It doesn't work in any kind even outside
of stocks people tested in other places. Therefore, it kind
of makes low beta stocks a little bit of a
free lunch because they are lower risk and they keep up.
If you actually go read Graham and DoD, they're not

(14:09):
just buying low multiples. They're much more holistic than that.
You know, high quality companies that have a moat, that
have some kind of margin of safety I think was
the term they use. Margin of safety and looking for
low risk doesn't sound so so different. So over time
I've thought at least a core amount of what quants

(14:30):
and academics, if you take them as a whole, are finding,
is the full paneplyy of stuff that a Graham and
DoD investor. We do it very differently. Again, we're betting
on the concept working on average.

Speaker 3 (14:41):
They are using it.

Speaker 5 (14:42):
In a soft or a hard sense as a screen
to look for candidates, and then they're trying to learn
a lot about that situation. They're upside as if they
learn a lot about that situation, they could be more
reliable than my You know, hey, we could be wrong
about AMC, and their downside is they better be right.
The concept can work, and they can still lose if
they're wrong about the specific So over time I've gotten

(15:04):
a little less hubrious about this. I think quants caused
the problem, by the way. I think when Gene fam
and Ken French started looking at like price to book
in the late eighties and early nineties, and there were
other people who did it too, I'm just Chicago guy,
so I'm going to just go with Fama and French.
They did it best. In my very biased opinion. I
don't think i'd have to go back and check, but
I don't think the first few papers use the word

(15:26):
value investing. Over time, low multiple investing in the quant
world came to be called value investing, and in the
Gramm and Dodd world they get kind of mad at that,
and they'd be like, it's not value investing. There are
plenty of low multiple companies that deserve to be low multiple,
and there are plenty of high multiple companies that deserve it,
and I think over time, the quantitative process agreed with them.

Speaker 3 (15:51):
More and more.

Speaker 5 (15:51):
So I still think it's a communication problem because they'll
still talk about the value factor. In the quant world,
that's just low multiples. And if a Gramm and Dodd
gets mad or any old school active stock picker gets
mad at that, I'll just say you're right, because value
implies a more holistic thing.

Speaker 1 (16:08):
But isn't like modern quantit and what you're doing now
kind of just that more holistic thing, like you're just
ingesting more data plans and you have a less linear model,
and it's like moving towards that.

Speaker 5 (16:17):
Anyway, if you look at machine learning, where either to
construct factors. One of the best uses of mL we
found is a subset of mL called natural language processing,
where you take textual data and you try to say
is this good news or bad news? Quants have kind
of done this forever. You get transcript of an earnings call.
And the old school way to do this for a

(16:38):
long time was you count up good words and bad words,
good phrases and bad phrases, so increasing plus one and
you tech parse the whole thing, and sure, you guys
immediately see the problem. If the actual sentence was massive
embezzlement is increasing, then you were off on that plus one.
Quantitative stuff can survive doing some horrifically stupid things in isolation,

(16:58):
If fifty three percent of the time increasing is a
good word, than forty percent of the time you were stupid,
turns out natural language processing or NLP, if we want
to sound like the cool kids. That is, taking that
same data and training a mL model to say what
predicts and what doesn't predict, and of course never gets

(17:18):
near perfect at the end of the day, though we
believe it does a lot better than the word count
methods and is additive to a model. But importantly, and
I think this was your point, Matt, it's not qualitatively different.
We've looked at both price and fundamental momentum forever. Fundamental
momentum the classic measures of things like our earnings being

(17:39):
revised and you want the revision.

Speaker 3 (17:41):
You want the new news.

Speaker 5 (17:42):
Up faster or slower. Our earning surprise is coming in
positive or negative. And this is this anchoring and adjustment
idea that if that's good or bad news, you can
make money trading on it. Because it's not fully incorporated
in the stock price parsing and earnings call poorly as
in the past or better now, we think of it
as just another form of our good things happening in

(18:03):
the new year term, and if so, they're probably under appreciated.
So yes, I don't think it's changed dramatically. It's much
more of an evolution rather than a spirit change.

Speaker 3 (18:13):
But we got bigger tools in the tool chest now.

Speaker 1 (18:16):
I mean, like, you know, you ask, like what a
sort of like traditional old school fundamental manager would do.
One thing they do is listen to their next call
and like talk to management. So it's like your machines
are moving in the direction of being an old school analyst.

Speaker 5 (18:27):
Yeah, as someone with four kids in or around college age,
my wife and I and she's done a lot more
of this, has spent a lot of time trying to
figure out what careers will not be utterly destroyed by MLO,
and it's not an easy question.

Speaker 3 (18:46):
A podcaster is probably a good one.

Speaker 5 (18:48):
For a while.

Speaker 3 (18:48):
They have those fakes, so it will all be podcasters.

Speaker 5 (18:53):
But yeah, it's another example. I always say, these useless
statements like them sufficiently long horizon, we're all replaced by
machine learning.

Speaker 3 (19:00):
It's a question of what.

Speaker 5 (19:03):
We're getting way too philosophical, but the transition to that
can be very painful and weird. But a world of
abundance and leisure. Maybe our horrible fate in the long term,
I'll take it.

Speaker 6 (19:14):
Yeah.

Speaker 1 (19:30):
I want to talk about market timing because I was
reading the Virtue of Complexity paper from Brian Kelly at All.

Speaker 5 (19:35):
Which is I will say he may be one of
the only hosts of a podcast to read that paper.

Speaker 3 (19:39):
This is not a simple paper. It's like he writes,
very clue.

Speaker 1 (19:43):
It's the sort of like it's the notion of taking
like a sort of simple factor model and blowing it
up into like a nonlinear AI model, and one almost
throwaway sentence in the pieces like this, like simplified AI
model that he built for like illustrative purposes, lowered its
risk before fourteen of the last fifteen recessions. And I

(20:03):
always thought the naive like the best way to invest
would be just market timing, just like you know, have
all your money in the stock market before the market
goes up and not before it goes down, if you
can do it. My impression is that like respectable headgeplot managers,
respectable clime managers, respectable academics say the hardest thing in
the world is market timing, and like no one claims
to get alpha from at and it's not a thing.

(20:25):
Is that changed, and is that changed due to like
machine learning?

Speaker 5 (20:28):
It has changed a big Do we do trend following
on macro assets old school CTA stuff. We think we've
made a new school by incorporating fundamental momentum by doing
a lot of more esoteric market so we think even
that's had a march of progress to it, But all
else equal. I wrote my dissertation on momentum in individual
stocks for some reason that I cannot explain. If you're

(20:51):
using past returns to predict the future, just what's going
up will keep going up, and vice versa to pick
individual stocks. The whole industry calls momentum. And if you
think markets tend to keep going in the same direction,
everyone calls it trend falling.

Speaker 3 (21:05):
Same thing. Starting in I think two thousand and eight,
we started.

Speaker 5 (21:09):
We always had it in our macro models, but we
started formally offering separate trend falling products.

Speaker 3 (21:15):
It doesn't take hero bets on anyone market.

Speaker 5 (21:18):
It's not Gazarelli selling all the stocks a minute before
October nineteenth of eighty seven. I'm dating myself. My wife's
birthday is October nineteenth, which always always gets a little amused.

Speaker 3 (21:28):
Crash eighty seven meant to.

Speaker 1 (21:30):
Be Did you really crashed them together?

Speaker 5 (21:31):
Yeah, there have been some jokes over over time. So
trend following it's essentially market timing, but it's highly diverse,
many small bets. Whatever's been happening tends to keep happening.
That's the main way we'll do market timing.

Speaker 1 (21:44):
You want to like your main equity fund is like sometimes.

Speaker 5 (21:47):
That that was kind of a toy model to illustraate
a point. I know we're not taking a lot of
risks on that model. Market timing I still think is
quite hard. Might there be advances in it in the future, absolutely,
but are we taking significant risk in it now aside
from trend following, not really.

Speaker 1 (22:07):
You probably sim in more papers to financial journals than
the average like asset.

Speaker 3 (22:11):
Manager, by like one hundred percent.

Speaker 1 (22:12):
I want to ask two questions. One is like, I
want to learn about your relationship with academia, because I
think it is fascinating that like you employ half of
the l faculty the finance PhD pipeline runs mainly to
AQR and then too because I think of like what
HEGEMA manager does as sort of like finding anomalies, finding
like market inefficiencies, finding factors that are predictive every turns,

(22:34):
and I think of what a finance academic does is
mainly also that when do you publish and when do
you just trade on it?

Speaker 5 (22:39):
Oh, there's so much to talk about here. First, there
are a lot of reasons we do it. One is
just personal consumption. We grew up in this world. We
liked being part of it. We were interested in this
stuff in a purely academic sense before we got seduced
into making money.

Speaker 3 (22:56):
I mean, I.

Speaker 1 (22:56):
Would like pause it that you think there is a
value to employing the finance PhDs to like build your models,
and the way to attract fancy finance PhDs is to
offer them the most academia like possible working.

Speaker 5 (23:11):
And to letting them publish. There are people, including our
two Yale professors. I don't don't know if would have
come take you are if we said you can never
write about this. We do a crass calculation, though, if
we think there is something that we are relatively unique
on entirely unique, or a very small handful of people
know this, we won't publish it. The optimal the optimal

(23:36):
time to publish a paper is after you've made money
from something for eleven years and an hour and a
half before someone else is going to publish the paper,
and we cannot get that right. I can think of
one example of momentum in factor. It's the fact that
factors themselves, like value, profitability, also exhibit momentum as something
that we've traded on for many years that we've always

(23:57):
refrained from writing a paper on because no one else has.
And again, we knew we couldn't be the only people
who do this, but we didn't think the cat was
out of the bag. And then someone else wrote the paper.
And I'm sure we've done this to other people too,
because you never know what they're doing internally. So life,
you know, you do it long enough, life works out
kind of fair, but that.

Speaker 3 (24:14):
Is the goal.

Speaker 1 (24:15):
But you were mad there because, like as academics, you
wanted credit for that paper.

Speaker 3 (24:20):
It's fun.

Speaker 5 (24:21):
You could call it childish, but it's human. It's it's
fun to discover something.

Speaker 1 (24:24):
Well, you also mad because publishing that reduces the value
of the signal or maybe a.

Speaker 3 (24:28):
Little bit, maybe a little bit.

Speaker 5 (24:30):
So far, it's still worked wonderfully. The trend is still
your friend when it comes to two factors, but it
would be a fireable offense for make you are to
publish something that we thought was truly proprietary.

Speaker 3 (24:40):
Let me give you my favorite example of this, because
it came up recently.

Speaker 5 (24:43):
One of the things that we've gotten into in a
fairly big way, as have some other quants, is what's
called alternative data. Those are new data sets that put
people put together with sweat equity. The classic example and
the only one. And this is the point that I'm
allowed to talk about our credit card receiver data were
a credit cards because that's been discussed.

Speaker 3 (25:02):
A million times.

Speaker 1 (25:04):
Anative there.

Speaker 5 (25:04):
Yeah, yeah, Well that's the point is this stuff is arbitrageble.

Speaker 3 (25:08):
I mean, it can go away.

Speaker 5 (25:10):
Value, be it the narrow Quan sense of value or
the more broad gram and Odd sense of value, is
trying to take advantage of I think basic human nature.
I think spreads being cheap and expensive are wider still
than the historical average, not tighter. I don't think there's
a lot of evidence that so much capital is in
that that.

Speaker 3 (25:27):
It's making you go away.

Speaker 5 (25:28):
If you get a short term information advantage because you
have put the time in or paid someone who's put
the time in to create a new data set that
other people don't have. They're going to have it eventually.
So I am talking to the Australian Financial Review. I
don't even know if we had discussed alternative data before him,
but he asked me about it, and I said to

(25:50):
him that our head of stock selection, a fellow named
Andrea Ferzini, has asked me, I might have said, told me,
but has asked me not to talk.

Speaker 3 (26:01):
About these things.

Speaker 5 (26:02):
There are things I'll talk about, but there are things
we think are you know, overused word, but our true
alpha that the world doesn't know, and I think it's reasonable,
So I really can't talk about him. He writes the
article and what it says is mostly that, but instead
of saying he's asked me not to talk about it,
it says Andrea Ferzini won't tell me what we're doing

(26:23):
in alternative data, which it has a slightly different connotation,
and that's a connotation of adult old man, don't worry
about it.

Speaker 3 (26:31):
We're doing some stuff here.

Speaker 5 (26:33):
So again it's an example where there certainly are things
we won't talk about.

Speaker 1 (26:37):
Is that ever true? Are you like most cutting edge
machine learning people like that? Or are that or do
you read all the papers.

Speaker 5 (26:43):
I read most of the papers. I don't read all
of the papers. I at least skim all the papers.
I know the gist. We do a lot of different
things at this point. Once you're a quant you want
more factors and you want to trade it in more places.
And I will admit that the week doesn't go by
where I don't and ask someone for a review of
what we're doing. Somewhere at some point I approved it,

(27:05):
so Pastcliff had some understanding. Yeah, but also just you know,
some of this is pretty decent math, and the old
mathematicians who their best work in their twenties, I can
tell you I'm probably still decent in math, but I'm
not what I was when I was twenty two. Wisdom
has hopefully replaced some of mathematical ability because.

Speaker 1 (27:24):
The personnel at AQR different from a like I don't
know who you think of as like quantity competitors, but like,
my impression is that like you have many more finance
PhDs than like other places that might have more like
pure math people or math undergrads or something like.

Speaker 3 (27:41):
Yeah, I think there's some truth to that.

Speaker 1 (27:44):
What makes them better or worse?

Speaker 3 (27:45):
First, I think.

Speaker 5 (27:46):
There's going to be a correlation that the closer you
get to the high frequency world, the more you're going
to be more in the pure math realm. My very
tortured analogy is quantum mechanics versus Newtonian physics. When you
get into the high frequency world, first you have a
ton of data. By nature, you just have a lot
more instances.

Speaker 3 (28:05):
And a lot less theory.

Speaker 5 (28:07):
So turning yourself over to the data more so than
having an economic rationale is far more rational, and that
becomes a more mathematical exercise.

Speaker 3 (28:16):
We tend to think in our long.

Speaker 5 (28:18):
Seven hundred short seven hundred stock portfolios, average holding period
is maybe nine months, maybe closer a year.

Speaker 3 (28:24):
Times. That's nowhere near high frequency. Yeah, high frequent. Two.

Speaker 5 (28:28):
When I wrote my dissertation and I did have this
in there was a monthly contrarian strategy. Now we're talking,
you know, sub seconds kind of thing. I think when
you're gonna have a medium holding period strategy, as I
think of us, we're not Warren Buffett, but we're not HFT.
Even the machine learning stuff needs some economics too. You
simply don't have enough data and there's too much of

(28:50):
a dimensionality even with Brian's virtue of complexity, you need
to give it some structure or else it's going to
overfit and go mad. So I think think that's the reason,
not saying we'll never do something in a higher frequency world,
in which case we'd probably have to shift more. But
in our world being a mathematician, being an excellent programmer,

(29:11):
but also having the economics behind it, it's kind of
what we're looking for.

Speaker 1 (29:16):
I think of like renaissances famously employing exclusively people who
have never thought about markets or financer economics, and like
they come to it pure, and I feel that you
are very much like people who have math shops, but
like have economic intuition and think about it as an economy.

Speaker 5 (29:36):
I think that's accurate. I do have a couple of
Renaissance observations.

Speaker 1 (29:40):
Okay.

Speaker 5 (29:41):
One of my favorite questions people asked me was howd
they do it? And I love that question because I
get to respond. So your hypothesis is, I know how
they took a few billion dollars and still take a
few billion dollars out of the market to share among
a relevant tiny group of people every year would apparently
very low risk, and I choose not to I have

(30:01):
some inklings of the general whether they've worked on. My
biggest guess is that they were ten fifteen years ahead
of everyone else on most of this stuff and are
just have developed more sophisticated systems over time. I think
natural language processing they were very early on.

Speaker 1 (30:15):
It came from like from.

Speaker 3 (30:18):
A few other things.

Speaker 5 (30:19):
John Leu and I, my one of my founding partners,
wrote when Fama and Schiller shared the Nobel Prize, we
wrote a whole overview of market efficiency and the debate
about it, and I brought them up as an example.
The Medallion Fund has almost nothing to say about market efficiency.
It says, these guys can extract a toll on the
market with reliable consistency, But in terms of market size

(30:42):
it's a giant number for a few people to make
each year. It's a tiny number in terms of whether
the markets are efficient, and apparently the rest of us
can't do it quite as well as them. They're the
goat when it comes to this.

Speaker 1 (30:52):
Yeah, I think of like, you know, like Jane Street
can rely excite somebody from the market, and I think
of that as like a few first service ye, like
a market maker, rather than being like a predictor of
asset precesce.

Speaker 5 (31:03):
I'm gonna guess, and again it's a purely guess that
not all of it, but a fair amount of Renaissance
had a similar similar flavor.

Speaker 1 (31:09):
I think when you talk to people who run pod shops, like,
some of what they do they conceive of as it's
not literally market making, but it has that sort of flavor.

Speaker 5 (31:17):
And the constraint on those things is typically capacity. Now
it's a ton of money for those firms. They're amazing firms,
but give you an example of Renaissance. I've complimented them
like crazy, so hopefully they won't be mad at me
for this.

Speaker 3 (31:28):
One last comment.

Speaker 5 (31:30):
Everyone talks about the Medallion Fund, but no one's allowed
to invest in that. They keep that for themselves. Another
favorite question I get is this doesn't happen much anymore,
but every once in a while I would get an
investor saying, so, Medallion Fund better than you guys. I'd
be like, oh, hell yes, but they won't take your
money or my money, and I think we're pretty darn
good and we will. So why is that relevant? But

(31:51):
Renaissance also runs a fair amount of money in more
open institutional products where they look very good. I'm not
gonna to ride them at all, but they don't look
better than us. They look like really good, solid, regular quants.
So what they cannot do, I think it's kind of obvious,
is take the medallion process and scale it up many,

(32:14):
many times bigger. They've discovered a way, as you put it,
to just take a certain amount out to provide a service.
I called it taking a toll, and that's amazing. But
they've not discovered a way to do that at institutional scale,
which thank god, because that leaves something for the rest
of us to do.

Speaker 1 (32:30):
I'll get back to something you talked about the very beginning,
because you mentioned that paper you wrote with John Leu,
which you talking about like the two possible explanations for
making money for anomolies for whatever, which are sort of
behavioral irrationality or whatever, and you were bearing a risk
for someone. Everything you write, and like what you said
here you have, you seem sort of like agnostic about

(32:52):
which one or what combination there's like, do you prefer one,
is it more reliable to get paid for taking your
risk or.

Speaker 5 (32:59):
Well forgetting which one you think is really going on?
They do have different characteristics The positive behind a risk
premium is you may be able to get it forever
because it's rational that you get it. The negative behind
it is it's a risk premium. You have to figure
out why. If the risk premium is most of the
time this is fine, but it has depression risk. It's

(33:19):
going to do particularly bad in a depression. Well, that's
not a very pleasant risk to have. You may get
paid for doing that. The positive of behavioral is it's
essentially over the long term. Over the short term it
can be very very painful lunch because the noise work.
But if on average it works long term, it's a
free lunch and that you're not taking additional systematic risk.

Speaker 3 (33:42):
The negative is it can go away.

Speaker 5 (33:44):
It can be arbitraged away if that ra stops being made,
If too much capital chases it, it can be arbitraged away.
It's hard to arbitrage some of them away, like basic valuation.
It's very easy to arbitrage some others, like alternative data,
where the point is to be quicker to getting a
data set and to trading the day to set. I
will say this, and I hope teen Fama isn't listening.

(34:04):
I was probably seventy five twenty five. For risk guy
thirty years ago, I'm probably seventy five twenty five.

Speaker 1 (34:10):
A behavioral is that GameStop or something not?

Speaker 5 (34:13):
Well, the GameStop maybe the unfairly extreme example. It's not
fair to pick the most extreme. Craziness is to make
your point, But yeah, it's real life experience. It's watching
the spread between cheap and expensive stocks in ninety nine
two thousand.

Speaker 3 (34:30):
We started our firm in late ninety.

Speaker 5 (34:31):
Eight, so right before the real crazy part of what's
called now the dot com bubble, the spread between how
we define cheap and expensive, either just using quant multiples
or adjusting for forms of growth, set records. We had
fifty seventy five years of data and we lived through it,
blowing past those. Then after that, I'm like, all right,

(34:53):
that was a once in fifty year event.

Speaker 3 (34:54):
We survived it. We ended up being right.

Speaker 5 (34:56):
We ended up making money round trip that was excruciating
on the first leg. If you ask me back then,
am I ever going to see that in my career again?
I hope I'd be smart enough not to say never.
None of us in our field, yours or mine, should
say never. Markets are pretty hard things to say never about.
But I think I would have said it's highly unlikely.

(35:16):
For one, it was literally the most extreme event in
at least fifty years. Second, the question presupposes I and
people like me will still be around. Unpresumably, if we're
still around, we're in more supervisory and authoritative positions. So
how's it going to happen again? And then it happened
again almost exactly twenty years later, from twenty eighteen through

(35:38):
twenty twenty. You can point to COVID. It went absolutely
mad during COVID. You guys remember for the six month
period after COVID when the only two stocks you're supposed
to own were Peloton and Tesla. One of them has
still worked out mostly for you.

Speaker 3 (35:50):
One of them did not.

Speaker 5 (35:52):
But even before COVID, though you cannot pin it on COVID,
by late nineteen early twenty we were approaching tech bubble levels,
which again had not been seeing the prior fifty years before.
The tech bubble again, painful period for us, again made
money round trip. I'm going to brag about that we
survived it.

Speaker 3 (36:09):
I think we're.

Speaker 5 (36:09):
Stronger than we were beforehand. But if the first one
didn't make you start to go this behavioral stuff may
be real and maybe bigger than it.

Speaker 3 (36:18):
Used to be. Yeah, and I wrote a piece.

Speaker 5 (36:20):
Called the less efficient market hypothesis markets getting less efficient.
That may be true, but I think it's more accurate
to say there what my evidence is there prone to
some extreme bouts of craziness on occasion. It's not quite
the same as in steady state always being less efficient,
but probably some of both. But I do think that
has happened. I probably was not giving behavioral enough credit

(36:43):
early on, and I think it's gotten to be a
bigger part.

Speaker 1 (37:02):
I want to read something from the Johnlely paper from
twenty fourteen. Suppose you imagine some investors get joy from
owning particular stocks, for example, being able to brag out
a cocktail party about the growth stocks that they own
that have done well. One way to describe this some
investors have a taste for growth stocks beyond simply their
effect on their portfolios. It can be rational for them
to accept somewhat lower returns for this pleasure. But even

(37:23):
if rational to the individuals who have this taste, if
some investors are willing to give up returns to others
because they care about cocktail party bragging, can we really
call that a rational market and feel this statement is useful.
I feel like that was like a little prescient about
how I have experienced some of the last five years,
which is that, like it seems to me that it
is hard to explain some of the stuff I write about,

(37:43):
which is maybe not like the most important thing in
the world. It's often like I think the most important
do too, but like it's like, you know, it's maybe
not like the biggest, you know, dollar, the test LIS's
pretty big. It does seem like a thing that is
regularly happening is that people have a taste for stocks
beyond any rational or irrational calculation about how we'll fight

(38:03):
their portfolio.

Speaker 5 (38:04):
Well, particularly the whole meme world. Meme coins are clearly
a political statement slash.

Speaker 1 (38:11):
Clearly there is a non economic motivation for some of that,
and you can callude rational.

Speaker 3 (38:14):
But it's odd, Well a few things that paragraph.

Speaker 5 (38:17):
Even writing it, I think we went a little too
far because I don't think these people, we kind of
write it like they're consciously doing it, like I just
love owning these I know I'm gonna lose money. I
think they kind of end up they resolve cognitive dissonance
by saying I'm gonna make money even if that's what's
really going on.

Speaker 1 (38:34):
Right, I think this is maybe a little harsh on
growth investors in twenty fourteen, but on MEME investors.

Speaker 5 (38:38):
And now instead on meme investors. We were also given
a little mild shot to some academics who kind of
try to save market efficiency by saying it's more meta
efficient if you count people just have tastes for this.
That argument's been made. I think that's kind of a
cop out. You know, if we've done market efficiency down
to that, what do we actually even mean by market efficiency?
If you go, I know, I'm gonna do terrible on this,

(39:00):
but it's fun to me, that's functionally a fairly inefficient market.
So we proposed in that piece which did not catch
on that classically market efficiency and the testing of it
has a joint hypothesis problem. You're testing whether markets are efficient,
but you also need a model for how prices are set,

(39:20):
and if that model for how prices are set includes
this is fun to own, then you've pushed it too far.

Speaker 3 (39:27):
It has to be a reasonable hypothesiz.

Speaker 1 (39:29):
This is what I read about this is I want
someone and it's probably not me. To write a finance
textbook that incorporates the factor of this is fun to
own and gives some guidance how to price that.

Speaker 3 (39:39):
Be and how long it's going to continue.

Speaker 1 (39:43):
And people like someone is making money on this, oh yeah,
and like not just like fading it. Someone is like,
I have a model for which meme coins will go up,
And well, people have.

Speaker 2 (39:54):
Tried to do that.

Speaker 4 (39:55):
You think about all the different buzzy strategies, something about
ETFs real cool, you can do it. Yeah, but you
know there have been attempts if you scraped social media,
et cetera and find out what people are buzzing about.

Speaker 3 (40:06):
But it's the old value manager's lament.

Speaker 5 (40:09):
But it's also I think true if ultimately it's a
bad investment in return sense that will happen eventually.

Speaker 3 (40:17):
I do think that time horizon and the extremes have
lengthened a lot of the point of this way, Why
will it happen eventually? Why will it happen eventually?

Speaker 5 (40:24):
Well, if you buy a company that continues to perform
poorly and you paid a ton for it, I think
there's only so long that can go on. I think
it gets more and more obvious.

Speaker 3 (40:35):
For one thing.

Speaker 1 (40:35):
I think a lesson of the meme stack and frankly,
if crypto is that fundamental sety floor and valuation. But
you know, you know he's like doing LBI, right. But
I don't want to just be mean to bitcoin for
no reason. But like, you can certainly have a model
in which you say the fundamentals of bitcoin are nothing,
and you could then say and in fifty years it'll
go to zero, but like that's a weird thing to say. Now,

(40:57):
I don't know.

Speaker 5 (40:58):
Well, let me be clear, one short bitcoin with my
worst enemies portfolio, and I one hundred percent sure I'll
never say money is a little bit of magic. What
becomes money is a little bit of magic. But I
think most of the probability is eventually zero, but it
may be a very long time arizon. I think what
we've learned watching this stuff is that time horizon is lengthened.

Speaker 1 (41:17):
Okay.

Speaker 5 (41:19):
In my piece on this, I try to hypothesize, and
I admit it's real opinionated hypothesizing. You cannot prove if
I'm right that markets are somewhat less efficient. Why gets
even harder. But one of my favorite explanations is an
old man complaining about social media and twenty four to

(41:40):
seven gamified trading. I don't think most people need a
lot of convincing that this stuff has made, say, our
politics worse, made us more in bubbles hate each other
more when it was supposed to make us love each
other more. Marcus are just voting mechanisms. I don't think
it's any different than politics. So this notion that things
get crazier and can go on for longer, and I
have a very cynical view of your statement that this

(42:02):
stuff might take fifty years. I think the more absolutely
unsubstantiated by anything something is, the longer the craziness can
go on.

Speaker 1 (42:12):
Right. I think that if you compare the longevity of
bitkind to like the game Stop premium, I think right.

Speaker 3 (42:17):
But go back to the tech bubble.

Speaker 5 (42:19):
Cisco Systems great company, but was selling at a very
stupid price, selling it about one hundred PE when the
e was gigantic. You can have a small startup company
it's growing super rapidly, sell at one hundred pee.

Speaker 3 (42:31):
One of the largest companies in the.

Speaker 5 (42:32):
World with some of the largest earnings in the world
selling at one hundred pe. You needed some very heroic
and I would argue near. I'll never say impossible, but
near impossible assumptions even if the tech bubble didn't break
in March of two thousand when it did, and by
the way, I still don't know why it broke in
March of two thousand and not a year earlier or
a year later. Once you're well passed what I would

(42:53):
consider rational saying you know exactly where but the time horizon,
you can imagine that going on for when the growth
is good, maybe even great, but not nearly enough to
justify that price. It gets more and more obvious if
something is based completely on air. One of the weird
of down Matt's point that there's there's no arm to

(43:14):
the upside, there's no LBO mechanism, and shorting it is
just frankly too dangerous.

Speaker 3 (43:19):
It's a weird way to say.

Speaker 5 (43:20):
Sometimes people say markets are efficient because you can't make
money from these things, and I'm like, it's another weird
way to defend efficient markets to go they can be
so friggin stupid that they're terrifying to make efficient. That
may be totally true, but it also is a weird
way to argue that markets are efficient.

Speaker 4 (43:38):
What does it mean for AQR if markets are less efficient.

Speaker 5 (43:41):
Well, any active management is an inherently arrogant act. You
cannot tell the average person we should all be active managers,
we should all have podcasts.

Speaker 7 (43:51):
But I agree, So it's it's top to believe you
should be an active manager and to believe you're doing
something good for your clients, you have to believe two things.

Speaker 5 (44:03):
That you have alpha, and that you're not charging the
full extent of that alpha through your fees. And we
do believe that, and a lot of active management managers
believe it. But it's an inherently arrogant act. It's consistent
to say most people shouldn't do this, but we should,
though the arrogance is obvious.

Speaker 1 (44:21):
What percent of your alpha should in your face?

Speaker 5 (44:24):
That is a super hard question. I've thought about writing
about this at one point. It kind of depends on
how unique your alpha.

Speaker 1 (44:31):
Is, Okay, because I would think that if you got
a podshot manager really drunk, they would say, no.

Speaker 5 (44:40):
That's what they can charge for their fees, not what
they should charge. I don't think they'd ever admit them
at ALGI, like Wilvers shall.

Speaker 1 (44:49):
Deef in their soul. They want one hundred and ten percent.

Speaker 5 (44:52):
It's a great question, but it's a hard question if
your alpha is doing FOM and French price to book.
By the way, I still think Farm and French are
going to be right in the next thirty years. They
haven't been right in a while, but spreads have gotten
wider and wider, and that's been a wind in their face.
I wrote a piece on this saying the long run
is lying to you, saying that x the spread widening
values to the simple Farm of French value has delivered alpha.

Speaker 3 (45:15):
It's just lost on the repricing.

Speaker 5 (45:17):
But what you can actually charge for doing price to
book should be very low, a very small fraction of
the expected return.

Speaker 1 (45:25):
That's not alpha data, right, But.

Speaker 5 (45:26):
That's kind of what we're saying. Everything exists on a
spectrum from one to the other. If you have discovered
and built the database yourself, an alternative data source that
you have built. This is extreme and I can't think
of an example at AQR that fits this. But you
can charge nearly one hundred percent of that alpha because
what they're getting is still absolutely unrelated to everything else

(45:47):
what they're doing. There is some deminimous notion that if
you charge ninety nine percent, maybe no one would bother
to do it. But you can charge a large fraction
of the alpha because what you're delivering is still ultimately
net returns that you can not get elsewhere that aren't
correlated to the rest of the portfolio are worth it.

Speaker 1 (46:06):
We can talk abstractly about what percentage of your alpha
you should charge, but like, no one could send a
bill for like ninety percent on the alpha, right, Like
is pricing sort of like set by just like anchored norms.

Speaker 5 (46:17):
Anchored norms is another way of saying, what are other
people charge or.

Speaker 3 (46:20):
In the ballpark of what you're doing?

Speaker 5 (46:22):
So of course that matters, right if you are way
off the anchor, way off on the high side, no
one should invest with you.

Speaker 3 (46:29):
If you're way off on the low side.

Speaker 1 (46:30):
People who charge really high fees and are pretty good
at demonstrating they have alpha, and oh.

Speaker 5 (46:36):
Some of the block shops charge insane fees and have
been very good, And that goes to that are they
doing something unique?

Speaker 3 (46:43):
And I think to some extent they are.

Speaker 5 (46:45):
I think their problem becomes kind of in the direction
of Medallion without going all the way. I don't think
they've rebuilt medallion. Nothing is that, but they should charge
a higher percent if they're doing something very unique, and
they do. I have a very you can you know
violin playing I think rosy view of how we think
about fees. We're building a long term business. We think

(47:06):
we have business value. We do not think we're just
a hedge fund. We run a lot of traditional assets too,
even the hedge funds.

Speaker 3 (47:12):
We were a big.

Speaker 5 (47:14):
Pioneer in doing the more obvious strategies and considerably lower fees,
starting in merger, ARB and TREND. Following the way we
broke into some of those as standalone products, not as
part of our multistrats was charging less and saying, you know,
this is real and it's good. But you know you
can do one of every merger and make a fair

(47:34):
amount of money. But that's not magic, and we shouldn't
charge magic fees for it. But in that kind of kumbay,
a big picture sense, charging fees such that clients are
happy with the long term results is probably how you
build business value if you step.

Speaker 4 (47:50):
Back from just AQR though, I'm curious to hear your
thoughts on your industry overall and whether alternative strategies in
general are too expensive.

Speaker 3 (47:59):
Yes, they are.

Speaker 4 (48:00):
I have some numbers to back up that statement. There's
a new study out there. My understanding of it is
that you basically take a sixty to forty since two
thousand and eight, you add Alt's exposure to it in
various proportions, and that blended portfolio basically trails that benchmark,
basically in close proportions to the fees that are charged
by some of the ALTS managers.

Speaker 2 (48:21):
And I don't know.

Speaker 4 (48:23):
I read that and I was just kind of wondering.
I mean, you could make the case that why are
we charging so much?

Speaker 5 (48:27):
Well, this is almost a mathematical certainty. Yeah, forget about
ALTS for a second. You cannot tell me the average
active portfolio beats the market after fees and costs.

Speaker 3 (48:42):
The average active adds up to the.

Speaker 5 (48:44):
Market because for every deviation one way, there's deviation the
other way. When I said it to inherently an arrogant
act to be an active manager, it means you think
you got it, even though if you buy one of
each you can't have it. I mean a subset of
the market like could. But I think a lot of
that still applies. It's inherently arrogant act. But we've been

(49:05):
saying this for at least twenty four years. We wrote
a paper, and yes, I started a lot of sentences
with We wrote a paper in two thousand and one
called do hedge funds Hedge where we took the known
indices of hedge funds and we tried to take out
just the market beta. You could argue for a more
sophisticated risk model, and that could as usual and go
down that rabbit hole. But we found that betas were.

(49:27):
First of all, this is more mundane but statistically underestimated,
partly because a lot of hedge funds do some stuff
that is not of perfect liquidity.

Speaker 3 (49:36):
And this was a small.

Speaker 5 (49:39):
Early version of what I got into at the private
world later on. But if something doesn't trade all the
time and you try to estimate its correlation with the market,
you will underestimate it because one great way to look
uncorrelated is to have a three day leg, and when
you trade, your returns will be off. So the betas
were underestimated by earlier studies. Given the correct betas, there
was pretty much no alpha to the hedge fund world.

(50:02):
First of all, I was a lot younger than a
lot less well known, so I cared Nowadays, I quite
obviously court controversy. But back then I probably had ten
famous managers call me and yell at me about that paper.

Speaker 3 (50:14):
Yeah. The first time, I was, of course obnoxious.

Speaker 5 (50:16):
They called and said, why did you write this? And
I gave the obnoxious answer because we think it's true.
Now it was apparently not an acceptable answer. I will
only call out one person on the positive side, Richard Perry,
famous hedge fund manager.

Speaker 3 (50:30):
He called me up.

Speaker 5 (50:30):
And I already been yelled at by a whole bunch
of people. And so when I heard Richard Perry on
the phone, people like Richard Perry didn't call people like
me back then.

Speaker 3 (50:38):
He was big, I was small. So I'm like, I
know what this is.

Speaker 5 (50:40):
He's just gonna yell at me. He gets on the phone,
he goes, that paper you wrote.

Speaker 3 (50:43):
That's just correct.

Speaker 5 (50:44):
Good job, right, And I'm only telling that. I'm not
giving the names. And I do remember him, of.

Speaker 3 (50:49):
Course, in a book somewhere. It's all I have a
list of Vedettas and my BRAINSU.

Speaker 1 (50:56):
Wait, so you've been You've been making fun of private
equity managers on some lines recently. Do you get calls
from them?

Speaker 5 (51:02):
No, they're so fat and happy, they don't even care
about me making fun. No, I get yelled at by
some usually friends. I live in Greenwich connection, so you
all hang out.

Speaker 1 (51:12):
The country club. They're like, I'm.

Speaker 5 (51:14):
Actually a country club, but the old often used in
a very bad way. Some of my best friends are
so I'm off the hook. I have some good friends
who are private equity managers. Most of them can accept
the you're right about the industry but not our firm,
which is.

Speaker 3 (51:30):
Essentially what I'm saying. So I can't not being mean
about this.

Speaker 1 (51:35):
Right, You're not like, your criticism is volatility lunder right.
Your criticism is that private equity seems to have a
higher sharp because it has a lower of volatility because
it doesn't report.

Speaker 5 (51:46):
Yes, that is my main criticism, which I think is
quite obviously true.

Speaker 3 (51:50):
That's just me. Some people do disagree.

Speaker 5 (51:53):
The best disagreement I've heard, and I have some sympathy
for this because I do not think markets are perfect,
is we are right about the valuations. You are right
that we move at a highly damped version of the market,
but market moves too much.

Speaker 3 (52:08):
We are right.

Speaker 5 (52:09):
My response to that is, why don't we get to
do that?

Speaker 1 (52:11):
It's fair?

Speaker 5 (52:12):
You know when we've had a tough year because the
market's gone crazy and we were on the wrong side
of that. I have to tell my clients we're down
twelve percent. I don't get to tell my clients we're
up based on where I'd mark the portfolio. So why
one group? And by the way, they could market just
like we market. They're brilliant at valuing companies and they
can tell you where they could sell it for today.
So it's like an institutional legal quirk that they get

(52:35):
to do it one way and we have to do
it another. Everyone can mark their portfolio at what they
could sell it for today or what they think it's worth,
and one side gets to do it one one gets
to do the other. So that's a reasonable argument, but
I still think it leads to an unreasonable conclusion. I
will say ninety percent of my critique is about the
volatility or the beta, is about saying these things are

(52:57):
low risk.

Speaker 3 (52:58):
Ten percent is.

Speaker 5 (52:59):
About percent if not trailing future returns, because if I'm
right about the volatility laundering, it has implications for returns
going forward. If when David Swenson was pioneering, which is
what he called his book, Private equity is part of
an institutional endownment portfolio.

Speaker 3 (53:17):
He's quite clear. It's a lot of it. It's an
ill liquidity premium that no one wants.

Speaker 5 (53:21):
Illiquidity. Everyone's scared of it. So if you're willing to
do that, you get paid extra. If I'm right that
people love the fact that they don't have to look
at the volatility, that means illiquidity is no longer a bug.
It is now a feature and very simple model for
how expective returns are set. You get paid a higher
expective return in something if you have to bear a

(53:42):
bug nobody wants and you pay through a lower expector return.
If you have a feature everyone wants, then you have
to pay up for it. So the chance that that
is going on going forward, I think is quite high.
Whether it means there's no edge to private equity or
a negative edge or a smaller positive edge can tell
you that. But I do think if you think of
risk ajusy return as numerator of return denominator of risk,

(54:06):
I think my statements are mostly about the denominator, but
they're ten percent now about the numerator.

Speaker 1 (54:11):
Going forward. You're in my concernami private equity, private markets
by which is that it seems to me that like

(54:32):
there is a ton of fee pressure in public markets
and everyone has kind of like learned the gospel of
like Bilow cost index funds and even charging for farma
French factors is not a two and twenty business. And
private markets because they're not indexible because it's harder to
like extract factors because they're not liquid, don't have those problems,

(54:54):
and you can charge two and twenty for a lot
of private stuff. And so like it seems to me
that there is a move to you put a lot
of stuff that would have previously been public into private markets,
and to say we can put privates into four oh
one ks and there's a good economic grastionals we're putting
private assets into four one case because you don't need liquidity.
But there's also like this like really overwhelming if.

Speaker 8 (55:14):
There's a positive illiquidity premium. Yeah too right, sorry, go no.
I just like you know, you've written for years ago
like criticizing people for charging alpha fees for beta, and
it seems to me that like the reprivatization on the
market is a way to sneak some alpha fies onto beta.

Speaker 5 (55:31):
I think a tremendous amount of the private world is
charging massive alpha fees for beta.

Speaker 3 (55:36):
I won't mince words about that.

Speaker 5 (55:38):
If they outperform or underperform on net after all these
massive fees, and if performance going forward, it is tougher.
And by the way, your point about the fee compression
in the public world only makes my hypothesis that they
won't beat the public world by the same amount going
forward stronger. I think privates have a big function in
the world. I don't think they're going away what you
started out with me, what do you guys do for

(55:59):
the world. There are things that are in between what
should be public and what's mom and pop. But I
think where we are now, I think a lot of
institutions are giving up some amount of expected return for
the ease of limit, of reducing their agency problem of
sticking with something. Now, if they're going to be terrible
and not stick with things, it might be rational to

(56:21):
give up some return. But you can't double count and
say we're making ourselves better investors giving up some expected
return and oh yeah, our expected returns are going.

Speaker 1 (56:30):
To be higher.

Speaker 5 (56:31):
If that's the rationale, then you've accepted my argument, and
I think you have to say, this is what we
get paid for by making your life easier.

Speaker 4 (56:39):
I am curious what you make of the push to
put privates into more retail accessible wrappers. I'm talking about
et avs, but I guess I'm also talking about interval
funds a little bit.

Speaker 2 (56:49):
It just seems like that's where the world is headed.

Speaker 5 (56:51):
I'm going to hedge this and say it very carefully.
I think it's a terrible idea.

Speaker 6 (56:55):
Okay, go on.

Speaker 5 (56:56):
There are things that end up in retail in a
very good way eventually, but we've done some of this
when we introduce mutual funds. I can't say always going
to retail is a bad thing. You can price it reasonably,
you can say these are strategies you've never had before.
This feels a little bit more like we've exhausted the institutions.
I think I saw a number saying endown. It's like
forty three percent for a number I can't verify. That's

(57:18):
wildly specific, but that's the number I remember. So it
has a feel of who else we're going to get
to own this stuff.

Speaker 1 (57:25):
It seems so explicitly, and it's really wild.

Speaker 5 (57:27):
I think it's explicitly that it's one of these things
that for the cynics, I don't think they'll ever be
a satisfying moment where we get to say we were right.
It'll just be somewhat worse over the next decade. It's
not one of these things like a tech bubble in
ninety nine two thousand that at least, I don't think
there are scenarios where things get worse rapidly and secondary sales.

(57:50):
We got close to some of that in the GFC.
I was on some investment committees where we were talking
we didn't do it, but we were talking about whether
we would have to do that. So I'm not saying
it's a chance of ugliness, but I think the meat
of the probability is is just somewhat worse. But it
does cause me some worry sadness to say, yeah, what
we really need is to stick a liquids in four.

Speaker 3 (58:11):
To one case.

Speaker 2 (58:12):
Yeah.

Speaker 3 (58:12):
So yeah, bluntly, I think it's a bad idea.

Speaker 4 (58:15):
Well, I just wanted to talk about and I don't
know if this goes too far, but it feels like
you had a change of heart when it comes to
machine learning and AI in general.

Speaker 2 (58:22):
This is something we've spoken about before.

Speaker 4 (58:25):
I did well, was that like a light bulb moment
or was that just you know, maybe people on your
team wearing you down over time?

Speaker 3 (58:31):
It was more the ladder.

Speaker 5 (58:33):
Yeah, the ladder is the second one, right, I gotta
think that through every time I tell that people this.
I've said in a lot of public arenas I sell
it to clients. I think I probably slowed us down
by a couple of years in machine learning. I think
it probably costs us some money because the stuff has
worked pretty well. We've always described ourselves and we were
getting into this a little bit earlier. As a blend

(58:53):
of data, back tests are nice out of sample long
periods are even nicer, but also theory.

Speaker 3 (59:01):
Theory can be a.

Speaker 5 (59:02):
Formal economic theory, but it can also just mean a
common sense story where you think you understand why you're
making money. And there's no way to say exactly what
percentage of both. But I've often described it as an
attempt to be fifty to fifty. We want something to
make sense to us as economists and have strong data
when you move into the machine learning world. I don't
think you have to abandon theory, and this is something

(59:23):
I think we do a little different than most I
think there are some in the machine learning world to
kind of throw theory out the window. We're still using
common sense and theory to kind of limit the scope,
but you are leaning more on the data. And again
I'm making up these numbers. But if regular stuff is
fifty to fifty, machine learning seventy five twenty five data
even for us, that was uncomfortable for me.

Speaker 3 (59:45):
When you've been telling a story.

Speaker 5 (59:46):
For twenty five years and it's worked for you and
your clients, it's not easy to move. I also think
it's not improper for the role for the old man
of the firm to go, let's.

Speaker 3 (59:57):
Just slow down.

Speaker 5 (59:58):
You guys come in here doing this with a Southern
droll and your new fangled machine learning. No, that's the
villainous rule.

Speaker 2 (01:00:06):
Oh no, I think it's kind of cool.

Speaker 5 (01:00:07):
Yeah, okay, so I actually can defend it as entirely appropriate.
But yeah, I had to be convinced. It wasn't a
light bulb moment. It was people like Brian Kelly, Andrea Ferzini,
Laura Serb and all partners of mine presenting great results
that made increase it and I did a lot more reading.

Speaker 3 (01:00:23):
I was probably I.

Speaker 5 (01:00:24):
Programmed in a programming language called LISP in the nineteen eighties.
That was an early machine learning or AI language, not
even machine learning, and then I didn't do anything about
that for the next thirty years. So I think part
of it was just me getting up to speed.

Speaker 1 (01:00:38):
Frankly, the thing I find compelling in the Kelly paper
is like you seed like a sort of toy set
of factors into like a ten thousand neuron model, And
what he argues is basically you might get a better
view of the actual like function that generates the results
than if you just try to like use your economic
intuition in a lineary aggression.

Speaker 3 (01:00:58):
Right.

Speaker 1 (01:00:58):
There's like theory behind it. In the theory is like
things are not as linear as you know traditional methods, and.

Speaker 5 (01:01:04):
It's basically saying it's still a sin to only look
for patterns. You still need some economics, but machine learning
is better at balancing those trade offs. So the idea
of throwing more at it when you have a better
technique for that, you lean in that direction. I did
have a better title, I think than him. I wanted
him that's not better. His titles great pretty well, no,

(01:01:26):
but I wanted to call it simply. Ackham was wrong.

Speaker 3 (01:01:31):
Okay, all right, and maybe not bad, but I still
want him to use that for maybe a follow up paper.

Speaker 1 (01:01:37):
All right, Cliff, thanks for coming.

Speaker 3 (01:01:38):
Oh, this was fun.

Speaker 2 (01:01:39):
Thanks for video with us.

Speaker 1 (01:01:47):
And that was the Money Stuff Podcast.

Speaker 2 (01:01:48):
I'm Matt Levi and I'm Katie Grifeld.

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