Cliff Asness on How Markets Got Dumber in the Last 10 Years

The Odd Lots podcast has been around for 10 years. Unfortunately, markets have gotten less rational over the same time frame. At least this is the contention of Cliff Asness, the co-founder and CEO of AQR Capital Management, a quantitative investing firm that's been around for nearly three decades. Asness' approach to investing is rooted in academic theory, having studied under the legendary Eugene Fama at the University of Chicago. In the world of social media and meme stocks, it's tough out there for the academically minded. And that's forced Cliff to adjust his approach over time. On this episode, we talk about the history of quantitative investing, market efficiency, and the emergence of AI/ML in his process. We also talk about the reality of investing other people's money, and the challenge of sticking with one's convictions at a time when temporary forces are working against you.

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Episode Transcript

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

Speaker 2 (00:18):
Hello and welcome to another episode of the All Thoughts Podcast.
I'm Tracy Allaway.

Speaker 3 (00:22):
And I'm Joe wysnent Thal.

Speaker 2 (00:23):
Joe, it's a big month for us.

Speaker 3 (00:25):
Big month for us. We've been doing this for ten years.

Speaker 2 (00:27):
I know, I can't believe it. Do you remember the
first episode?

Speaker 3 (00:31):
Yeah, of course with Tom Keane.

Speaker 2 (00:33):
Yeah, But and then our second episode I think was
about bananas for some reason, it was You're.

Speaker 4 (00:38):
Right, it took us a long time to figure out
what we were doing. It took us a long time
to figure out what we were doing, and I don't
think at that point I would have expected that we'd be.

Speaker 3 (00:48):
Doing it ten years later. I don't know what I
was expecting. I think turning on a microphone in a
radio studio and talking for a while.

Speaker 2 (00:54):
We started doing it because we wanted to have a
podcast and talk to interesting people. I think we were
hashtag bless. No one was listening for a very very
long time, which gave us a long runway to figure
things out. So we got lucky. That said, you know,
ten years, it is, in fact a long time to
be doing this, and a lot has changed in that period.

Speaker 4 (01:14):
A lot has changed in that period, sometimes mind blowing.
And we've talked about this before for sure, But the
things that we were covering is capital and news at
the time are now capital age history.

Speaker 3 (01:25):
And it's like these things that are we sort of
take for granted. Everyone was there.

Speaker 4 (01:29):
It's like, no, children, let us tell you what it
was like in the old days when people were worried
the world was going to come to an end because
you know, Greece is sovereign debt and all this stuff
that we just sort of part of the landscape is
people don't remember it.

Speaker 2 (01:43):
No, one of those things has to be the idea
of value or fundamental investing, right, Like, let us tell
you about the days when price actually mattered and had
a limit to what investors would pile into.

Speaker 3 (01:54):
Yeah, that's exactly right.

Speaker 4 (01:55):
Let us tell you about the days when people used
to talk about pe ratios and this stock, Oh so
the twenty five pe we better sell it and buy
the stock at a fifteen p or whatever.

Speaker 3 (02:04):
Yes, that feels quaint.

Speaker 4 (02:06):
Maybe it'll be back there one day, but for now,
given how many things in the market seem to be
the Graham and DoD kind of stuff, feels a little.

Speaker 2 (02:15):
Old, a little old fashioned, little old.

Speaker 5 (02:17):
Yeah.

Speaker 2 (02:17):
Okay, So one of the big themes that has emerged
in the ten years that we've been doing this podcast
is everyone seems to have grown more stupid. I would say,
hopefully that's not related, so that's not like a correlation thing.
It could be all right, but you know, we have
all this gamification of investing, people betting onlines going up,
we're down, people betting on random meme coins, things like that,

(02:40):
and I think, you know, we talk about it a
lot on the podcast, but this is actually a fundamental
shift in the market. If you think about the market
as something that's supposed to be about capital allocation, alignment
of incentives. People are investing in something because they think
it's going to be profitable in the future at the
right price. And now people are just sort of piling
into stuff because other people are doing it, and again,

(03:01):
line go up.

Speaker 4 (03:02):
Deep down, I still believe that the value of a
stock should reflect the net present value of all. I
know you're an MH guy, but I've it's been a
little bit hard with some of these things. And you
know the other thing too, that is sort of changes
that like half of our episodes these days are kind
of AI related in some way, and so I think
there's a lot of interesting stuff going on, particularly at

(03:23):
the intersection of tech and applying tech to both investing
in tech, but then the application of tech to investing
and so forth.

Speaker 3 (03:30):
So yes, much has changed.

Speaker 2 (03:33):
A lot has changed, and we have the perfect guests
to talk about how everyone has grown more stupid over time.
We're going to be speaking with someone we've wanted on
the show for a really, really long time. I'm very
excited about this. It's Cliff Asnas, the co founder and
CIO of AQR. Thank you so much for coming on.

Speaker 5 (03:49):
All thoughts, Thank you for having me.

Speaker 2 (03:51):
What's it like to be a rational person living in
irrational Cliff, Yeah, you've been doing it for a long time.

Speaker 6 (03:57):
Well, a rational person is not always how I'm described,
but this rational investing and in this rational conduct in
your personal life.

Speaker 5 (04:04):
So let's just distinguish those.

Speaker 6 (04:06):
You guys in your intro said like, it's a it's
a little scary for the next hour because you said
like a third of the things I want to say. Oh,
I wrote a rather gigantic piece in the Journal of
Portfolio Management. They were having a fiftieth anniversary. None of
us were quite old enough to have been there in
the first issue, but they were looking for the old
guys to write kind of retrospectives, and the piece I

(04:28):
wrote was called the less Efficient Market Hypothesis. Now, I
think you guys probably know this, but my dissertation advisor
was a little known guy named Eugene Fama.

Speaker 3 (04:39):
We've heard of.

Speaker 5 (04:40):
I was his TA for two years. I grew up
in EMH.

Speaker 6 (04:43):
I love that you guys just say EMH and your
audience knows where you're talking about. That is not the
norm for me when I talk about these things. I
was not a perfect efficient marketer even back then.

Speaker 5 (04:55):
Neither is Gene.

Speaker 6 (04:56):
By the way, Genes not a zealot about third week
of class. I know this because I took the class
three times. I didn't fail, but as the TA, I
sat through it three full years, and like the third week,
he always tells the class.

Speaker 5 (05:09):
Markets are almost certainly not perfectly efficient.

Speaker 6 (05:12):
Because Jean's a brilliant guy and recognizes that perfection is
a really stupid idea.

Speaker 2 (05:17):
He's been on the show. By the way.

Speaker 6 (05:18):
Oh, I don't know, yeah, yeah, And I don't know
if that came up, but he's always very honest about that.
He probably thinks they're considerably more efficient than I do
these days, and I think I probably think it's they're
more efficient than maybe.

Speaker 5 (05:28):
The active average retail trader.

Speaker 6 (05:31):
But I wrote a dissertation for him on the success
of price momentum. That is not a very gene Fama dissertation.
A gene Fama dissertation is I've studied price momentum and
it loses gobs of money, and these idiots on Wall
Street do it anyway. That's kind of a fishing market.
It's lot how silly they are. And he was great
about it. I remember I kind of mumbled, I'm like,

(05:52):
I want to write a dissertation on price momentum, and
by the way, I find it works very well.

Speaker 5 (05:57):
What was that, cliff?

Speaker 6 (05:58):
It works very well? And he said, if it's in
the data, write the paper. So I've drifted at least
a little bit further from efficient markets even way back then. Now,
price momentum, I'll do a lot of segues. You're gonna
have to stop. I do parentheticals within parentheticals. Price momentums
often thought of as this index of irrationality. It can

(06:18):
work for two different reasons. It can work because of yes,
feedback loops, chasing line goes up just like you said before,
and people pour in, which isn't very connected to reality.

Speaker 5 (06:29):
It's just chasing returns.

Speaker 6 (06:31):
But it can also work because of what the behavioral
finance people would call underreaction. News comes out that should
move the price by so and so on average. We
have found, I say we it's the royal we of
academia and private researchers that the price moves the right direction,
but doesn't move all the way. So if you trade
on that, there's still a little bit to go. That's

(06:52):
a quant thing. You wouldn't want to bet on one
stock that way. But if on average that happens and
you could do that through observing the price or the fundamentals,
usually a little bit more to go. So it's not
always irrational momentum. But here's what I observed in the
very beginning of AQR. EQR launched in nineteen ninety eight,
after we had a fabulous run at Goldman sacks this

(07:13):
survivorship bias. In this you don't get to start your
own billion dollar hedge fun unless you have a fabulous
runner at Goldman sacks or something equivalent. We had a
good first month. You know how the story is going
to go when you say you had a good first month, right,
And then that first month, by the way, it was
August of nineteen ninety eight when the S and P
was down twenty percent on the Russian debt crisis, and
we're doing high fives, like we say, we're marketing neutral

(07:37):
and we're up a little bit in a crash. And
it was my first of many lessons never to high
five in this business. When you fully retire and divest,
you get one high five.

Speaker 2 (07:49):
But that the well, no, it's the end when you
do the high five.

Speaker 6 (07:52):
I'm a kwant who is also superstitious, which if that's
a contradiction, what the heck. But the next eighteen months
was the crescendo the famous dot com or tech bubble,
and that was not kind to us. It was particularly
not kind because we decided to start with a extremely
aggressive market neutral fund. So momentum helped, as you can

(08:13):
imagine in a bubble, but value was just destroyed and
that was most of the model back then. Things have
really broadened out. We hit spreads between cheap and expensive.
This is something we invented at the time, and now.

Speaker 5 (08:23):
A lot of people do.

Speaker 6 (08:24):
People said shortstocks on valuations go along in the cheap,
short the expensive, But the very obvious question of how
cheap and how expensive are they sometimes pretty tightly clustered?
Are they sometimes wider? Does that mean they're better or
worse going forward? Was not asked before. So we invented
this measure, and the spread between cheap and expensive for

(08:44):
fifty years had looked like a well behaved series.

Speaker 5 (08:48):
It moved around a fair amount.

Speaker 6 (08:51):
And then I'm drawing on my hand if only see
the video, Yeah.

Speaker 2 (08:54):
I should mention this is an audio medium.

Speaker 6 (08:57):
And then in late ninety nine two thousand and went
to just way wider than anything ever seen for fifty
plus years. We also showed that historically those were better times.
You never saw that before. But when it was wider
or better times for value, we stuck with it. We
made money round trip life was good.

Speaker 5 (09:16):
If you had.

Speaker 6 (09:17):
Asked me after the round trip, which was harrowing. You know,
you know, even if we love our process, you never
want to start a business with poor returns. If you
ask me at the end of that, which is probably
a couple of years later, two thousand and three. Do
you think you're ever going to see that in your
career again? No one asked, thank God, because I think
it would have gotten it wrong. But I think I
would have said, oh, probably not hopefully. I wouldn't say definitely.

(09:38):
No one who does what any of us do for
a living should say definitely. That's a bad word in markets.

Speaker 2 (09:44):
Is the favorite term.

Speaker 5 (09:47):
But A it was the.

Speaker 6 (09:49):
Craziest thing numerically in fifty plus years. B. The question
presupposed is it's built in that I and people of
my core will still be around right and will probably
be are to in charge. So how's it going to
happen again? And then it happened again even before COVID.
By late twenty nineteen, that spread between cheap and expensive

(10:09):
was approaching dot com extremes, and then it blew past it.

Speaker 5 (10:13):
In COVID.

Speaker 6 (10:14):
It went to what I in a geeky math joke
that no one ever gets called one hundred and twenty
fifth percentile. There is no one hundred twenty five percentile.
It's just a new hundred percentile. I'm trying to convey
that it went further, and we survived that one too.
We suffered somewhat, and then we made more than all
of it back ground trip good. Most of the time,
we really don't look like value investors, by the way,

(10:35):
only in extreme bubbles do we have seem to have
that property.

Speaker 5 (10:39):
And I think it's smaller now than it used to be.
Last five years have been quite strong for us, and
it's not been a very good value market.

Speaker 4 (10:45):
God, there's so many questions that I have that I
want to ask that are sort of embedded or related
to your answer. I'm going to ask at you just
like a very narrow question, sort of skip ahead into something.

Speaker 5 (10:56):
Though.

Speaker 4 (10:57):
You make this spread between the most expensive than the
cheapest stocks, how much easier it is to simply compile
that spread? Is it today versus the technology that you're
working with when you're starting your career?

Speaker 6 (11:11):
For that, I got to disappoint you and say not
that much. Here we had the databases. A lot of
technological investment is about speed and new data sets. What
quanto Alternative data is something we're very into. But the
classic data if you're looking for price to sales ratios,
I'm old, but we had we have telephones and Bloombergs.

(11:34):
But that does bring us to me observing these two episodes,
and I stepped back and I asked a question, what
the hell happened? Why did we see something crazier than
fifty years? And then why did it happen again? And
that led to this paper, the less efficient market Hypothesis.
I do believe that markets have shown and I think

(11:55):
I have some good guesses as to why that they
are more susceptible to bouts of crazy than they used
to be.

Speaker 4 (12:02):
Just before going further, one quick definitional question efficient markets,
because one way you could define whether market is efficient
is are these securities disconnected from what you would say
the net present value of the all future free cash flows.
Another way that I often think about it, but I'm
no kuant is are there obvious opportunities for the active

(12:23):
manager to make money?

Speaker 3 (12:24):
Just for our purposes here? How do you define market efficients?

Speaker 6 (12:28):
Much closer to the first one, Okay, The practical question
of can you make money from these is if there
are big deviations from fair value? And this relates to
some stuff I did in a less efficient market hypothesis.
If there are big deviations the classic and you'll notice
a giant caveat if you can stick with your position
not a small thing.

Speaker 5 (12:48):
You will make money.

Speaker 6 (12:49):
There are opportunities, and in fact, I think a less
efficient market in that sense, in your present value sense,
almost has to deliver bigger opportunities for people who can
stick with it. It also makes it considerably harder to
stick with because the extremes you have to live through
and the length of time those extremes can go on.
For one thing, that I'm dying for an academic to

(13:10):
take me up on this. I'm too old to do
the math on this, but I've never seen a model
that looks at pain, disutility, the negative of losing money
in terms of how long you've lost money for, not
just magnitude. Right, And in real life, I can tell
you a drawdown that is one and a half times bigger,

(13:30):
but with six months instead of three years, is ridiculously
easier to live through. So the bigger disconnect from reality. Again,
I haven't even told you why I think it's going on,
But if I'm right that we have these bouts of it,
it's not necessarily every day things are crazy, but these
bouts of it is a two edged sword. It's a
bigger opportunity for people who can stick with it, and

(13:52):
it's harder to do, and I find that not that
what I think is fair is particularly relevant. But I
find that really fair harder to do, but more lucrative
if you can do it. I don't think the efficient
market idea of buying what is fundamentally mismatched against its
future cash flows adjusted for as risk forecasting those cash
flows what risk really means? We still have some open

(14:13):
issues there. Yeah, but I don't think that will ever
go away unless markets are perfectly efficient. But how easy
it is to identify and how easy it is to
stick with. I think crazy markets make it easier to
identify what to do and harder.

Speaker 5 (14:28):
To deal like that.

Speaker 2 (14:30):
I do want to ask you why you think this
is happening, but before we do, this is a very
basic question. But sometimes the basic questions are the most interesting.
But when you say it's painful to try to stay
rational during these bouts of irrationality, why is that exactly?
Because I get that there are funding costs. I get
that there are carrying costs. But on the other hand,
you're a big hedge fund with deep pockets. This is

(14:51):
presumably what investors are paying you to do. Is it
just the emotional trauma or stress of having to explain
to everyone why you're making the position that you have
when it's not paying off yet.

Speaker 5 (15:04):
That is a big part of it.

Speaker 6 (15:05):
I have a running fight with one of my co
founders who never gets upset. I'm always upset, but I'm
more upset when we're losing money.

Speaker 2 (15:12):
This is sort of our dynamic. I have to I
get more upset than Joj.

Speaker 6 (15:15):
Hunhes where He'll come in my office during a bad
period and I'll be upset.

Speaker 5 (15:20):
It'll be a bad day in a bad period. He's like,
why are you upset?

Speaker 6 (15:23):
And I'm like, well, cause people are yelling at us
and I were losing money. And he's like, but you're
pretty sure.

Speaker 5 (15:30):
We're going to win, right. I'm like yeah.

Speaker 6 (15:33):
He's like, and you have all your own money and
your kids money in this right, so you're not doing
anything different.

Speaker 5 (15:37):
You wouldn't do that if you weren't. I'm like yeah,
He's like, so we're gonna win. It's just a question
of when why do you care? And I look at
him like he's from Mars and go, why do you
not care?

Speaker 6 (15:47):
And we finally figured out it's kind of obvious that
I talk to clients a lot more than he does.
It's a lot easier to have that attitude when you're
just sitting in your office. Also, we are not immune
from this. Even if people love you and think you've
done well for twenty five years, you have a bad
two years, you get redemptions, and sometimes they're of serious size.
We fell by like half over about three years, and

(16:10):
that's not fun. You have to shrink your firm, you
have to let some people you love go, so there
is some real pain that goes with it. That period
did not shake my confidence in the actual investment process.
I feel bad. I think I did better than our
investors because I kept adding and saying, take the ball
up on what I do. Not everyone can do that,
and I know more than they know. Not in a

(16:31):
weird insider sense and just a I should be more
confident in my own process than anyone else's. But it
is excruciating the amount. I remember someone I admire tremendously,
when Stan Druckenmeller retired to run his own money. He's
still very active in markets. I think he wrote a
note that resonated with me because I forget the exact details,
but the essence was, it's too painful and too upsetting

(16:54):
to run client money. The man never had a down year,
and I'm like, if Stan can't take it, those of
us and we've had a lot more up years than
down years and life's been good, or I won't be
sitting here. But we've had never three but two plus
years of pain, and I'm like, Stan can't take it. Man,
this is harder to do than it looks like.

Speaker 5 (17:14):
The whole world.

Speaker 6 (17:15):
Thinks you stupid when you losing money, no matter what
you can point to, no matter what evidence you can
point to, I should say, the whole world.

Speaker 5 (17:21):
Your mom still likes you, but a lot of your
world and no, a lot of investors stuck with us,
and I love them for it. But you do lose
a lot of people. So it's not fun for a
business to go through that.

Speaker 4 (17:32):
Intuitively, though, it's to your point measuring the disutility of
long draw downs versus deep draw downs, this must be
a very acute thing for anyone who's not just managing
their own personal portfolio. For the reason that you've just described.

Speaker 6 (17:47):
Yeah, I've talked about this with other money managers, this
idea of length versus severity. Yeah, and I've never had
one who's not like, yeah, length is much worse.

Speaker 5 (17:56):
Well. One of the things is when.

Speaker 6 (17:57):
Something goes on, it's often a similar story for two
and a quarter years. Right, So you go back after
six months where rationality is getting absolutely punished. You get
a lot of sympathy from people. You show them look
bigger bargains, We're right, we're gonna be right. You go
back six months.

Speaker 5 (18:17):
Later, they're like, Okay, you go back.

Speaker 6 (18:20):
Six months and six months later they're like, you're just
saying the same adventure.

Speaker 4 (18:24):
And eventually they must just think maybe you're a dinosaur,
Like maybe you just don't get this new wave that's
going on.

Speaker 3 (18:29):
Is it possible that we've really had.

Speaker 5 (18:31):
Again, it's not everyone.

Speaker 6 (18:32):
We have the tremendous amount of investors stick with us,
and we have ones who double up, who get it
that those are often opportunities. But you shrink when you
lose money for a while, and you grow when you
make money for a while. It's the ironclad rule of
this business. And sometimes it's just backwards.

Speaker 2 (19:03):
So one of the things that has been happening recently
that has changed from twenty fifteen when we started doing
this is retail participation in the market and it just
feels like it is such a big thing for retail
investors now to use things like options, even you know,
one or zero day options, the kind of stuff that
you might more traditionally associate with someone running a hedge fund.

(19:25):
Now they're trading Oh.

Speaker 5 (19:26):
We're not stupid enough, Okay, options.

Speaker 2 (19:29):
All right, some hedge funds. Then, does the increased presence
of retail in the market change the way that you
do business at all?

Speaker 6 (19:38):
I think it contributes to what we're talking about of
this dislocation. I hate this because I sound very elitist
when I diss on retail, but there's a lot of
academic work that shows retail on net loses net is important.
They go through periods where they win, They go through
periods where they win a lot. You know, if you
buy a memestock into triples tomorrow, you don't always lose.

(20:01):
But on average, retail transfers money to Wall Street and
to institutions. So if there are a bigger force in markets,
you're going to have more of that going on. And
it jives perfectly because that would raise the opportunity if
they're generally on the wrong side of things, but there
are more of them many more of them than they
used to be. They can be right even if they're

(20:23):
wrong on the facts. They can be right on the
numbers for longer than they used to be. So I
do think this is part of it. And I apologize
to all the really smart retail investors. Anytime you talk
about averages, you're dissing a whole lot of people who
don't deserve to be.

Speaker 5 (20:38):
But on average, retail loses zero day options.

Speaker 6 (20:42):
My god, they're making exactly the people they claim to
despise on Wall Street very rich by trading these things.

Speaker 5 (20:50):
And it's just fan duels. I love that.

Speaker 4 (20:52):
Well, all right, we got to get to the question
of why right, But one last question sort of leading
up to it.

Speaker 3 (20:58):
How do you establish that what do you look.

Speaker 4 (21:00):
At in the market that right now you say this
is a less efficient market than once it was. What
is the measure or is it just feel where you
just feel sort of crazy like all of us observe.

Speaker 6 (21:11):
Most of it is quantitative. I do start with this
thing I've talked about, right, It spreads between cheap and expensive. Yeah, yeah,
I don't just look at spreads. I wrote a piece
back in two thousand during the tech bubble it's.

Speaker 5 (21:23):
Called bubble logic.

Speaker 6 (21:24):
It never got published because I tried to make a
book out of it and the bubble came down too
fast for me. That was good for my business, but
bad form my author career. Where I didn't just look
at price multiples. I looked at it in a more
holistic sense. What growth do we need to justify these
multiples trying to come up The only time I will
use the word bubble is when I've tried very hard,

(21:47):
and it doesn't mean we'll come up with the same answer,
but this is the framework I use.

Speaker 5 (21:50):
I've tried very hard to come up with.

Speaker 6 (21:52):
Assumptions, even if I don't like them and think they're
at the outer edge of possible, that could justify these prices. Yeah,
and if they don't, I don't come close. Twice in
my career I've been willing to go no, I'm willing
to use the B word. I should tell you right now.
When it comes to within stocks, the whole market is
a different issue. The market is quite expensive right now.

(22:13):
But when it comes to this spread between cheap and expensive,
I'm not using the bubble word oka that same measure.
It's not the one hundred and twenty fifth percentile anymore,
it's the seventy seventh percentile. It's wider than on average,
maybe making it a little more attractive if you can
stick with it that big if. But I don't use
the word bubble for seventy seventh percentile. I used it

(22:33):
for blowing through. So five years ago I was saying
it's a bubble. Now I'm just saying this is a
little bit of an odd market.

Speaker 2 (22:41):
So I want to ask you more about the bubble.
But we keep touting that we're going to ask you
the why question. So let's ask the why question? Okay,
why are markets becoming less efficient?

Speaker 5 (22:49):
Okay?

Speaker 6 (22:50):
Well, first of all, and I say this in the piece,
a lot of conjecture going on.

Speaker 5 (22:54):
This is an op ed.

Speaker 6 (22:56):
As statisticians, it sound like a fifty page Yeah, fifty
academic look our first time doing this, you will be convinced.
I could do a fifty page, single space ofp ed.

Speaker 3 (23:08):
I believe it.

Speaker 5 (23:08):
I know you can't.

Speaker 6 (23:09):
It's just as a quant as a statistician, you'd like
to see a hundred bubbles have stats on each one.
This way, they're all they'd rhyme, but they wouldn't be
exactly the same you tease out what's going on. If
you see two in a thirty five year career, You're
not gonna be able to prove this statistically. But I
believe in my conjectures. I'm not soft selling them. I'm

(23:30):
I just wanted clear that nobody is going to walk
away saying.

Speaker 5 (23:32):
He proved it.

Speaker 6 (23:34):
I list a few reasons in the piece why markets
might be prone to bouts of bigger disconnects from reality.
My second favorite is one I'm sure you've talked about.
I know I've listened to you guys talk about it,
the rise of passive investing. I am not a passive hater.
They're really smart people. Mike Green's out there the Mount.

(23:54):
That guy hates passive.

Speaker 5 (23:56):
I don't know.

Speaker 6 (23:56):
The Middle East has never seen hate like the Mount.
Mike Green hates pass but again, he's a smart guy.
He makes interesting arguments. I'm not that guy. I think
passive has been a huge positive for an investor welfare.
I actually was lucky enough to be fairly good friends
with Jack Bogel, and he was a hero of mine.
We had a podcast briefly and he and he came
on it. In fact, i'll tell you part of that

(24:17):
story in a second. So I'm not a passive hater.
But here's what we know. We know the whole world
cannot be passive. And when I say passive, I mean
in a Jack Bogel market cap weighted sence. Some nice
people use passive for people like us, and they really
mean rules base and that's not how I use the
word passive.

Speaker 3 (24:35):
I mean someone who owns the entire mark.

Speaker 6 (24:37):
Yeah, we're long, short and leverage. How you get to
passive on that, I don't know, but some people do.
I own the entire market. We had Jack on the
podcast and he absolutely agreed everyone can't be passive. Now,
of course, being Jack Bogel, he thinks at that point
the marginal investor should still move to passive. But he
recognizes the obvious fact that if one hundred percent of
the people are not looking at prices, nobody's looking prices.

(25:00):
Who's figuring out if Nvidia is worth more or less
than the corner drug store? Right, So the market gets
very weird there. We don't even understand what happens. It's
a singularity. I use a physics analogy. We don't know
what happens there. PhD students in finance, and I used
to be one of these, will stay up late at
night in their cups talking about what happens if everyone

(25:22):
was passive. What would it even look like? We know
it's very weird, and I doubt all the weirdness happens
between ninety nine point nine nine nine percent passive and
one hundred. So we're on a curve. We're a lot
more passive than we used to be. Even that you're
probably aware, it's hard to measure exactly how much just passive,
direct passive true market cap weight you can measure. But

(25:42):
what about people who take you know, eighty basis points
of tracking era, they're kind of mostly passive.

Speaker 3 (25:48):
You have, remember, pegged.

Speaker 2 (25:49):
To like custom indices. Now, it's kind of funny you.

Speaker 6 (25:51):
Guys remember the Princess Bride? Yeah, yeah, remember mostly dead.
I'm fully dead. You're mostly passive. So I think fewer
people thinking about prices, fewer people willing to take the
other side when things get a little crazy. If one
side really starts to get crazy, there are fewer people
out there that has to exacerbate these swings. My actual

(26:14):
number one reason, though you talked about the gamification, for me,
it's the overall, I'm going to sound like a very
old man yelling at the sky on my lawn right now,
you know, get off my lawn, or.

Speaker 5 (26:26):
Yelling at the sky some Simpsons thing. Yeah, I'm abe
in this case.

Speaker 3 (26:31):
But Saniel's a cloud.

Speaker 5 (26:32):
Yeah exactly.

Speaker 6 (26:33):
Thank you social media and the broader environment that you
guys were talking about. I don't want to do politics
except to say I don't think you find many people.
There'll be some, but I don't think you find many
people who don't agree with the idea that this environment
has made our politics worse and more dangerous confirmation bias.

Speaker 5 (26:52):
We live in our own bubbles.

Speaker 6 (26:54):
We have algorithms that push us further and further towards
You start out as a moderate belief, but it keeps
pushing you towards extremes, and pretty soon you're saying stupid
things like, Hey, that Tucker Carlson.

Speaker 5 (27:06):
He's a good guy. All right, I might have revealed
a little poem.

Speaker 2 (27:09):
Yeah you did a bit of politics there.

Speaker 5 (27:11):
So all of that adds up to making our politics worse,
more prone, in particular to swings and extremes. Markets are
not arbitrage mechanisms that sometimes misunderstood they're voting mechanisms.

Speaker 6 (27:28):
The price is a weight at average vote, where the
weight is by dollars. I lucky enough to get more
vote than the average person, and Warren Buffett gets a
lot more votes than I get if we all disagree
on opinions. The reason it's not an arbitrage mechanism. And
here I'll get a little geeky. Imagine you're reasonably sure
this thing is mispriced in that Graham and DoD sense,

(27:49):
and you think it's massively mispriced, trading enough to make
it a third less mispriced. It's not very risky to
you because it's not that big a trade, and it's
it's very high expected return because it's that mispriced. Now
you've moved it back to a third or maybe half,
the next part of the trade is much riskier to

(28:10):
you because you already have the trade on, so you're.

Speaker 5 (28:12):
Just adding more.

Speaker 2 (28:13):
Oh, I see it.

Speaker 5 (28:13):
Yeah, and it.

Speaker 6 (28:14):
Has half the game because you've already closed it by half.
So arbitrage will not take things. If on net more
people believe something stupid, stupid's gonna win, and we're going
to be at least somewhat off of real prices. And
I find it remarkably easy to believe that this same
environment that makes our politics go a little crazy for
markets to be efficient in any degree, not even perfectly efficient,

(28:37):
This famous idea of the wisdom of crowds has to
be helping us a lot. The hypothesis that we're all
geniuses is never gonna fly, right. So the wisdom of
crowds and you know it well says, even if on
average most people don't know the answer, the stupid answers
cancel and the right answers don't because they're the same.
My favorite example of that, and this it might be

(28:59):
dating myself is Regis philbin and who wants to be
a millionaire?

Speaker 5 (29:03):
Right? Remember the show Multiple Choice.

Speaker 2 (29:05):
Showy, it's frightening that you're describing that as old, but
I guess it is.

Speaker 5 (29:09):
Yeah, it's been off the air, sadly Regis has passed away.
It's old.

Speaker 6 (29:12):
Sorry, sorry, but you had to answer multiple choice questions
and if you miss one, you're out. They start off
ridiculously easy, and they get harder and harder. You had
multiple cheats, like three cheats. One was a friend was
almost useless. A friend usually wasn't much smarter than you,
and b people at least to my eye, I didn't
watch every episode, but seemed to choose friends who knew

(29:34):
the same stuff they knew, Right, you really want to
choose a friend who is in like a totally different field.

Speaker 2 (29:40):
I think in some countries the friends also had a
tendency to deliberately give the wrong answer because they just
didn't want to see their friend actually win money.

Speaker 5 (29:47):
Well, the most cynical phrase ever is nothing succeeds like
a friend's failure.

Speaker 6 (29:52):
I don't believe I'm not condoning that, but it is
out there. The other one was eliminate two of the
row answers. That's great, obviously, even if you have no idea.
You go from one out of four to one out
of two.

Speaker 5 (30:04):
The other one was pulled the audience.

Speaker 6 (30:06):
And at least to my non exhaustive examination, it seemed
to work pretty much every time, even if.

Speaker 5 (30:15):
The question was hard.

Speaker 6 (30:16):
Because imagine you have one hundred people in a room.
Ten of them know the answer, the other ones are guessing.
The ninety distribute evenly over the four. Maybe not perfectly evenly,
but roughly evenly. The ten all land on B. So
you pick B because it's bigger. Work pretty much every time.
There's a crucial assumption in that the audience has to

(30:39):
be releively independent of each other.

Speaker 5 (30:41):
And they did that. They weren't talking. It was silent voting.

Speaker 6 (30:44):
If the audience all gets to talk, maybe the ten
convinced the ninety, but maybe they don't. Maybe a demagogue
with a better Twitter feed convinces everyone. And if you
ruin the independence, and I think, have we ever come
up with a better vehicle turning a wisdom of crowds
into a craziness of mobs than social media?

Speaker 5 (31:07):
I'd be hard pressed to describe it.

Speaker 3 (31:09):
I find this would be very compelling.

Speaker 4 (31:10):
James sir Wicket at the New York Times, he came
out with that book Wisdom of Crowds in two thousand
and five, but that was right before social media blew up.
And then you know, there's that famous book in eighteen
forty one, Extraordinary Popular Delusion and the Madness of the Crowds.
So we've always sort of understood that crowds can be
both mad and wise. And I find this very interesting,

(31:31):
the idea that perhaps the linkedness of the crowd is
what sort of flips it from wisdom to meta.

Speaker 6 (31:37):
I think that's exactly It maybe can come up with counterexamples,
but I think most of the time, if the crowd
is making independent decisions, then this can go for political voting,
it can go for markets. You're going to get some
degree of wisdom. When the crowd is all making a
unified decision, it could still work out, but you are
much more susceptible to what the quants technical term is.

Speaker 4 (31:58):
Craig, Craig, I want to pivot a little bit and
talk about AI. You've written or you've talked recently. I

(32:21):
forget exactly the word that was used in some of
the headlines succumbing to the rendering, our surrendering.

Speaker 3 (32:27):
To the I regret saying so forth.

Speaker 4 (32:30):
We tried to talk a lot about AI. I still
don't know exactly what it means in any context. What
does it mean to surrender to the machines in the
AQR context?

Speaker 6 (32:39):
Well, first, not every journalist has Bloomberg's high standards. I
am reasonably certain I said partially in that, and the
word partially got dropped even skipping all the details. If
you're going to use AI in your process at all,
almost by definition, you were gonna lose a little intuition,

(33:02):
and it bothered me for a couple of years. I
think I slowed us down on AI by a year
or two just by saying, you know, we've always prided
ourselves on the balance of we intuitively understand why we
think this makes money and the evidence that it makes money.
And when you go to AI, you're normally giving.

Speaker 5 (33:18):
Up some not all. We actually do try very hard
to figure out why we think this works or doesn't work.
But if you weren't giving up some intuition, what the
heck is the AI doing. If it's simple and you
could have just seen it with the naked eye, it's
hard to imagine it's helping. But let me give you
a concrete example. We like good momentum.

Speaker 6 (33:39):
We like it in price, we like it in fundamentals.
One way people on the QUANDT side have tried to
measure this for years is something like Earning's calls, trying
to decide if Earning's calls a good news are bad news,
and if people underreact to good news, you want to
buy when it's good news.

Speaker 2 (33:56):
Is this just like how many times people say great quarter, guys,
or are you looking as something else?

Speaker 6 (34:00):
It's gonna sound about as silly as that you build
up tables of words and phrases with numerical values and
then you say, what's the numerical score of this? And
they can be much more subtle than this. I'm gonna
use a real simple example the word increasing plus one, right,
and I'm sure you see the flaw. If the actual
sentence was massive embezzlement is increasing, you know, are bad

(34:25):
on that one.

Speaker 2 (34:26):
The amount of fraud we're seeing in our private credit
deals is increasing.

Speaker 6 (34:30):
Quant can survive looking stupid a lot. If that's forty
seven percent of the time, If fifty three percent of
the time it's getting it right, fine, And those things
had some efficacy, but they weren't great. What we do
today is we train mL. It's called natural language processing.
It's the sub field of mL to analyze corporate statements.

(34:53):
But what it does, and this is going to be
the geekiest thing, I'll say, it represents every corporate statement.
It looks across them and presents them as a set
of numbers, what the geeks will call a vector of numbers.

Speaker 5 (35:04):
Then what we do is empirics to.

Speaker 6 (35:06):
Say, all right, we have fifty years of this across
many firms. We have different vectors or numbers for every
earnings call. What combination go long when the first number
is good, short when the second number is high?

Speaker 5 (35:21):
Blah blah blah. What best combination forecasts?

Speaker 6 (35:24):
That seems to be correlated to what we were doing before.
These word count things just considerably better.

Speaker 5 (35:32):
It does a better job than word counts.

Speaker 6 (35:34):
That language is very nonlinear whether something is good, whether
that word increasing is good. AI is not perfect, but
it's chance of figuring out if increasing was good or bad,
especially when it's trained on tons of these Yes, better
than us. Here's where you lose the intuition. Though I
skip a step.

Speaker 5 (35:50):
I like to do that. It's fun sneaky.

Speaker 6 (35:53):
If you ask myself or even some of the younger
people who are much more tooled up on machine learning
than I am these days, what does that vector of
numbers actually mean? You often get, Ah, we really can't
tell you that.

Speaker 5 (36:07):
We can say it.

Speaker 6 (36:09):
It's summing up, in a mathematical sense, the information content
of that. We still get intuition because this indicator acts
like a short term momentum indicator, so it's picking up
what we wanted to pick up. It's also done fabulously
well for us for multiple years in real life, but
we are giving up intuition at one stage that we
used to not do. So my answer was meant to

(36:32):
be much more subtle, that there are give ups in
intuition when you move to something like machine learning. They
almost have to be or use again, what are you doing?
I was uncomfortable for that for a while, so I
was starting to do mia kulpa saying that I slowed
us down, and it came out as well.

Speaker 5 (36:47):
I used to hate this, but now I have no job.

Speaker 6 (36:50):
The machine runs it, which is probably gonna be true
in eight years, but not yet.

Speaker 2 (36:54):
This reminds me actually of something I wanted to ask,
because another big multi year decade trend is the rise
of the multi strats. And at AQR, as I understand it,
you have a multistrat model in there, but it's more
centralized than some other places. Can you go into a
little bit more detail.

Speaker 6 (37:13):
Sure, they get used interchangeably, but I think this is
the difference between multi strat and multi manager. Multistrat just
means I wrote a dissertation on choosing US stocks. We
have applied similar things to stocks around the world, to currencies,
to commodities, to directional bets through trend following. They are
correlated but low. So we think of these as different

(37:36):
strategies and we think a set of our strategies is
better than a single one. Or it's just the power
of diversification. So we're big believers in particularly if you
have a common philosophy. So it's not just fitting the data,
it's fitting into an overall theme of what you believe in.
We're big believers in multistrats. What we share with multimanager

(37:59):
is that believe and diversification. Multi manager is what it
sounds like it is. We are one team building these.
We might of course have little separate teams at AQR,
but we're one firm building these. They are farming it
out to different people. To be frank, if you had
told me their business model ten twenty years ago, I

(38:20):
would have been very cynical that it worked. If you
told me a what the total fees are gonna be
when you add up everything that's passed through and a
fair amount of them. And they vary in how quick
they do this. If something's not working for what I
would consider a very short while, yeah they stop doing it.
And I know there are a lot of load to
medium sharp ratio risk adjustive return strategies that are really

(38:41):
good long term but have bad periods. So if someone
told me that model, I would go, you're gonna charge
a ton and you're gonna throw out people who have
a bad two quarters. No, and there have been people
who've obviously proven me wrong. And I'm humble about this.
I don't fully understand why. They must be very very
very good at actual selecting the alpha. Yeah, right, And

(39:04):
this is something that we don't do. We're internal quants
who build our own models. We've never maybe be interesting
one day, we've never tried to apply that to choosing
outside people.

Speaker 5 (39:14):
But plenty of people, by the way, have.

Speaker 6 (39:15):
Tried to start multi manager and failed. So it's not
like you just apply this charge a ton, hire a
bunch of active managers and fire them if they have
a bad two hours. Just automatically works.

Speaker 2 (39:27):
It does seem though, that talent is the thing. That's
like capping multi strat expansion, right, that's the limiting factor.

Speaker 6 (39:35):
They vary, but some of the major ones are giving
back money. Even if you believe in this model, and
I even from Afar, I have to be a believer.
I've seen the results are too good for too long,
in my view, to have a decent chance of randomness.
I don't exactly know what they're doing. I'm still waiting
for Ken and Stevie and Isy to send me their

(39:57):
exact process. That would be wonderful is he doesn't share
it with you. No, even better if Medallion would send
me their exact process. But I respect it. But the
amount of individual alpha from teams that can be out
there has to all alpha's finite, but has to have
a decently tight cap. And the ones I've seen are

(40:18):
fairly disciplined about this, And again it varies.

Speaker 5 (40:20):
I can't speak for everyone. I know.

Speaker 6 (40:22):
We compete for talent with them much more than we
used to. They have non quant parts of what they do,
but they have quant parts of what they do. And
for young quants, they're often considering an offer for ake
you are and Citadel, to pick just one example. We
win our fair share, we lose our fair share. I
think we probably pay a little less in the short term,

(40:44):
but we don't.

Speaker 5 (40:44):
Fire you if you have a bad week. So some
people are and I'm he's not doing that. I'm just
saying no. But we know that it's a more cutthroat environment.

Speaker 4 (40:53):
You've done a lot of episodes on that environment. We
know like they cut pretty quickly if you lose money.
I want to go back a little bit to the
connection between sort of AI machine learning and interpretability of
the factor, you know, the quintessential. You know, the risk is,
of course, that you find something that works strictly from
data mining.

Speaker 5 (41:13):
Right.

Speaker 3 (41:13):
Tickers that start with B tend to rise on Tuesdays, and.

Speaker 5 (41:18):
He's the CEO's middle initial.

Speaker 4 (41:19):
Yeah, stuff like that we don't like. But you know,
even when you were talking about momentum and you said, well,
there's two reasons theories for why momentum can work. Let's
just go back to the canonical like quant factors. Is
there complete consensus about why these factors work?

Speaker 5 (41:36):
Not at all.

Speaker 3 (41:36):
Okay, they you start out with the because intuitively like
cheap stars go.

Speaker 4 (41:40):
But my impression is that there's even still disagreement about why.

Speaker 6 (41:43):
Yeah, you start out with the Great Divide and when
when they split the Nobel Prize between Gene Fama and
Robert Schiller, My co founder and I wrote a piece
and the cover of Institutional Investor with that title, The
Great Divide. Lars Hanson also want to share of it,
I shouldn't leave him out. But Schiller and Fama were juxtaposed.
Is the efficient market guy and the inefficient market guy,
which is fairly close to only either of them as zealots,

(42:06):
but it's fairly.

Speaker 5 (42:07):
Close to to true.

Speaker 6 (42:08):
So you start out if something has worked historically and
made a lot of money over a long period.

Speaker 5 (42:16):
You start out with what you led with? Is it
data mining? Let's say you convince yourself it's not. OK.

Speaker 6 (42:21):
Let's just put that away, and that's really important that
I'm not, you know, poo pooing that step.

Speaker 5 (42:26):
You gotta do that. Then you got to ask yourself why.

Speaker 6 (42:29):
The two main contenders are a rational gene Foma kind
of market where some stocks are riskier than others, and
whatever you're using to say go long these and short
these is loading on that risk. And if something is risky,
you should make more for investing in it. One of

(42:50):
the problems is identifying a There are sub fights within
these fights. What do we mean by risk? Is risk
beta like.

Speaker 4 (42:58):
The capital asset prices nan academics are like every other
academic because fight.

Speaker 6 (43:04):
Talmudic at this point, But is risk beta like the
capital acid pricing model? Well, the capital acid pricing model
is a beautiful, elegant model that has failed everywhere it's
ever been attempted. Is risk more multi dimensional? Is risk
something like what happens in a great depression, which is
very hard to measure. The other argument is markets are
not perfect. People make errors, and its behavioral finance. So

(43:27):
if a cheap stock beats an expensive stock because it's
inherently adding some risk that you can't diversify away, that's
a Gene Fom explanation. If it beats an expensive stock
because people went too far, they went past the Graham
and Dodd point, and if you can hold it, you
make money when reality sets in.

Speaker 5 (43:45):
That's more than Bob Schiller point. I love Gene, He's
my hero.

Speaker 6 (43:49):
I have probably drifted from seventy five to twenty five
Gene to seventy five to twenty five Bob over my career.
World's complicated. One of the hard parts is everyone wants
to win. But both explanations can be true, and they
can be true at different times. Life isn't so simple.
But those are the two biggies. But once you get
into behavioral finance, now you can fight about why what

(44:09):
behavioral bias? Notice you could sum up the two reasons
I gave you for momentum as underreaction and overreaction. Overreaction
is chasing right. Underreaction is the information came out, didn't
move far enough, and I hopped on that bandwagon. You
know you're in a little bit of a dodgy area
when your two best explanations sound a little like antonyms.

Speaker 5 (44:33):
Now I'm being intentionally funny.

Speaker 4 (44:36):
We know it works, we're just not sure whether it's
literally opposite things or intuitively opposite things.

Speaker 3 (44:41):
If we know it's one of them, and.

Speaker 6 (44:42):
There's some being a little too negative, you can actually
they're good papers. Teasing out the underreaction is easier to show.
You can actually show the fundamentals do catch up. I
think the overreaction probably kicks in more in bubbles where
there is just more feedback trading. Again, both explanations can
be true, and they can vary in their intensity over time.

(45:03):
The overreaction might have a very small part of it,
except plus a minus two years around a major bubble,
when it may be the driving force. So we have
to get really comfortable. We don't have to have the
be all, end all, single explanation. But if there are
a few good explanations and no real counter ones, and
we have really strong fifty year empirical results. Yeah, we're

(45:24):
okay with taking some.

Speaker 5 (45:26):
Bet on this. You never want to put all your
money on one thing that's not a quant thing.

Speaker 2 (45:30):
I'm going to try to connect a bunch of the
sort of decade long mega trends that we've been discussing.
But I'm thinking behavioral finance, gamification markets, big data, AI,
all those fun things combining into sports betting, which seems
to be getting bigger and bigger than ever. And the
reason I ask is because I was talking to someone

(45:51):
in a bar the other day and they said that
AQR was doing more sports betting stuff.

Speaker 5 (45:55):
Someone in a bar was talking about AQR doing.

Speaker 2 (45:57):
Sports Yeah, no, well, it probably says more about the
bars that I'm in and the people I'm talking to
than anything else.

Speaker 6 (46:03):
But yeah, they mentioned I'm picturing the Star Wars canteena
right now.

Speaker 5 (46:07):
I gotta tell you.

Speaker 2 (46:08):
So, I'm just curious, is that a thing or are
you maybe interested in the sort of prediction market aspect
of things nowadays? Susquehanna is getting into it.

Speaker 6 (46:17):
I think no one again tells you exactly what they're
they're doing my senses. They are into it and are
good at it. This is incipient for us. We are
considering and looking at it. We have a long history
Toby Moskowitz, who's a Yale professor and an AQR partner.

Speaker 5 (46:37):
Great deal, by the way, you get paid for two
full time jobs that are eighty percent overlap.

Speaker 6 (46:40):
I keep that in mind for the Toby's listening. Just
you know, you're a lucky man. He wrote a book
called score Casting that was all about sports. But the
sports analytics, the moneyball type stuff looks a lot like
what we do. It looks a lot like people are
not pricing this right people. I wrote my most downloaded

(47:02):
paper ever, which is really a little annoying because it's
like the only one I've written outside of my field,
was on when to pull the goaltender in a hockey game? Yeah,
and I wrote it with a colleague and friend, Aaron Brown.
We built a model and we came up with you
should be pulling the goalie if you're losing by one
with five or six minutes left, not with two minutes left.
And I'm pretty sure we're right. We spent a lot

(47:25):
of time in that paper saying why aren't they doing
it and making analogies to investing, you know, when people
know the right thing to do but can't do it
because the public opprobrium if they get it wrong will
be much worse.

Speaker 4 (47:36):
Like aren't there saying like football teams should go forward
on fourth down a lot more than they do And
that's like one.

Speaker 6 (47:40):
Of these things that happened and they have started to Yeah,
there is a feedback where some of these things are slow,
some of them are fast. Baseball has largely absorbed a
lot of these things. You know, on base percentage was
one of the major insights of moneyball, and no nobody's
missing that one anymore. But we still think, particularly in
the betting markets, are not probably not as rational as

(48:01):
Toby's scorecasting book. So we do think there might be
opportunities there. But I don't want to overstate it just
because I am cynical about online sports betting.

Speaker 5 (48:11):
I gotta tell you two things.

Speaker 6 (48:13):
I was always a very libertarian guy. I still think
I am, but a couple things have made me less libertarian.
Children The existence of my children will make you go, yeah,
maybe people shouldn't be able to do anything they.

Speaker 5 (48:26):
Want when they turn twenty with rules and sports.

Speaker 6 (48:29):
Betting and maybe walking down the New York City streets
and getting a contact high sports betting. We're already, you know,
some of these scandals were starting to see how do
you not have them? When you know some of these
people make a ton of money, but they don't all
make a ton of money, and there's a lot of
money in sports betting. Anything you do for entertainment is
fine if you do it entertainment size. If you go

(48:49):
to Las Vegas and you go the odds are on
the house's side. I'm gonna lose five hundred dollars over
two days probably If I win, great, but it's gonna
be fun.

Speaker 2 (48:58):
It's factored into the cost of a Vegas.

Speaker 6 (49:01):
But like, I have a whole bunch of twenty something
year old mostly guys in my extended family. A lot
of them are sports betters, and they're not even It
even pisses me off that they're not even rooting for
their teams anymore.

Speaker 5 (49:14):
They're kind of rooting for this player to score on
their prop.

Speaker 2 (49:18):
It is a strange dynamic.

Speaker 6 (49:19):
Y So do I think that gamification is highly related.
I think if you go look at the robin Hood
app and fan duel. I think you will find they
are far more similar on their feedback and how they
treat things and in how their investors do on average,
particularly sports betting. You know, investors lose on average because

(49:40):
the sports betting companies make money. It's like staying on average.
Insurance is a bad deal, you know how. I know
because insurance companies are profitable doesn't mean it's stupid to
do because maybe if it's in a risk you can't tolerate,
that could be a fair trade.

Speaker 2 (49:54):
If there's a price you pay for a peace of mind,
which is something I've come to appreciate over the course
of ten years.

Speaker 6 (49:58):
There is zero chance that the average online sports better
is making money.

Speaker 4 (50:03):
Yeah, I just have one last question, you know. I
think if you looked at, like some particularly crazy times
in markets, here's something that's changed very much since we
started the podcast first ten years ago. I think if
you look at some crazy times in markets, whether it's
you know, twenty nineteen when some of these measures were
getting extreme, we're just you know, the spack mania of
twenty twenty one. One theory that one might have offered

(50:27):
is zerp and he's like, oh, this is now we have.
You know, we haven't been at zert for a long time,
and yet some of this sort of speculative mania crazes
has not gone away. How surprised are you that the
move from zero percent to say five percent or whatever
didn't have more of a sapping effect on some of
the behavior and speculative fraud or just sort of animal

(50:50):
spirits in these markets.

Speaker 5 (50:51):
I am mildly surprised.

Speaker 6 (50:53):
Okay, I left this out, but I actually had three
possible reasons in my paper, and the third one was
super low interest rates for a long time for why
things can get crazy now. The counter argument is, once
you break people's brains, they don't necessarily repair themselves instantly,
So I think it was probably a contributory factor. Again,
very hard to prove. A lot of people in nineteen

(51:16):
and twenty when the spread's being cheap and expensive. We're here,
We're saying it was a super low interest rate environment,
and gross stocks have more cast flows in the future.
Low interest rates means they're worth more.

Speaker 5 (51:26):
We did the math on that.

Speaker 6 (51:27):
It explained like two percent of the insuring value spread,
and in ninety nine two thousand interest rates were quite hot,
so it's not a unified field theory explanation. Do I
think it helped kickstart them. And again we're in the
soft guess work, but I think these are educated guesses
and I listed it as one of my three. I

(51:48):
think it's certainly kicked us off on some of these things,
certainly loosen the bounds of rationality.

Speaker 5 (51:53):
Absolutely, free money, we'll do that.

Speaker 6 (51:55):
I don't think it's not human nature that they take
away zup and everything comes back. Would I have thought
it was a bigger effect in going back to you know,
at one point we hit about five percent on the
ten year almost five percent.

Speaker 5 (52:07):
Would I have thought that would have mattered more.

Speaker 6 (52:09):
Yeah, only in twenty twenty two did we see one
ugly year over that. But it has mattered less than
I thought. It doesn't mean it will never matter.

Speaker 2 (52:19):
All right, Cliff Astnes, thank you so much for coming
off all the lots kicking off our ten year anniversary
celebration again.

Speaker 3 (52:26):
Yeah, thank you so much.

Speaker 2 (52:28):
That was really a pleasure.

Speaker 3 (52:29):
Thank you, Joe.

Speaker 2 (52:43):
That was so much fun.

Speaker 3 (52:44):
It was great.

Speaker 2 (52:45):
Literally the perfect guest, literally the perfect guest.

Speaker 5 (52:47):
One.

Speaker 2 (52:47):
Well, there's so many things that stuck out from that conversation,
but one of the things that stuck out from the conversation.
It's this idea about the thing that flips the wisdom
of crowds into the madness of crowds, right, And the
idea that maybe wisdom of crowd's theory works as long
as everyone is sort of isolated and independent and making
their own choice off of the information available to them.

(53:09):
But it starts to fall apart when everyone is tied
to everyone else and sort of in the same social network.
And you know, if you think about one of the
dominant themes in markets in recent years, it has been
people hurting into the same positions. Right.

Speaker 4 (53:22):
I found that to be really fascinating. I think it
makes it a lot of intuitive sense. It probably can
explain a lot of things about the world of politics,
about the world of markets, et cetera.

Speaker 3 (53:33):
This idea that we're all just sort.

Speaker 4 (53:34):
Of one connected global village, as Marshall mccluan put.

Speaker 2 (53:39):
It, we're all just gossiping with each other all.

Speaker 3 (53:41):
The time, just constant talk, talk talking.

Speaker 4 (53:43):
No, that's a very interesting idea, and also those interesting
the idea of length of draw down versus depth of
draw down and the former being more painful, which strikes
me is something I hadn't really heard anyone talk about
that before, but especially from the perspective of a manager
of other people's money, it's a very highly intuitive that

(54:04):
makes a lot of sense to me that Okay, like, yeah,
you had a bad quarter or whatever. Eventually you're like, oh,
your ideas are just out of date. It's been three
years since you've made money. Maybe time to rethink some
of your fundamental assumptions people.

Speaker 2 (54:17):
That's one thing I've learned over the course of ten years.

Speaker 3 (54:20):
Yeah.

Speaker 2 (54:20):
The other thing was the idea of markets not necessarily
being an arbitrage mechanism, which I think is very counterintuitive
to the way a lot of people will think about markets,
and this idea that like, well, sometimes you can't compress
the price all the way to where it should be
rationally or logically or according to EMH or whatever, because
the reward just isn't necessarily there to get to that

(54:42):
like final ten percent.

Speaker 4 (54:44):
It's interesting to think about the sort of link between
patterns and interpretability why something works.

Speaker 3 (54:52):
And in our.

Speaker 4 (54:52):
Conversation a couple of weeks ago with Ian Dunning of
Hudson River Trading, is like, they do not put a
lot of emphasis on interpretability there's a pattern and some
reasons to establish that the pattern works, it makes money.
The idea that they then have to also come up
with an economic story about why it works is not
so important to them. Maybe that's because it has to
do with time frames. Obviously, cliffs trading time frame is

(55:16):
going to be very different than a high frequency trading
firm like HRT.

Speaker 3 (55:20):
But it is interesting. And then it's interesting to think
that even in the.

Speaker 4 (55:24):
Most established quant patterns like why do cheap stocks outperform
more expensive stocks over the long term?

Speaker 3 (55:30):
Right, even there.

Speaker 4 (55:31):
There's dispute about why this pattern holds, even though it
feels a little bit more intuitive. So much interesting stuff here.

Speaker 2 (55:37):
It's also I guess it sort of warms my old
cynical heart that maybe maybe the edge for humans will
be spotting the regime change, right, which, you know, at
least there's something left for us to do if it's
not just pure pattern recognition. There's that one very difficult
to good luck to us. Yeah, all right, shall we
leave it there.

Speaker 3 (55:57):
Let's leave it there.

Speaker 2 (55:58):
This has been another episode of the All Thoughts podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.

Speaker 4 (56:03):
And I'm Jill Wisenthal. You can follow me at the Stalwart,
follow our guest Cliff Asnest He's at Clifford Asni. Follow
our producers Carmen Rodriguez at Carmen armand dash O Bennett
at Dashbod and Kelbrooks at Kelbrooks. From our odd Logs content,
go to Bloomberg dot com slash odd Lots with the
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(56:23):
in our discord discord dot gg slash od Lots And.

Speaker 2 (56:27):
If you enjoy odd Lots, if you like it when
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