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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.

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Speaker 2: Hello and welcome to another episode of the All Thoughts Podcast.

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Speaker 2: I'm Tracy Allaway.

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Speaker 3: And I'm Joe wysnent Thal.

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Speaker 2: Joe, it's a big month for us.

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

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Speaker 2: I know, I can't believe it. Do you remember the

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Speaker 2: first episode?

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Speaker 3: Yeah, of course with Tom Keane.

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Speaker 2: Yeah, But and then our second episode I think was

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Speaker 2: about bananas for some reason, it was You're.

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Speaker 4: Right, it took us a long time to figure out

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Speaker 4: what we were doing. It took us a long time

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Speaker 4: to figure out what we were doing, and I don't

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Speaker 4: think at that point I would have expected that we'd be.

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Speaker 3: Doing it ten years later. I don't know what I

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Speaker 3: was expecting. I think turning on a microphone in a

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Speaker 3: radio studio and talking for a while.

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Speaker 2: We started doing it because we wanted to have a

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Speaker 2: podcast and talk to interesting people. I think we were

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Speaker 2: hashtag bless. No one was listening for a very very

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Speaker 2: long time, which gave us a long runway to figure

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Speaker 2: things out. So we got lucky. That said, you know,

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Speaker 2: ten years, it is, in fact a long time to

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Speaker 2: be doing this, and a lot has changed in that period.

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Speaker 4: A lot has changed in that period, sometimes mind blowing.

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Speaker 4: And we've talked about this before for sure, But the

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Speaker 4: things that we were covering is capital and news at

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Speaker 4: the time are now capital age history.

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Speaker 3: And it's like these things that are we sort of

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Speaker 3: take for granted. Everyone was there.

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Speaker 4: It's like, no, children, let us tell you what it

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Speaker 4: was like in the old days when people were worried

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Speaker 4: the world was going to come to an end because

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Speaker 4: you know, Greece is sovereign debt and all this stuff

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Speaker 4: that we just sort of part of the landscape is

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Speaker 4: people don't remember it.

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Speaker 2: No, one of those things has to be the idea

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Speaker 2: of value or fundamental investing, right, Like, let us tell

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Speaker 2: you about the days when price actually mattered and had

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Speaker 2: a limit to what investors would pile into.

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Speaker 3: Yeah, that's exactly right.

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Speaker 4: Let us tell you about the days when people used

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Speaker 4: to talk about pe ratios and this stock, Oh so

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Speaker 4: the twenty five pe we better sell it and buy

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Speaker 4: the stock at a fifteen p or whatever.

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Speaker 3: Yes, that feels quaint.

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Speaker 4: Maybe it'll be back there one day, but for now,

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Speaker 4: given how many things in the market seem to be

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Speaker 4: the Graham and DoD kind of stuff, feels a little.

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Speaker 2: Old, a little old fashioned, little old.

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Speaker 5: Yeah.

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Speaker 2: Okay, So one of the big themes that has emerged

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Speaker 2: in the ten years that we've been doing this podcast

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Speaker 2: is everyone seems to have grown more stupid. I would say,

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Speaker 2: hopefully that's not related, so that's not like a correlation thing.

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Speaker 2: It could be all right, but you know, we have

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Speaker 2: all this gamification of investing, people betting onlines going up,

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Speaker 2: we're down, people betting on random meme coins, things like that,

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Speaker 2: and I think, you know, we talk about it a

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Speaker 2: lot on the podcast, but this is actually a fundamental

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Speaker 2: shift in the market. If you think about the market

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Speaker 2: as something that's supposed to be about capital allocation, alignment

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Speaker 2: of incentives. People are investing in something because they think

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Speaker 2: it's going to be profitable in the future at the

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Speaker 2: right price. And now people are just sort of piling

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Speaker 2: into stuff because other people are doing it, and again,

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Speaker 2: line go up.

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Speaker 4: Deep down, I still believe that the value of a

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Speaker 4: stock should reflect the net present value of all. I

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Speaker 4: know you're an MH guy, but I've it's been a

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Speaker 4: little bit hard with some of these things. And you

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Speaker 4: know the other thing too, that is sort of changes

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Speaker 4: that like half of our episodes these days are kind

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Speaker 4: of AI related in some way, and so I think

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Speaker 4: there's a lot of interesting stuff going on, particularly at

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Speaker 4: the intersection of tech and applying tech to both investing

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Speaker 4: in tech, but then the application of tech to investing

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Speaker 4: and so forth.

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Speaker 3: So yes, much has changed.

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Speaker 2: A lot has changed, and we have the perfect guests

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Speaker 2: to talk about how everyone has grown more stupid over time.

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Speaker 2: We're going to be speaking with someone we've wanted on

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Speaker 2: the show for a really, really long time. I'm very

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Speaker 2: excited about this. It's Cliff Asnas, the co founder and

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Speaker 2: CIO of AQR. Thank you so much for coming on.

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Speaker 5: All thoughts, Thank you for having me.

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Speaker 2: What's it like to be a rational person living in

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Speaker 2: irrational Cliff, Yeah, you've been doing it for a long time.

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Speaker 6: Well, a rational person is not always how I'm described,

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Speaker 6: but this rational investing and in this rational conduct in

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Speaker 6: your personal life.

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Speaker 5: So let's just distinguish those.

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Speaker 6: You guys in your intro said like, it's a it's

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Speaker 6: a little scary for the next hour because you said

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Speaker 6: like a third of the things I want to say. Oh,

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Speaker 6: I wrote a rather gigantic piece in the Journal of

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Speaker 6: Portfolio Management. They were having a fiftieth anniversary. None of

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Speaker 6: us were quite old enough to have been there in

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Speaker 6: the first issue, but they were looking for the old

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Speaker 6: guys to write kind of retrospectives, and the piece I

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Speaker 6: wrote was called the less Efficient Market Hypothesis. Now, I

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Speaker 6: think you guys probably know this, but my dissertation advisor

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Speaker 6: was a little known guy named Eugene Fama.

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Speaker 3: We've heard of.

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Speaker 5: I was his TA for two years. I grew up

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Speaker 5: in EMH.

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Speaker 6: I love that you guys just say EMH and your

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Speaker 6: audience knows where you're talking about. That is not the

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Speaker 6: norm for me when I talk about these things. I

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Speaker 6: was not a perfect efficient marketer even back then.

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Speaker 5: Neither is Gene.

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Speaker 6: By the way, Genes not a zealot about third week

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Speaker 6: of class. I know this because I took the class

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Speaker 6: three times. I didn't fail, but as the TA, I

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Speaker 6: sat through it three full years, and like the third week,

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Speaker 6: he always tells the class.

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Speaker 5: Markets are almost certainly not perfectly efficient.

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Speaker 6: Because Jean's a brilliant guy and recognizes that perfection is

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Speaker 6: a really stupid idea.

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Speaker 2: He's been on the show. By the way.

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Speaker 6: Oh, I don't know, yeah, yeah, And I don't know

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Speaker 6: if that came up, but he's always very honest about that.

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Speaker 6: He probably thinks they're considerably more efficient than I do

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Speaker 6: these days, and I think I probably think it's they're

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Speaker 6: more efficient than maybe.

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Speaker 5: The active average retail trader.

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Speaker 6: But I wrote a dissertation for him on the success

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Speaker 6: of price momentum. That is not a very gene Fama dissertation.

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Speaker 6: A gene Fama dissertation is I've studied price momentum and

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Speaker 6: it loses gobs of money, and these idiots on Wall

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Speaker 6: Street do it anyway. That's kind of a fishing market.

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Speaker 6: It's lot how silly they are. And he was great

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Speaker 6: about it. I remember I kind of mumbled, I'm like,

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Speaker 6: I want to write a dissertation on price momentum, and

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Speaker 6: by the way, I find it works very well.

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Speaker 5: What was that, cliff?

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Speaker 6: It works very well? And he said, if it's in

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Speaker 6: the data, write the paper. So I've drifted at least

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Speaker 6: a little bit further from efficient markets even way back then. Now,

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Speaker 6: price momentum, I'll do a lot of segues. You're gonna

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Speaker 6: have to stop. I do parentheticals within parentheticals. Price momentums

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Speaker 6: often thought of as this index of irrationality. It can

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Speaker 6: work for two different reasons. It can work because of yes,

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Speaker 6: feedback loops, chasing line goes up just like you said before,

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Speaker 6: and people pour in, which isn't very connected to reality.

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Speaker 5: It's just chasing returns.

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Speaker 6: But it can also work because of what the behavioral

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Speaker 6: finance people would call underreaction. News comes out that should

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Speaker 6: move the price by so and so on average. We

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Speaker 6: have found, I say we it's the royal we of

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Speaker 6: academia and private researchers that the price moves the right direction,

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Speaker 6: but doesn't move all the way. So if you trade

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Speaker 6: on that, there's still a little bit to go. That's

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Speaker 6: a quant thing. You wouldn't want to bet on one

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Speaker 6: stock that way. But if on average that happens and

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Speaker 6: you could do that through observing the price or the fundamentals,

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Speaker 6: usually a little bit more to go. So it's not

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Speaker 6: always irrational momentum. But here's what I observed in the

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Speaker 6: very beginning of AQR. EQR launched in nineteen ninety eight,

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Speaker 6: after we had a fabulous run at Goldman sacks this

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Speaker 6: survivorship bias. In this you don't get to start your

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Speaker 6: own billion dollar hedge fun unless you have a fabulous

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Speaker 6: runner at Goldman sacks or something equivalent. We had a

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Speaker 6: good first month. You know how the story is going

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Speaker 6: to go when you say you had a good first month, right,

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Speaker 6: And then that first month, by the way, it was

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Speaker 6: August of nineteen ninety eight when the S and P

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Speaker 6: was down twenty percent on the Russian debt crisis, and

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Speaker 6: we're doing high fives, like we say, we're marketing neutral

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Speaker 6: and we're up a little bit in a crash. And

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Speaker 6: it was my first of many lessons never to high

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Speaker 6: five in this business. When you fully retire and divest,

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Speaker 6: you get one high five.

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Speaker 2: But that the well, no, it's the end when you

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Speaker 2: do the high five.

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Speaker 6: I'm a kwant who is also superstitious, which if that's

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Speaker 6: a contradiction, what the heck. But the next eighteen months

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Speaker 6: was the crescendo the famous dot com or tech bubble,

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Speaker 6: and that was not kind to us. It was particularly

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Speaker 6: not kind because we decided to start with a extremely

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Speaker 6: aggressive market neutral fund. So momentum helped, as you can

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Speaker 6: imagine in a bubble, but value was just destroyed and

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Speaker 6: that was most of the model back then. Things have

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Speaker 6: really broadened out. We hit spreads between cheap and expensive.

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Speaker 6: This is something we invented at the time, and now.

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Speaker 5: A lot of people do.

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Speaker 6: People said shortstocks on valuations go along in the cheap,

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Speaker 6: short the expensive, But the very obvious question of how

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Speaker 6: cheap and how expensive are they sometimes pretty tightly clustered?

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Speaker 6: Are they sometimes wider? Does that mean they're better or

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Speaker 6: worse going forward? Was not asked before. So we invented

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Speaker 6: this measure, and the spread between cheap and expensive for

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Speaker 6: fifty years had looked like a well behaved series.

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Speaker 5: It moved around a fair amount.

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Speaker 6: And then I'm drawing on my hand if only see

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Speaker 6: the video, Yeah.

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Speaker 2: I should mention this is an audio medium.

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Speaker 6: And then in late ninety nine two thousand and went

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Speaker 6: to just way wider than anything ever seen for fifty

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Speaker 6: plus years. We also showed that historically those were better times.

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Speaker 6: You never saw that before. But when it was wider

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Speaker 6: or better times for value, we stuck with it. We

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Speaker 6: made money round trip life was good.

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Speaker 5: If you had.

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Speaker 6: Asked me after the round trip, which was harrowing. You know,

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Speaker 6: you know, even if we love our process, you never

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Speaker 6: want to start a business with poor returns. If you

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Speaker 6: ask me at the end of that, which is probably

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Speaker 6: a couple of years later, two thousand and three. Do

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Speaker 6: you think you're ever going to see that in your

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Speaker 6: career again? No one asked, thank God, because I think

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Speaker 6: it would have gotten it wrong. But I think I

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Speaker 6: would have said, oh, probably not hopefully. I wouldn't say definitely.

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Speaker 6: No one who does what any of us do for

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Speaker 6: a living should say definitely. That's a bad word in markets.

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Speaker 2: Is the favorite term.

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Speaker 5: But A it was the.

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Speaker 6: Craziest thing numerically in fifty plus years. B. The question

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Speaker 6: presupposed is it's built in that I and people of

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Speaker 6: my core will still be around right and will probably

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Speaker 6: be are to in charge. So how's it going to

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Speaker 6: happen again? And then it happened again even before COVID.

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Speaker 6: By late twenty nineteen, that spread between cheap and expensive

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Speaker 6: was approaching dot com extremes, and then it blew past it.

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Speaker 5: In COVID.

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Speaker 6: It went to what I in a geeky math joke

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Speaker 6: that no one ever gets called one hundred and twenty

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Speaker 6: fifth percentile. There is no one hundred twenty five percentile.

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Speaker 6: It's just a new hundred percentile. I'm trying to convey

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Speaker 6: that it went further, and we survived that one too.

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Speaker 6: We suffered somewhat, and then we made more than all

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Speaker 6: of it back ground trip good. Most of the time,

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Speaker 6: we really don't look like value investors, by the way,

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Speaker 6: only in extreme bubbles do we have seem to have

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Speaker 6: that property.

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Speaker 5: And I think it's smaller now than it used to be.

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Speaker 5: Last five years have been quite strong for us, and

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Speaker 5: it's not been a very good value market.

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Speaker 4: God, there's so many questions that I have that I

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Speaker 4: want to ask that are sort of embedded or related

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Speaker 4: to your answer. I'm going to ask at you just

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Speaker 4: like a very narrow question, sort of skip ahead into something.

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Speaker 5: Though.

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Speaker 4: You make this spread between the most expensive than the

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Speaker 4: cheapest stocks, how much easier it is to simply compile

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Speaker 4: that spread? Is it today versus the technology that you're

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Speaker 4: working with when you're starting your career?

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Speaker 6: For that, I got to disappoint you and say not

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Speaker 6: that much. Here we had the databases. A lot of

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Speaker 6: technological investment is about speed and new data sets. What

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Speaker 6: quanto Alternative data is something we're very into. But the

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Speaker 6: classic data if you're looking for price to sales ratios,

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Speaker 6: I'm old, but we had we have telephones and Bloombergs.

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Speaker 6: But that does bring us to me observing these two episodes,

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Speaker 6: and I stepped back and I asked a question, what

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Speaker 6: the hell happened? Why did we see something crazier than

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Speaker 6: fifty years? And then why did it happen again? And

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Speaker 6: that led to this paper, the less efficient market Hypothesis.

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Speaker 6: I do believe that markets have shown and I think

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Speaker 6: I have some good guesses as to why that they

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Speaker 6: are more susceptible to bouts of crazy than they used

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Speaker 6: to be.

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Speaker 4: Just before going further, one quick definitional question efficient markets,

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Speaker 4: because one way you could define whether market is efficient

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Speaker 4: is are these securities disconnected from what you would say

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Speaker 4: the net present value of the all future free cash flows.

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Speaker 4: Another way that I often think about it, but I'm

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Speaker 4: no kuant is are there obvious opportunities for the active

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Speaker 4: manager to make money?

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Speaker 3: Just for our purposes here? How do you define market efficients?

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Speaker 6: Much closer to the first one, Okay, The practical question

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Speaker 6: of can you make money from these is if there

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Speaker 6: are big deviations from fair value? And this relates to

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Speaker 6: some stuff I did in a less efficient market hypothesis.

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Speaker 6: If there are big deviations the classic and you'll notice

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Speaker 6: a giant caveat if you can stick with your position

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Speaker 6: not a small thing.

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Speaker 5: You will make money.

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Speaker 6: There are opportunities, and in fact, I think a less

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Speaker 6: efficient market in that sense, in your present value sense,

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Speaker 6: almost has to deliver bigger opportunities for people who can

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Speaker 6: stick with it. It also makes it considerably harder to

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Speaker 6: stick with because the extremes you have to live through

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Speaker 6: and the length of time those extremes can go on.

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Speaker 6: For one thing, that I'm dying for an academic to

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Speaker 6: take me up on this. I'm too old to do

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Speaker 6: the math on this, but I've never seen a model

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Speaker 6: that looks at pain, disutility, the negative of losing money

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Speaker 6: in terms of how long you've lost money for, not

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Speaker 6: just magnitude. Right, And in real life, I can tell

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Speaker 6: you a drawdown that is one and a half times bigger,

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Speaker 6: but with six months instead of three years, is ridiculously

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Speaker 6: easier to live through. So the bigger disconnect from reality. Again,

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Speaker 6: I haven't even told you why I think it's going on,

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Speaker 6: But if I'm right that we have these bouts of it,

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Speaker 6: it's not necessarily every day things are crazy, but these

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Speaker 6: bouts of it is a two edged sword. It's a

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Speaker 6: bigger opportunity for people who can stick with it, and

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Speaker 6: it's harder to do, and I find that not that

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Speaker 6: what I think is fair is particularly relevant. But I

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Speaker 6: find that really fair harder to do, but more lucrative

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Speaker 6: if you can do it. I don't think the efficient

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Speaker 6: market idea of buying what is fundamentally mismatched against its

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Speaker 6: future cash flows adjusted for as risk forecasting those cash

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Speaker 6: flows what risk really means? We still have some open

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Speaker 6: issues there. Yeah, but I don't think that will ever

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Speaker 6: go away unless markets are perfectly efficient. But how easy

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Speaker 6: it is to identify and how easy it is to

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Speaker 6: stick with. I think crazy markets make it easier to

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Speaker 6: identify what to do and harder.

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Speaker 5: To deal like that.

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Speaker 2: I do want to ask you why you think this

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Speaker 2: is happening, but before we do, this is a very

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Speaker 2: basic question. But sometimes the basic questions are the most interesting.

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Speaker 2: But when you say it's painful to try to stay

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Speaker 2: rational during these bouts of irrationality, why is that exactly?

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Speaker 2: Because I get that there are funding costs. I get

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Speaker 2: that there are carrying costs. But on the other hand,

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Speaker 2: you're a big hedge fund with deep pockets. This is

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Speaker 2: presumably what investors are paying you to do. Is it

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Speaker 2: just the emotional trauma or stress of having to explain

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Speaker 2: to everyone why you're making the position that you have

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Speaker 2: when it's not paying off yet.

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Speaker 5: That is a big part of it.

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Speaker 6: I have a running fight with one of my co

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Speaker 6: founders who never gets upset. I'm always upset, but I'm

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Speaker 6: more upset when we're losing money.

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Speaker 2: This is sort of our dynamic. I have to I

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Speaker 2: get more upset than Joj.

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Speaker 6: Hunhes where He'll come in my office during a bad

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Speaker 6: period and I'll be upset.

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Speaker 5: It'll be a bad day in a bad period. He's like,

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Speaker 5: why are you upset?

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Speaker 6: And I'm like, well, cause people are yelling at us

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Speaker 6: and I were losing money. And he's like, but you're

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Speaker 6: pretty sure.

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

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Speaker 6: He's like, and you have all your own money and

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Speaker 6: your kids money in this right, so you're not doing

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Speaker 6: anything different.

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Speaker 5: You wouldn't do that if you weren't. I'm like yeah,

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Speaker 5: He's like, so we're gonna win. It's just a question

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Speaker 5: of when why do you care? And I look at

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Speaker 5: him like he's from Mars and go, why do you

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Speaker 5: not care?

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Speaker 6: And we finally figured out it's kind of obvious that

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Speaker 6: I talk to clients a lot more than he does.

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Speaker 6: It's a lot easier to have that attitude when you're

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Speaker 6: just sitting in your office. Also, we are not immune

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Speaker 6: from this. Even if people love you and think you've

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Speaker 6: done well for twenty five years, you have a bad

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Speaker 6: two years, you get redemptions, and sometimes they're of serious size.

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Speaker 6: We fell by like half over about three years, and

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Speaker 6: that's not fun. You have to shrink your firm, you

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Speaker 6: have to let some people you love go, so there

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Speaker 6: is some real pain that goes with it. That period

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Speaker 6: did not shake my confidence in the actual investment process.

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Speaker 6: I feel bad. I think I did better than our

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Speaker 6: investors because I kept adding and saying, take the ball

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Speaker 6: up on what I do. Not everyone can do that,

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Speaker 6: and I know more than they know. Not in a

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Speaker 6: weird insider sense and just a I should be more

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Speaker 6: confident in my own process than anyone else's. But it

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Speaker 6: is excruciating the amount. I remember someone I admire tremendously,

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Speaker 6: when Stan Druckenmeller retired to run his own money. He's

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Speaker 6: still very active in markets. I think he wrote a

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Speaker 6: note that resonated with me because I forget the exact details,

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Speaker 6: but the essence was, it's too painful and too upsetting

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Speaker 6: to run client money. The man never had a down year,

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Speaker 6: and I'm like, if Stan can't take it, those of

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Speaker 6: us and we've had a lot more up years than

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Speaker 6: down years and life's been good, or I won't be

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Speaker 6: sitting here. But we've had never three but two plus

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Speaker 6: years of pain, and I'm like, Stan can't take it. Man,

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Speaker 6: this is harder to do than it looks like.

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Speaker 5: The whole world.

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Speaker 6: Thinks you stupid when you losing money, no matter what

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Speaker 6: you can point to, no matter what evidence you can

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Speaker 6: point to, I should say, the whole world.

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Speaker 5: Your mom still likes you, but a lot of your

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Speaker 5: world and no, a lot of investors stuck with us,

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Speaker 5: and I love them for it. But you do lose

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Speaker 5: a lot of people. So it's not fun for a

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Speaker 5: business to go through that.

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Speaker 4: Intuitively, though, it's to your point measuring the disutility of

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Speaker 4: long draw downs versus deep draw downs, this must be

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Speaker 4: a very acute thing for anyone who's not just managing

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Speaker 4: their own personal portfolio. For the reason that you've just described.

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Speaker 6: Yeah, I've talked about this with other money managers, this

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Speaker 6: idea of length versus severity. Yeah, and I've never had

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Speaker 6: one who's not like, yeah, length is much worse.

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Speaker 5: Well. One of the things is when.

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Speaker 6: Something goes on, it's often a similar story for two

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Speaker 6: and a quarter years. Right, So you go back after

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Speaker 6: six months where rationality is getting absolutely punished. You get

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Speaker 6: a lot of sympathy from people. You show them look

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Speaker 6: bigger bargains, We're right, we're gonna be right. You go

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Speaker 6: back six months.

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Speaker 5: Later, they're like, Okay, you go back.

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Speaker 6: Six months and six months later they're like, you're just

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Speaker 6: saying the same adventure.

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Speaker 4: And eventually they must just think maybe you're a dinosaur,

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Speaker 4: Like maybe you just don't get this new wave that's

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Speaker 4: going on.

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Speaker 3: Is it possible that we've really had.

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Speaker 5: Again, it's not everyone.

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Speaker 6: We have the tremendous amount of investors stick with us,

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Speaker 6: and we have ones who double up, who get it

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Speaker 6: that those are often opportunities. But you shrink when you

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Speaker 6: lose money for a while, and you grow when you

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Speaker 6: make money for a while. It's the ironclad rule of

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Speaker 6: this business. And sometimes it's just backwards.

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Speaker 2: So one of the things that has been happening recently

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Speaker 2: that has changed from twenty fifteen when we started doing

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Speaker 2: this is retail participation in the market and it just

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Speaker 2: feels like it is such a big thing for retail

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Speaker 2: investors now to use things like options, even you know,

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Speaker 2: one or zero day options, the kind of stuff that

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Speaker 2: you might more traditionally associate with someone running a hedge fund.

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Speaker 2: Now they're trading Oh.

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Speaker 5: We're not stupid enough, Okay, options.

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Speaker 2: All right, some hedge funds. Then, does the increased presence

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Speaker 2: of retail in the market change the way that you

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Speaker 2: do business at all?

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Speaker 6: I think it contributes to what we're talking about of

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Speaker 6: this dislocation. I hate this because I sound very elitist

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Speaker 6: when I diss on retail, but there's a lot of

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Speaker 6: academic work that shows retail on net loses net is important.

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Speaker 6: They go through periods where they win, They go through

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Speaker 6: periods where they win a lot. You know, if you

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Speaker 6: buy a memestock into triples tomorrow, you don't always lose.

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Speaker 6: But on average, retail transfers money to Wall Street and

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Speaker 6: to institutions. So if there are a bigger force in markets,

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Speaker 6: you're going to have more of that going on. And

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Speaker 6: it jives perfectly because that would raise the opportunity if

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Speaker 6: they're generally on the wrong side of things, but there

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Speaker 6: are more of them many more of them than they

439
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Speaker 6: used to be. They can be right even if they're

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Speaker 6: wrong on the facts. They can be right on the

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Speaker 6: numbers for longer than they used to be. So I

442
00:20:28,640 --> 00:20:30,639
Speaker 6: do think this is part of it. And I apologize

443
00:20:30,680 --> 00:20:33,440
Speaker 6: to all the really smart retail investors. Anytime you talk

444
00:20:33,480 --> 00:20:36,560
Speaker 6: about averages, you're dissing a whole lot of people who

445
00:20:36,560 --> 00:20:37,919
Speaker 6: don't deserve to be.

446
00:20:38,040 --> 00:20:41,000
Speaker 5: But on average, retail loses zero day options.

447
00:20:42,080 --> 00:20:45,879
Speaker 6: My god, they're making exactly the people they claim to

448
00:20:45,920 --> 00:20:49,679
Speaker 6: despise on Wall Street very rich by trading these things.

449
00:20:50,040 --> 00:20:52,600
Speaker 5: And it's just fan duels. I love that.

450
00:20:52,880 --> 00:20:54,400
Speaker 4: Well, all right, we got to get to the question

451
00:20:54,480 --> 00:20:57,720
Speaker 4: of why right, But one last question sort of leading

452
00:20:57,800 --> 00:20:58,159
Speaker 4: up to it.

453
00:20:58,640 --> 00:21:00,800
Speaker 3: How do you establish that what do you look.

454
00:21:00,600 --> 00:21:04,040
Speaker 4: At in the market that right now you say this

455
00:21:04,200 --> 00:21:07,080
Speaker 4: is a less efficient market than once it was. What

456
00:21:07,200 --> 00:21:09,159
Speaker 4: is the measure or is it just feel where you

457
00:21:09,200 --> 00:21:11,640
Speaker 4: just feel sort of crazy like all of us observe.

458
00:21:11,520 --> 00:21:14,600
Speaker 6: Most of it is quantitative. I do start with this

459
00:21:14,640 --> 00:21:18,000
Speaker 6: thing I've talked about, right, It spreads between cheap and expensive. Yeah, yeah,

460
00:21:18,000 --> 00:21:21,000
Speaker 6: I don't just look at spreads. I wrote a piece

461
00:21:21,040 --> 00:21:23,560
Speaker 6: back in two thousand during the tech bubble it's.

462
00:21:23,359 --> 00:21:24,240
Speaker 5: Called bubble logic.

463
00:21:24,320 --> 00:21:25,919
Speaker 6: It never got published because I tried to make a

464
00:21:25,920 --> 00:21:28,159
Speaker 6: book out of it and the bubble came down too

465
00:21:28,200 --> 00:21:30,359
Speaker 6: fast for me. That was good for my business, but

466
00:21:30,440 --> 00:21:33,560
Speaker 6: bad form my author career. Where I didn't just look

467
00:21:33,760 --> 00:21:36,920
Speaker 6: at price multiples. I looked at it in a more

468
00:21:36,920 --> 00:21:40,320
Speaker 6: holistic sense. What growth do we need to justify these

469
00:21:40,520 --> 00:21:43,040
Speaker 6: multiples trying to come up The only time I will

470
00:21:43,119 --> 00:21:46,720
Speaker 6: use the word bubble is when I've tried very hard,

471
00:21:47,280 --> 00:21:49,119
Speaker 6: and it doesn't mean we'll come up with the same answer,

472
00:21:49,160 --> 00:21:50,440
Speaker 6: but this is the framework I use.

473
00:21:50,960 --> 00:21:52,560
Speaker 5: I've tried very hard to come up with.

474
00:21:52,480 --> 00:21:55,200
Speaker 6: Assumptions, even if I don't like them and think they're

475
00:21:55,359 --> 00:21:59,520
Speaker 6: at the outer edge of possible, that could justify these prices. Yeah,

476
00:21:59,560 --> 00:22:01,919
Speaker 6: and if they don't, I don't come close. Twice in

477
00:22:02,000 --> 00:22:04,480
Speaker 6: my career I've been willing to go no, I'm willing

478
00:22:04,480 --> 00:22:06,960
Speaker 6: to use the B word. I should tell you right now.

479
00:22:07,520 --> 00:22:10,480
Speaker 6: When it comes to within stocks, the whole market is

480
00:22:10,480 --> 00:22:12,720
Speaker 6: a different issue. The market is quite expensive right now.

481
00:22:13,040 --> 00:22:15,520
Speaker 6: But when it comes to this spread between cheap and expensive,

482
00:22:16,000 --> 00:22:18,840
Speaker 6: I'm not using the bubble word oka that same measure.

483
00:22:19,160 --> 00:22:21,240
Speaker 6: It's not the one hundred and twenty fifth percentile anymore,

484
00:22:21,280 --> 00:22:25,120
Speaker 6: it's the seventy seventh percentile. It's wider than on average,

485
00:22:25,160 --> 00:22:27,320
Speaker 6: maybe making it a little more attractive if you can

486
00:22:27,359 --> 00:22:30,000
Speaker 6: stick with it that big if. But I don't use

487
00:22:30,040 --> 00:22:33,199
Speaker 6: the word bubble for seventy seventh percentile. I used it

488
00:22:33,240 --> 00:22:36,679
Speaker 6: for blowing through. So five years ago I was saying

489
00:22:37,119 --> 00:22:38,960
Speaker 6: it's a bubble. Now I'm just saying this is a

490
00:22:39,000 --> 00:22:40,760
Speaker 6: little bit of an odd market.

491
00:22:41,400 --> 00:22:43,760
Speaker 2: So I want to ask you more about the bubble.

492
00:22:43,800 --> 00:22:45,439
Speaker 2: But we keep touting that we're going to ask you

493
00:22:45,440 --> 00:22:47,800
Speaker 2: the why question. So let's ask the why question? Okay,

494
00:22:47,800 --> 00:22:49,840
Speaker 2: why are markets becoming less efficient?

495
00:22:49,960 --> 00:22:50,320
Speaker 5: Okay?

496
00:22:50,560 --> 00:22:53,000
Speaker 6: Well, first of all, and I say this in the piece,

497
00:22:53,600 --> 00:22:54,840
Speaker 6: a lot of conjecture going on.

498
00:22:54,880 --> 00:22:55,760
Speaker 5: This is an op ed.

499
00:22:56,480 --> 00:23:00,359
Speaker 6: As statisticians, it sound like a fifty page Yeah, fifty

500
00:23:02,080 --> 00:23:05,600
Speaker 6: academic look our first time doing this, you will be convinced.

501
00:23:05,600 --> 00:23:07,960
Speaker 6: I could do a fifty page, single space ofp ed.

502
00:23:08,240 --> 00:23:08,760
Speaker 3: I believe it.

503
00:23:08,840 --> 00:23:09,480
Speaker 5: I know you can't.

504
00:23:09,560 --> 00:23:13,119
Speaker 6: It's just as a quant as a statistician, you'd like

505
00:23:13,200 --> 00:23:16,160
Speaker 6: to see a hundred bubbles have stats on each one.

506
00:23:16,240 --> 00:23:18,240
Speaker 6: This way, they're all they'd rhyme, but they wouldn't be

507
00:23:18,280 --> 00:23:21,000
Speaker 6: exactly the same you tease out what's going on. If

508
00:23:21,040 --> 00:23:24,320
Speaker 6: you see two in a thirty five year career, You're

509
00:23:24,320 --> 00:23:27,399
Speaker 6: not gonna be able to prove this statistically. But I

510
00:23:27,440 --> 00:23:29,920
Speaker 6: believe in my conjectures. I'm not soft selling them. I'm

511
00:23:30,240 --> 00:23:32,360
Speaker 6: I just wanted clear that nobody is going to walk

512
00:23:32,359 --> 00:23:33,040
Speaker 6: away saying.

513
00:23:32,880 --> 00:23:33,440
Speaker 5: He proved it.

514
00:23:34,000 --> 00:23:37,000
Speaker 6: I list a few reasons in the piece why markets

515
00:23:37,080 --> 00:23:40,040
Speaker 6: might be prone to bouts of bigger disconnects from reality.

516
00:23:40,640 --> 00:23:44,480
Speaker 6: My second favorite is one I'm sure you've talked about.

517
00:23:44,680 --> 00:23:46,879
Speaker 6: I know I've listened to you guys talk about it,

518
00:23:46,920 --> 00:23:50,760
Speaker 6: the rise of passive investing. I am not a passive hater.

519
00:23:51,359 --> 00:23:54,160
Speaker 6: They're really smart people. Mike Green's out there the Mount.

520
00:23:54,200 --> 00:23:55,320
Speaker 6: That guy hates passive.

521
00:23:56,000 --> 00:23:56,359
Speaker 5: I don't know.

522
00:23:56,359 --> 00:23:59,040
Speaker 6: The Middle East has never seen hate like the Mount.

523
00:23:59,119 --> 00:24:02,000
Speaker 6: Mike Green hates pass but again, he's a smart guy.

524
00:24:02,040 --> 00:24:04,280
Speaker 6: He makes interesting arguments. I'm not that guy. I think

525
00:24:04,320 --> 00:24:07,760
Speaker 6: passive has been a huge positive for an investor welfare.

526
00:24:07,800 --> 00:24:10,240
Speaker 6: I actually was lucky enough to be fairly good friends

527
00:24:10,280 --> 00:24:12,560
Speaker 6: with Jack Bogel, and he was a hero of mine.

528
00:24:12,840 --> 00:24:15,440
Speaker 6: We had a podcast briefly and he and he came

529
00:24:15,480 --> 00:24:17,800
Speaker 6: on it. In fact, i'll tell you part of that

530
00:24:17,840 --> 00:24:20,840
Speaker 6: story in a second. So I'm not a passive hater.

531
00:24:21,240 --> 00:24:24,119
Speaker 6: But here's what we know. We know the whole world

532
00:24:24,119 --> 00:24:26,919
Speaker 6: cannot be passive. And when I say passive, I mean

533
00:24:26,960 --> 00:24:29,760
Speaker 6: in a Jack Bogel market cap weighted sence. Some nice

534
00:24:29,760 --> 00:24:32,160
Speaker 6: people use passive for people like us, and they really

535
00:24:32,200 --> 00:24:34,879
Speaker 6: mean rules base and that's not how I use the

536
00:24:34,920 --> 00:24:35,680
Speaker 6: word passive.

537
00:24:35,960 --> 00:24:37,400
Speaker 3: I mean someone who owns the entire mark.

538
00:24:37,480 --> 00:24:39,359
Speaker 6: Yeah, we're long, short and leverage. How you get to

539
00:24:39,400 --> 00:24:41,760
Speaker 6: passive on that, I don't know, but some people do.

540
00:24:42,040 --> 00:24:44,720
Speaker 6: I own the entire market. We had Jack on the

541
00:24:44,760 --> 00:24:48,920
Speaker 6: podcast and he absolutely agreed everyone can't be passive. Now,

542
00:24:48,960 --> 00:24:51,160
Speaker 6: of course, being Jack Bogel, he thinks at that point

543
00:24:51,200 --> 00:24:54,359
Speaker 6: the marginal investor should still move to passive. But he

544
00:24:54,440 --> 00:24:56,879
Speaker 6: recognizes the obvious fact that if one hundred percent of

545
00:24:56,920 --> 00:25:00,640
Speaker 6: the people are not looking at prices, nobody's looking prices.

546
00:25:00,800 --> 00:25:03,240
Speaker 6: Who's figuring out if Nvidia is worth more or less

547
00:25:03,240 --> 00:25:07,080
Speaker 6: than the corner drug store? Right, So the market gets

548
00:25:07,160 --> 00:25:09,919
Speaker 6: very weird there. We don't even understand what happens. It's

549
00:25:09,960 --> 00:25:12,399
Speaker 6: a singularity. I use a physics analogy. We don't know

550
00:25:12,440 --> 00:25:16,480
Speaker 6: what happens there. PhD students in finance, and I used

551
00:25:16,480 --> 00:25:18,280
Speaker 6: to be one of these, will stay up late at

552
00:25:18,359 --> 00:25:22,200
Speaker 6: night in their cups talking about what happens if everyone

553
00:25:22,280 --> 00:25:24,639
Speaker 6: was passive. What would it even look like? We know

554
00:25:24,680 --> 00:25:28,199
Speaker 6: it's very weird, and I doubt all the weirdness happens

555
00:25:28,200 --> 00:25:30,600
Speaker 6: between ninety nine point nine nine nine percent passive and

556
00:25:30,640 --> 00:25:33,639
Speaker 6: one hundred. So we're on a curve. We're a lot

557
00:25:33,760 --> 00:25:36,000
Speaker 6: more passive than we used to be. Even that you're

558
00:25:36,000 --> 00:25:38,600
Speaker 6: probably aware, it's hard to measure exactly how much just passive,

559
00:25:39,200 --> 00:25:42,399
Speaker 6: direct passive true market cap weight you can measure. But

560
00:25:42,400 --> 00:25:45,040
Speaker 6: what about people who take you know, eighty basis points

561
00:25:45,040 --> 00:25:48,119
Speaker 6: of tracking era, they're kind of mostly passive.

562
00:25:48,200 --> 00:25:49,639
Speaker 3: You have, remember, pegged.

563
00:25:49,400 --> 00:25:51,920
Speaker 2: To like custom indices. Now, it's kind of funny you.

564
00:25:51,840 --> 00:25:55,120
Speaker 6: Guys remember the Princess Bride? Yeah, yeah, remember mostly dead.

565
00:25:55,720 --> 00:26:00,560
Speaker 6: I'm fully dead. You're mostly passive. So I think fewer

566
00:26:00,600 --> 00:26:03,720
Speaker 6: people thinking about prices, fewer people willing to take the

567
00:26:03,760 --> 00:26:06,440
Speaker 6: other side when things get a little crazy. If one

568
00:26:06,600 --> 00:26:09,480
Speaker 6: side really starts to get crazy, there are fewer people

569
00:26:09,560 --> 00:26:13,840
Speaker 6: out there that has to exacerbate these swings. My actual

570
00:26:14,280 --> 00:26:19,320
Speaker 6: number one reason, though you talked about the gamification, for me,

571
00:26:19,400 --> 00:26:22,000
Speaker 6: it's the overall, I'm going to sound like a very

572
00:26:22,200 --> 00:26:25,440
Speaker 6: old man yelling at the sky on my lawn right now,

573
00:26:25,480 --> 00:26:26,720
Speaker 6: you know, get off my lawn, or.

574
00:26:26,720 --> 00:26:29,760
Speaker 5: Yelling at the sky some Simpsons thing. Yeah, I'm abe

575
00:26:30,200 --> 00:26:31,080
Speaker 5: in this case.

576
00:26:31,280 --> 00:26:32,520
Speaker 3: But Saniel's a cloud.

577
00:26:32,600 --> 00:26:33,159
Speaker 5: Yeah exactly.

578
00:26:33,160 --> 00:26:36,159
Speaker 6: Thank you social media and the broader environment that you

579
00:26:36,160 --> 00:26:39,040
Speaker 6: guys were talking about. I don't want to do politics

580
00:26:39,680 --> 00:26:42,879
Speaker 6: except to say I don't think you find many people.

581
00:26:42,920 --> 00:26:44,480
Speaker 6: There'll be some, but I don't think you find many

582
00:26:44,480 --> 00:26:47,960
Speaker 6: people who don't agree with the idea that this environment

583
00:26:48,000 --> 00:26:52,439
Speaker 6: has made our politics worse and more dangerous confirmation bias.

584
00:26:52,560 --> 00:26:54,040
Speaker 5: We live in our own bubbles.

585
00:26:54,600 --> 00:26:57,919
Speaker 6: We have algorithms that push us further and further towards

586
00:26:58,280 --> 00:27:01,040
Speaker 6: You start out as a moderate belief, but it keeps

587
00:27:01,040 --> 00:27:04,600
Speaker 6: pushing you towards extremes, and pretty soon you're saying stupid

588
00:27:04,640 --> 00:27:06,080
Speaker 6: things like, Hey, that Tucker Carlson.

589
00:27:06,119 --> 00:27:08,920
Speaker 5: He's a good guy. All right, I might have revealed

590
00:27:08,920 --> 00:27:09,440
Speaker 5: a little poem.

591
00:27:09,600 --> 00:27:11,199
Speaker 2: Yeah you did a bit of politics there.

592
00:27:11,680 --> 00:27:16,159
Speaker 5: So all of that adds up to making our politics worse,

593
00:27:16,600 --> 00:27:22,840
Speaker 5: more prone, in particular to swings and extremes. Markets are

594
00:27:22,880 --> 00:27:27,720
Speaker 5: not arbitrage mechanisms that sometimes misunderstood they're voting mechanisms.

595
00:27:28,160 --> 00:27:30,600
Speaker 6: The price is a weight at average vote, where the

596
00:27:30,600 --> 00:27:33,520
Speaker 6: weight is by dollars. I lucky enough to get more

597
00:27:33,560 --> 00:27:35,639
Speaker 6: vote than the average person, and Warren Buffett gets a

598
00:27:35,680 --> 00:27:37,840
Speaker 6: lot more votes than I get if we all disagree

599
00:27:37,880 --> 00:27:41,240
Speaker 6: on opinions. The reason it's not an arbitrage mechanism. And

600
00:27:41,280 --> 00:27:45,440
Speaker 6: here I'll get a little geeky. Imagine you're reasonably sure

601
00:27:45,440 --> 00:27:48,119
Speaker 6: this thing is mispriced in that Graham and DoD sense,

602
00:27:49,600 --> 00:27:53,440
Speaker 6: and you think it's massively mispriced, trading enough to make

603
00:27:53,520 --> 00:27:57,240
Speaker 6: it a third less mispriced. It's not very risky to

604
00:27:57,280 --> 00:27:59,920
Speaker 6: you because it's not that big a trade, and it's

605
00:28:00,000 --> 00:28:04,440
Speaker 6: it's very high expected return because it's that mispriced. Now

606
00:28:04,480 --> 00:28:06,919
Speaker 6: you've moved it back to a third or maybe half,

607
00:28:07,520 --> 00:28:09,960
Speaker 6: the next part of the trade is much riskier to

608
00:28:10,000 --> 00:28:12,159
Speaker 6: you because you already have the trade on, so you're.

609
00:28:12,040 --> 00:28:12,760
Speaker 5: Just adding more.

610
00:28:13,240 --> 00:28:13,760
Speaker 2: Oh, I see it.

611
00:28:13,840 --> 00:28:14,600
Speaker 5: Yeah, and it.

612
00:28:14,560 --> 00:28:17,359
Speaker 6: Has half the game because you've already closed it by half.

613
00:28:17,640 --> 00:28:21,640
Speaker 6: So arbitrage will not take things. If on net more

614
00:28:21,640 --> 00:28:24,840
Speaker 6: people believe something stupid, stupid's gonna win, and we're going

615
00:28:24,920 --> 00:28:27,199
Speaker 6: to be at least somewhat off of real prices. And

616
00:28:27,240 --> 00:28:30,920
Speaker 6: I find it remarkably easy to believe that this same

617
00:28:31,040 --> 00:28:33,919
Speaker 6: environment that makes our politics go a little crazy for

618
00:28:34,000 --> 00:28:37,680
Speaker 6: markets to be efficient in any degree, not even perfectly efficient,

619
00:28:37,960 --> 00:28:41,040
Speaker 6: This famous idea of the wisdom of crowds has to

620
00:28:41,040 --> 00:28:43,760
Speaker 6: be helping us a lot. The hypothesis that we're all

621
00:28:43,800 --> 00:28:47,640
Speaker 6: geniuses is never gonna fly, right. So the wisdom of

622
00:28:47,680 --> 00:28:50,160
Speaker 6: crowds and you know it well says, even if on

623
00:28:50,200 --> 00:28:53,400
Speaker 6: average most people don't know the answer, the stupid answers

624
00:28:53,440 --> 00:28:56,840
Speaker 6: cancel and the right answers don't because they're the same.

625
00:28:57,200 --> 00:28:59,280
Speaker 6: My favorite example of that, and this it might be

626
00:28:59,360 --> 00:29:02,080
Speaker 6: dating myself is Regis philbin and who wants to be

627
00:29:02,120 --> 00:29:02,720
Speaker 6: a millionaire?

628
00:29:03,440 --> 00:29:05,800
Speaker 5: Right? Remember the show Multiple Choice.

629
00:29:05,560 --> 00:29:08,240
Speaker 2: Showy, it's frightening that you're describing that as old, but

630
00:29:08,280 --> 00:29:08,920
Speaker 2: I guess it is.

631
00:29:09,400 --> 00:29:11,920
Speaker 5: Yeah, it's been off the air, sadly Regis has passed away.

632
00:29:11,960 --> 00:29:12,440
Speaker 5: It's old.

633
00:29:12,880 --> 00:29:16,400
Speaker 6: Sorry, sorry, but you had to answer multiple choice questions

634
00:29:16,400 --> 00:29:18,400
Speaker 6: and if you miss one, you're out. They start off

635
00:29:18,480 --> 00:29:21,040
Speaker 6: ridiculously easy, and they get harder and harder. You had

636
00:29:21,120 --> 00:29:25,000
Speaker 6: multiple cheats, like three cheats. One was a friend was

637
00:29:25,040 --> 00:29:29,840
Speaker 6: almost useless. A friend usually wasn't much smarter than you,

638
00:29:30,200 --> 00:29:32,520
Speaker 6: and b people at least to my eye, I didn't

639
00:29:32,520 --> 00:29:34,840
Speaker 6: watch every episode, but seemed to choose friends who knew

640
00:29:34,880 --> 00:29:37,360
Speaker 6: the same stuff they knew, Right, you really want to

641
00:29:37,440 --> 00:29:39,920
Speaker 6: choose a friend who is in like a totally different field.

642
00:29:40,040 --> 00:29:42,160
Speaker 2: I think in some countries the friends also had a

643
00:29:42,240 --> 00:29:45,880
Speaker 2: tendency to deliberately give the wrong answer because they just

644
00:29:45,920 --> 00:29:47,880
Speaker 2: didn't want to see their friend actually win money.

645
00:29:47,920 --> 00:29:51,400
Speaker 5: Well, the most cynical phrase ever is nothing succeeds like

646
00:29:51,400 --> 00:29:52,520
Speaker 5: a friend's failure.

647
00:29:52,960 --> 00:29:55,600
Speaker 6: I don't believe I'm not condoning that, but it is

648
00:29:55,600 --> 00:29:58,760
Speaker 6: out there. The other one was eliminate two of the

649
00:29:58,840 --> 00:30:02,160
Speaker 6: row answers. That's great, obviously, even if you have no idea.

650
00:30:02,240 --> 00:30:03,640
Speaker 6: You go from one out of four to one out

651
00:30:03,680 --> 00:30:04,480
Speaker 6: of two.

652
00:30:04,640 --> 00:30:06,520
Speaker 5: The other one was pulled the audience.

653
00:30:06,960 --> 00:30:12,680
Speaker 6: And at least to my non exhaustive examination, it seemed

654
00:30:12,680 --> 00:30:15,160
Speaker 6: to work pretty much every time, even if.

655
00:30:15,040 --> 00:30:16,080
Speaker 5: The question was hard.

656
00:30:16,360 --> 00:30:20,160
Speaker 6: Because imagine you have one hundred people in a room.

657
00:30:20,440 --> 00:30:23,280
Speaker 6: Ten of them know the answer, the other ones are guessing.

658
00:30:23,840 --> 00:30:28,160
Speaker 6: The ninety distribute evenly over the four. Maybe not perfectly evenly,

659
00:30:28,360 --> 00:30:32,320
Speaker 6: but roughly evenly. The ten all land on B. So

660
00:30:32,360 --> 00:30:35,880
Speaker 6: you pick B because it's bigger. Work pretty much every time.

661
00:30:36,080 --> 00:30:39,400
Speaker 6: There's a crucial assumption in that the audience has to

662
00:30:39,480 --> 00:30:41,320
Speaker 6: be releively independent of each other.

663
00:30:41,720 --> 00:30:44,240
Speaker 5: And they did that. They weren't talking. It was silent voting.

664
00:30:44,640 --> 00:30:46,880
Speaker 6: If the audience all gets to talk, maybe the ten

665
00:30:47,240 --> 00:30:50,800
Speaker 6: convinced the ninety, but maybe they don't. Maybe a demagogue

666
00:30:50,840 --> 00:30:55,800
Speaker 6: with a better Twitter feed convinces everyone. And if you

667
00:30:55,920 --> 00:30:58,120
Speaker 6: ruin the independence, and I think, have we ever come

668
00:30:58,200 --> 00:31:02,880
Speaker 6: up with a better vehicle turning a wisdom of crowds

669
00:31:02,920 --> 00:31:06,920
Speaker 6: into a craziness of mobs than social media?

670
00:31:07,440 --> 00:31:09,440
Speaker 5: I'd be hard pressed to describe it.

671
00:31:09,520 --> 00:31:10,680
Speaker 3: I find this would be very compelling.

672
00:31:10,800 --> 00:31:12,320
Speaker 4: James sir Wicket at the New York Times, he came

673
00:31:12,320 --> 00:31:14,280
Speaker 4: out with that book Wisdom of Crowds in two thousand

674
00:31:14,320 --> 00:31:16,960
Speaker 4: and five, but that was right before social media blew up.

675
00:31:17,360 --> 00:31:20,160
Speaker 4: And then you know, there's that famous book in eighteen

676
00:31:20,280 --> 00:31:24,200
Speaker 4: forty one, Extraordinary Popular Delusion and the Madness of the Crowds.

677
00:31:24,240 --> 00:31:27,280
Speaker 4: So we've always sort of understood that crowds can be

678
00:31:27,600 --> 00:31:31,120
Speaker 4: both mad and wise. And I find this very interesting,

679
00:31:31,120 --> 00:31:35,040
Speaker 4: the idea that perhaps the linkedness of the crowd is

680
00:31:35,120 --> 00:31:37,200
Speaker 4: what sort of flips it from wisdom to meta.

681
00:31:37,720 --> 00:31:41,360
Speaker 6: I think that's exactly It maybe can come up with counterexamples,

682
00:31:41,360 --> 00:31:43,440
Speaker 6: but I think most of the time, if the crowd

683
00:31:43,480 --> 00:31:46,320
Speaker 6: is making independent decisions, then this can go for political voting,

684
00:31:46,560 --> 00:31:48,600
Speaker 6: it can go for markets. You're going to get some

685
00:31:48,680 --> 00:31:51,240
Speaker 6: degree of wisdom. When the crowd is all making a

686
00:31:51,320 --> 00:31:54,600
Speaker 6: unified decision, it could still work out, but you are

687
00:31:54,680 --> 00:31:58,560
Speaker 6: much more susceptible to what the quants technical term is.

688
00:31:58,640 --> 00:32:17,360
Speaker 4: Craig, Craig, I want to pivot a little bit and

689
00:32:17,400 --> 00:32:21,600
Speaker 4: talk about AI. You've written or you've talked recently. I

690
00:32:21,600 --> 00:32:23,640
Speaker 4: forget exactly the word that was used in some of

691
00:32:23,640 --> 00:32:27,760
Speaker 4: the headlines succumbing to the rendering, our surrendering.

692
00:32:27,440 --> 00:32:29,360
Speaker 3: To the I regret saying so forth.

693
00:32:30,360 --> 00:32:32,800
Speaker 4: We tried to talk a lot about AI. I still

694
00:32:32,800 --> 00:32:35,479
Speaker 4: don't know exactly what it means in any context. What

695
00:32:35,520 --> 00:32:38,160
Speaker 4: does it mean to surrender to the machines in the

696
00:32:38,200 --> 00:32:39,360
Speaker 4: AQR context?

697
00:32:39,600 --> 00:32:45,160
Speaker 6: Well, first, not every journalist has Bloomberg's high standards. I

698
00:32:45,200 --> 00:32:48,880
Speaker 6: am reasonably certain I said partially in that, and the

699
00:32:48,920 --> 00:32:53,880
Speaker 6: word partially got dropped even skipping all the details. If

700
00:32:53,920 --> 00:32:57,720
Speaker 6: you're going to use AI in your process at all,

701
00:32:58,600 --> 00:33:02,040
Speaker 6: almost by definition, you were gonna lose a little intuition,

702
00:33:02,520 --> 00:33:04,040
Speaker 6: and it bothered me for a couple of years. I

703
00:33:04,040 --> 00:33:05,800
Speaker 6: think I slowed us down on AI by a year

704
00:33:05,880 --> 00:33:08,560
Speaker 6: or two just by saying, you know, we've always prided

705
00:33:08,600 --> 00:33:11,760
Speaker 6: ourselves on the balance of we intuitively understand why we

706
00:33:11,800 --> 00:33:14,640
Speaker 6: think this makes money and the evidence that it makes money.

707
00:33:15,000 --> 00:33:18,320
Speaker 6: And when you go to AI, you're normally giving.

708
00:33:18,120 --> 00:33:21,280
Speaker 5: Up some not all. We actually do try very hard

709
00:33:21,320 --> 00:33:24,480
Speaker 5: to figure out why we think this works or doesn't work.

710
00:33:25,000 --> 00:33:28,280
Speaker 5: But if you weren't giving up some intuition, what the

711
00:33:28,280 --> 00:33:31,320
Speaker 5: heck is the AI doing. If it's simple and you

712
00:33:31,360 --> 00:33:34,000
Speaker 5: could have just seen it with the naked eye, it's

713
00:33:34,000 --> 00:33:36,320
Speaker 5: hard to imagine it's helping. But let me give you

714
00:33:36,360 --> 00:33:39,360
Speaker 5: a concrete example. We like good momentum.

715
00:33:39,840 --> 00:33:42,600
Speaker 6: We like it in price, we like it in fundamentals.

716
00:33:43,120 --> 00:33:45,920
Speaker 6: One way people on the QUANDT side have tried to

717
00:33:45,920 --> 00:33:49,640
Speaker 6: measure this for years is something like Earning's calls, trying

718
00:33:49,680 --> 00:33:51,760
Speaker 6: to decide if Earning's calls a good news are bad news,

719
00:33:52,000 --> 00:33:54,880
Speaker 6: and if people underreact to good news, you want to

720
00:33:54,880 --> 00:33:55,920
Speaker 6: buy when it's good news.

721
00:33:56,440 --> 00:33:59,320
Speaker 2: Is this just like how many times people say great quarter, guys,

722
00:33:59,440 --> 00:34:00,640
Speaker 2: or are you looking as something else?

723
00:34:00,800 --> 00:34:03,480
Speaker 6: It's gonna sound about as silly as that you build

724
00:34:03,560 --> 00:34:08,759
Speaker 6: up tables of words and phrases with numerical values and

725
00:34:08,800 --> 00:34:12,359
Speaker 6: then you say, what's the numerical score of this? And

726
00:34:12,520 --> 00:34:14,239
Speaker 6: they can be much more subtle than this. I'm gonna

727
00:34:14,280 --> 00:34:18,279
Speaker 6: use a real simple example the word increasing plus one, right,

728
00:34:18,520 --> 00:34:20,720
Speaker 6: and I'm sure you see the flaw. If the actual

729
00:34:20,760 --> 00:34:25,919
Speaker 6: sentence was massive embezzlement is increasing, you know, are bad

730
00:34:25,960 --> 00:34:26,480
Speaker 6: on that one.

731
00:34:26,520 --> 00:34:28,640
Speaker 2: The amount of fraud we're seeing in our private credit

732
00:34:28,680 --> 00:34:29,719
Speaker 2: deals is increasing.

733
00:34:30,000 --> 00:34:33,879
Speaker 6: Quant can survive looking stupid a lot. If that's forty

734
00:34:33,920 --> 00:34:35,960
Speaker 6: seven percent of the time, If fifty three percent of

735
00:34:35,960 --> 00:34:39,040
Speaker 6: the time it's getting it right, fine, And those things

736
00:34:39,280 --> 00:34:42,239
Speaker 6: had some efficacy, but they weren't great. What we do

737
00:34:42,320 --> 00:34:49,320
Speaker 6: today is we train mL. It's called natural language processing.

738
00:34:49,360 --> 00:34:53,040
Speaker 6: It's the sub field of mL to analyze corporate statements.

739
00:34:53,080 --> 00:34:54,840
Speaker 6: But what it does, and this is going to be

740
00:34:54,880 --> 00:34:58,440
Speaker 6: the geekiest thing, I'll say, it represents every corporate statement.

741
00:34:58,440 --> 00:35:01,279
Speaker 6: It looks across them and presents them as a set

742
00:35:01,280 --> 00:35:03,960
Speaker 6: of numbers, what the geeks will call a vector of numbers.

743
00:35:04,840 --> 00:35:06,640
Speaker 5: Then what we do is empirics to.

744
00:35:06,640 --> 00:35:09,880
Speaker 6: Say, all right, we have fifty years of this across

745
00:35:09,920 --> 00:35:13,439
Speaker 6: many firms. We have different vectors or numbers for every

746
00:35:13,440 --> 00:35:18,279
Speaker 6: earnings call. What combination go long when the first number

747
00:35:18,360 --> 00:35:21,359
Speaker 6: is good, short when the second number is high?

748
00:35:21,440 --> 00:35:24,080
Speaker 5: Blah blah blah. What best combination forecasts?

749
00:35:24,840 --> 00:35:28,040
Speaker 6: That seems to be correlated to what we were doing before.

750
00:35:28,080 --> 00:35:31,400
Speaker 6: These word count things just considerably better.

751
00:35:32,200 --> 00:35:34,040
Speaker 5: It does a better job than word counts.

752
00:35:34,120 --> 00:35:37,560
Speaker 6: That language is very nonlinear whether something is good, whether

753
00:35:37,600 --> 00:35:40,400
Speaker 6: that word increasing is good. AI is not perfect, but

754
00:35:40,480 --> 00:35:43,120
Speaker 6: it's chance of figuring out if increasing was good or bad,

755
00:35:43,560 --> 00:35:45,799
Speaker 6: especially when it's trained on tons of these Yes, better

756
00:35:45,840 --> 00:35:48,640
Speaker 6: than us. Here's where you lose the intuition. Though I

757
00:35:48,680 --> 00:35:49,440
Speaker 6: skip a step.

758
00:35:50,080 --> 00:35:52,080
Speaker 5: I like to do that. It's fun sneaky.

759
00:35:53,239 --> 00:35:56,080
Speaker 6: If you ask myself or even some of the younger

760
00:35:56,120 --> 00:35:59,040
Speaker 6: people who are much more tooled up on machine learning

761
00:35:59,040 --> 00:36:01,400
Speaker 6: than I am these days, what does that vector of

762
00:36:01,480 --> 00:36:07,319
Speaker 6: numbers actually mean? You often get, Ah, we really can't

763
00:36:07,320 --> 00:36:07,719
Speaker 6: tell you that.

764
00:36:07,800 --> 00:36:09,000
Speaker 5: We can say it.

765
00:36:09,000 --> 00:36:12,360
Speaker 6: It's summing up, in a mathematical sense, the information content

766
00:36:12,440 --> 00:36:17,080
Speaker 6: of that. We still get intuition because this indicator acts

767
00:36:17,160 --> 00:36:19,719
Speaker 6: like a short term momentum indicator, so it's picking up

768
00:36:19,760 --> 00:36:22,360
Speaker 6: what we wanted to pick up. It's also done fabulously

769
00:36:22,360 --> 00:36:25,480
Speaker 6: well for us for multiple years in real life, but

770
00:36:26,200 --> 00:36:28,520
Speaker 6: we are giving up intuition at one stage that we

771
00:36:28,680 --> 00:36:32,160
Speaker 6: used to not do. So my answer was meant to

772
00:36:32,160 --> 00:36:35,680
Speaker 6: be much more subtle, that there are give ups in

773
00:36:35,680 --> 00:36:38,640
Speaker 6: intuition when you move to something like machine learning. They

774
00:36:38,680 --> 00:36:40,759
Speaker 6: almost have to be or use again, what are you doing?

775
00:36:41,480 --> 00:36:43,239
Speaker 6: I was uncomfortable for that for a while, so I

776
00:36:43,280 --> 00:36:45,960
Speaker 6: was starting to do mia kulpa saying that I slowed

777
00:36:46,040 --> 00:36:47,759
Speaker 6: us down, and it came out as well.

778
00:36:47,760 --> 00:36:50,359
Speaker 5: I used to hate this, but now I have no job.

779
00:36:50,440 --> 00:36:53,120
Speaker 6: The machine runs it, which is probably gonna be true

780
00:36:53,160 --> 00:36:54,399
Speaker 6: in eight years, but not yet.

781
00:36:54,960 --> 00:36:56,960
Speaker 2: This reminds me actually of something I wanted to ask,

782
00:36:57,000 --> 00:37:00,560
Speaker 2: because another big multi year decade trend is the rise

783
00:37:00,600 --> 00:37:04,759
Speaker 2: of the multi strats. And at AQR, as I understand it,

784
00:37:04,840 --> 00:37:08,920
Speaker 2: you have a multistrat model in there, but it's more

785
00:37:09,160 --> 00:37:11,960
Speaker 2: centralized than some other places. Can you go into a

786
00:37:12,000 --> 00:37:12,920
Speaker 2: little bit more detail.

787
00:37:13,120 --> 00:37:15,719
Speaker 6: Sure, they get used interchangeably, but I think this is

788
00:37:15,760 --> 00:37:20,040
Speaker 6: the difference between multi strat and multi manager. Multistrat just

789
00:37:20,120 --> 00:37:24,000
Speaker 6: means I wrote a dissertation on choosing US stocks. We

790
00:37:24,040 --> 00:37:28,040
Speaker 6: have applied similar things to stocks around the world, to currencies,

791
00:37:28,120 --> 00:37:32,960
Speaker 6: to commodities, to directional bets through trend following. They are

792
00:37:32,960 --> 00:37:36,480
Speaker 6: correlated but low. So we think of these as different

793
00:37:36,480 --> 00:37:40,360
Speaker 6: strategies and we think a set of our strategies is

794
00:37:40,400 --> 00:37:43,560
Speaker 6: better than a single one. Or it's just the power

795
00:37:43,560 --> 00:37:47,440
Speaker 6: of diversification. So we're big believers in particularly if you

796
00:37:47,880 --> 00:37:51,520
Speaker 6: have a common philosophy. So it's not just fitting the data,

797
00:37:51,719 --> 00:37:54,319
Speaker 6: it's fitting into an overall theme of what you believe in.

798
00:37:54,760 --> 00:37:59,000
Speaker 6: We're big believers in multistrats. What we share with multimanager

799
00:37:59,480 --> 00:38:03,680
Speaker 6: is that believe and diversification. Multi manager is what it

800
00:38:03,719 --> 00:38:06,680
Speaker 6: sounds like it is. We are one team building these.

801
00:38:07,400 --> 00:38:09,760
Speaker 6: We might of course have little separate teams at AQR,

802
00:38:09,840 --> 00:38:13,360
Speaker 6: but we're one firm building these. They are farming it

803
00:38:13,400 --> 00:38:16,200
Speaker 6: out to different people. To be frank, if you had

804
00:38:16,239 --> 00:38:20,120
Speaker 6: told me their business model ten twenty years ago, I

805
00:38:20,160 --> 00:38:22,400
Speaker 6: would have been very cynical that it worked. If you

806
00:38:22,520 --> 00:38:24,880
Speaker 6: told me a what the total fees are gonna be

807
00:38:24,920 --> 00:38:28,000
Speaker 6: when you add up everything that's passed through and a

808
00:38:28,000 --> 00:38:29,759
Speaker 6: fair amount of them. And they vary in how quick

809
00:38:29,800 --> 00:38:32,440
Speaker 6: they do this. If something's not working for what I

810
00:38:32,480 --> 00:38:35,799
Speaker 6: would consider a very short while, yeah they stop doing it.

811
00:38:36,120 --> 00:38:38,400
Speaker 6: And I know there are a lot of load to

812
00:38:38,440 --> 00:38:41,879
Speaker 6: medium sharp ratio risk adjustive return strategies that are really

813
00:38:41,880 --> 00:38:45,000
Speaker 6: good long term but have bad periods. So if someone

814
00:38:45,040 --> 00:38:47,439
Speaker 6: told me that model, I would go, you're gonna charge

815
00:38:47,480 --> 00:38:49,680
Speaker 6: a ton and you're gonna throw out people who have

816
00:38:49,680 --> 00:38:54,040
Speaker 6: a bad two quarters. No, and there have been people

817
00:38:54,040 --> 00:38:56,759
Speaker 6: who've obviously proven me wrong. And I'm humble about this.

818
00:38:57,239 --> 00:39:00,239
Speaker 6: I don't fully understand why. They must be very very

819
00:39:00,320 --> 00:39:04,680
Speaker 6: very good at actual selecting the alpha. Yeah, right, And

820
00:39:04,719 --> 00:39:07,879
Speaker 6: this is something that we don't do. We're internal quants

821
00:39:07,920 --> 00:39:10,600
Speaker 6: who build our own models. We've never maybe be interesting

822
00:39:10,640 --> 00:39:12,720
Speaker 6: one day, we've never tried to apply that to choosing

823
00:39:12,760 --> 00:39:13,560
Speaker 6: outside people.

824
00:39:14,000 --> 00:39:15,399
Speaker 5: But plenty of people, by the way, have.

825
00:39:15,400 --> 00:39:19,239
Speaker 6: Tried to start multi manager and failed. So it's not

826
00:39:19,360 --> 00:39:22,520
Speaker 6: like you just apply this charge a ton, hire a

827
00:39:22,520 --> 00:39:24,560
Speaker 6: bunch of active managers and fire them if they have

828
00:39:24,600 --> 00:39:27,280
Speaker 6: a bad two hours. Just automatically works.

829
00:39:27,640 --> 00:39:30,959
Speaker 2: It does seem though, that talent is the thing. That's

830
00:39:31,000 --> 00:39:34,760
Speaker 2: like capping multi strat expansion, right, that's the limiting factor.

831
00:39:35,080 --> 00:39:38,200
Speaker 6: They vary, but some of the major ones are giving

832
00:39:38,239 --> 00:39:41,640
Speaker 6: back money. Even if you believe in this model, and

833
00:39:41,680 --> 00:39:44,640
Speaker 6: I even from Afar, I have to be a believer.

834
00:39:44,719 --> 00:39:47,720
Speaker 6: I've seen the results are too good for too long,

835
00:39:48,120 --> 00:39:51,319
Speaker 6: in my view, to have a decent chance of randomness.

836
00:39:51,480 --> 00:39:54,240
Speaker 6: I don't exactly know what they're doing. I'm still waiting

837
00:39:54,400 --> 00:39:57,120
Speaker 6: for Ken and Stevie and Isy to send me their

838
00:39:57,160 --> 00:40:01,000
Speaker 6: exact process. That would be wonderful is he doesn't share

839
00:40:01,000 --> 00:40:03,359
Speaker 6: it with you. No, even better if Medallion would send

840
00:40:03,400 --> 00:40:07,640
Speaker 6: me their exact process. But I respect it. But the

841
00:40:07,680 --> 00:40:10,879
Speaker 6: amount of individual alpha from teams that can be out

842
00:40:10,920 --> 00:40:15,239
Speaker 6: there has to all alpha's finite, but has to have

843
00:40:15,320 --> 00:40:18,239
Speaker 6: a decently tight cap. And the ones I've seen are

844
00:40:18,280 --> 00:40:20,920
Speaker 6: fairly disciplined about this, And again it varies.

845
00:40:20,960 --> 00:40:22,799
Speaker 5: I can't speak for everyone. I know.

846
00:40:22,880 --> 00:40:26,960
Speaker 6: We compete for talent with them much more than we

847
00:40:27,080 --> 00:40:30,000
Speaker 6: used to. They have non quant parts of what they do,

848
00:40:30,000 --> 00:40:32,560
Speaker 6: but they have quant parts of what they do. And

849
00:40:32,640 --> 00:40:36,160
Speaker 6: for young quants, they're often considering an offer for ake

850
00:40:36,200 --> 00:40:39,960
Speaker 6: you are and Citadel, to pick just one example. We

851
00:40:40,000 --> 00:40:42,480
Speaker 6: win our fair share, we lose our fair share. I

852
00:40:42,480 --> 00:40:44,520
Speaker 6: think we probably pay a little less in the short term,

853
00:40:44,520 --> 00:40:45,120
Speaker 6: but we don't.

854
00:40:44,960 --> 00:40:47,200
Speaker 5: Fire you if you have a bad week. So some

855
00:40:47,239 --> 00:40:51,319
Speaker 5: people are and I'm he's not doing that. I'm just

856
00:40:51,320 --> 00:40:53,880
Speaker 5: saying no. But we know that it's a more cutthroat environment.

857
00:40:53,920 --> 00:40:56,400
Speaker 4: You've done a lot of episodes on that environment. We

858
00:40:56,520 --> 00:40:59,800
Speaker 4: know like they cut pretty quickly if you lose money.

859
00:41:00,000 --> 00:41:01,279
Speaker 4: I want to go back a little bit to the

860
00:41:01,520 --> 00:41:06,200
Speaker 4: connection between sort of AI machine learning and interpretability of

861
00:41:06,239 --> 00:41:10,160
Speaker 4: the factor, you know, the quintessential. You know, the risk is,

862
00:41:10,200 --> 00:41:12,560
Speaker 4: of course, that you find something that works strictly from

863
00:41:12,640 --> 00:41:13,160
Speaker 4: data mining.

864
00:41:13,239 --> 00:41:13,520
Speaker 5: Right.

865
00:41:13,800 --> 00:41:17,359
Speaker 3: Tickers that start with B tend to rise on Tuesdays, and.

866
00:41:18,160 --> 00:41:19,440
Speaker 5: He's the CEO's middle initial.

867
00:41:19,640 --> 00:41:22,200
Speaker 4: Yeah, stuff like that we don't like. But you know,

868
00:41:22,320 --> 00:41:24,920
Speaker 4: even when you were talking about momentum and you said, well,

869
00:41:24,920 --> 00:41:28,719
Speaker 4: there's two reasons theories for why momentum can work. Let's

870
00:41:28,760 --> 00:41:31,880
Speaker 4: just go back to the canonical like quant factors. Is

871
00:41:31,920 --> 00:41:35,759
Speaker 4: there complete consensus about why these factors work?

872
00:41:36,120 --> 00:41:36,680
Speaker 5: Not at all.

873
00:41:36,840 --> 00:41:39,800
Speaker 3: Okay, they you start out with the because intuitively like

874
00:41:39,920 --> 00:41:40,600
Speaker 3: cheap stars go.

875
00:41:40,680 --> 00:41:43,520
Speaker 4: But my impression is that there's even still disagreement about why.

876
00:41:43,680 --> 00:41:46,200
Speaker 6: Yeah, you start out with the Great Divide and when

877
00:41:46,400 --> 00:41:48,920
Speaker 6: when they split the Nobel Prize between Gene Fama and

878
00:41:49,000 --> 00:41:51,880
Speaker 6: Robert Schiller, My co founder and I wrote a piece

879
00:41:51,920 --> 00:41:54,480
Speaker 6: and the cover of Institutional Investor with that title, The

880
00:41:54,520 --> 00:41:57,040
Speaker 6: Great Divide. Lars Hanson also want to share of it,

881
00:41:57,080 --> 00:41:59,960
Speaker 6: I shouldn't leave him out. But Schiller and Fama were juxtaposed.

882
00:42:00,080 --> 00:42:02,719
Speaker 6: Is the efficient market guy and the inefficient market guy,

883
00:42:02,719 --> 00:42:06,400
Speaker 6: which is fairly close to only either of them as zealots,

884
00:42:06,440 --> 00:42:07,520
Speaker 6: but it's fairly.

885
00:42:07,200 --> 00:42:08,560
Speaker 5: Close to to true.

886
00:42:08,840 --> 00:42:12,759
Speaker 6: So you start out if something has worked historically and

887
00:42:12,760 --> 00:42:15,359
Speaker 6: made a lot of money over a long period.

888
00:42:16,040 --> 00:42:17,920
Speaker 5: You start out with what you led with? Is it

889
00:42:18,000 --> 00:42:21,440
Speaker 5: data mining? Let's say you convince yourself it's not. OK.

890
00:42:21,800 --> 00:42:24,360
Speaker 6: Let's just put that away, and that's really important that

891
00:42:24,400 --> 00:42:26,480
Speaker 6: I'm not, you know, poo pooing that step.

892
00:42:26,520 --> 00:42:29,360
Speaker 5: You gotta do that. Then you got to ask yourself why.

893
00:42:29,640 --> 00:42:35,000
Speaker 6: The two main contenders are a rational gene Foma kind

894
00:42:35,000 --> 00:42:39,560
Speaker 6: of market where some stocks are riskier than others, and

895
00:42:39,640 --> 00:42:42,320
Speaker 6: whatever you're using to say go long these and short

896
00:42:42,360 --> 00:42:46,400
Speaker 6: these is loading on that risk. And if something is risky,

897
00:42:46,520 --> 00:42:50,040
Speaker 6: you should make more for investing in it. One of

898
00:42:50,040 --> 00:42:54,120
Speaker 6: the problems is identifying a There are sub fights within

899
00:42:54,160 --> 00:42:57,439
Speaker 6: these fights. What do we mean by risk? Is risk

900
00:42:57,560 --> 00:42:58,200
Speaker 6: beta like.

901
00:42:58,120 --> 00:43:01,520
Speaker 4: The capital asset prices nan academics are like every other

902
00:43:01,560 --> 00:43:03,080
Speaker 4: academic because fight.

903
00:43:04,680 --> 00:43:07,560
Speaker 6: Talmudic at this point, But is risk beta like the

904
00:43:07,560 --> 00:43:10,720
Speaker 6: capital acid pricing model? Well, the capital acid pricing model

905
00:43:10,800 --> 00:43:13,600
Speaker 6: is a beautiful, elegant model that has failed everywhere it's

906
00:43:13,640 --> 00:43:17,919
Speaker 6: ever been attempted. Is risk more multi dimensional? Is risk

907
00:43:18,000 --> 00:43:20,560
Speaker 6: something like what happens in a great depression, which is

908
00:43:20,640 --> 00:43:23,960
Speaker 6: very hard to measure. The other argument is markets are

909
00:43:23,960 --> 00:43:27,440
Speaker 6: not perfect. People make errors, and its behavioral finance. So

910
00:43:27,640 --> 00:43:29,879
Speaker 6: if a cheap stock beats an expensive stock because it's

911
00:43:29,880 --> 00:43:33,120
Speaker 6: inherently adding some risk that you can't diversify away, that's

912
00:43:33,160 --> 00:43:37,600
Speaker 6: a Gene Fom explanation. If it beats an expensive stock

913
00:43:37,680 --> 00:43:40,479
Speaker 6: because people went too far, they went past the Graham

914
00:43:40,560 --> 00:43:42,960
Speaker 6: and Dodd point, and if you can hold it, you

915
00:43:43,000 --> 00:43:45,000
Speaker 6: make money when reality sets in.

916
00:43:45,280 --> 00:43:47,960
Speaker 5: That's more than Bob Schiller point. I love Gene, He's

917
00:43:48,000 --> 00:43:48,480
Speaker 5: my hero.

918
00:43:49,080 --> 00:43:51,480
Speaker 6: I have probably drifted from seventy five to twenty five

919
00:43:51,520 --> 00:43:54,560
Speaker 6: Gene to seventy five to twenty five Bob over my career.

920
00:43:54,960 --> 00:43:57,520
Speaker 6: World's complicated. One of the hard parts is everyone wants

921
00:43:57,520 --> 00:44:00,480
Speaker 6: to win. But both explanations can be true, and they

922
00:44:00,480 --> 00:44:03,800
Speaker 6: can be true at different times. Life isn't so simple.

923
00:44:04,000 --> 00:44:06,120
Speaker 6: But those are the two biggies. But once you get

924
00:44:06,120 --> 00:44:09,600
Speaker 6: into behavioral finance, now you can fight about why what

925
00:44:09,960 --> 00:44:13,600
Speaker 6: behavioral bias? Notice you could sum up the two reasons

926
00:44:13,600 --> 00:44:19,759
Speaker 6: I gave you for momentum as underreaction and overreaction. Overreaction

927
00:44:19,920 --> 00:44:23,359
Speaker 6: is chasing right. Underreaction is the information came out, didn't

928
00:44:23,400 --> 00:44:26,200
Speaker 6: move far enough, and I hopped on that bandwagon. You

929
00:44:26,320 --> 00:44:28,600
Speaker 6: know you're in a little bit of a dodgy area

930
00:44:29,000 --> 00:44:32,680
Speaker 6: when your two best explanations sound a little like antonyms.

931
00:44:33,360 --> 00:44:36,480
Speaker 5: Now I'm being intentionally funny.

932
00:44:36,600 --> 00:44:39,040
Speaker 4: We know it works, we're just not sure whether it's

933
00:44:39,160 --> 00:44:41,719
Speaker 4: literally opposite things or intuitively opposite things.

934
00:44:41,719 --> 00:44:42,719
Speaker 3: If we know it's one of them, and.

935
00:44:42,640 --> 00:44:45,720
Speaker 6: There's some being a little too negative, you can actually

936
00:44:45,719 --> 00:44:49,200
Speaker 6: they're good papers. Teasing out the underreaction is easier to show.

937
00:44:49,280 --> 00:44:52,120
Speaker 6: You can actually show the fundamentals do catch up. I

938
00:44:52,160 --> 00:44:55,240
Speaker 6: think the overreaction probably kicks in more in bubbles where

939
00:44:55,440 --> 00:44:59,480
Speaker 6: there is just more feedback trading. Again, both explanations can

940
00:44:59,520 --> 00:45:02,640
Speaker 6: be true, and they can vary in their intensity over time.

941
00:45:03,080 --> 00:45:05,359
Speaker 6: The overreaction might have a very small part of it,

942
00:45:05,760 --> 00:45:08,440
Speaker 6: except plus a minus two years around a major bubble,

943
00:45:08,640 --> 00:45:11,759
Speaker 6: when it may be the driving force. So we have

944
00:45:11,840 --> 00:45:13,799
Speaker 6: to get really comfortable. We don't have to have the

945
00:45:13,800 --> 00:45:16,759
Speaker 6: be all, end all, single explanation. But if there are

946
00:45:16,760 --> 00:45:20,520
Speaker 6: a few good explanations and no real counter ones, and

947
00:45:20,560 --> 00:45:24,480
Speaker 6: we have really strong fifty year empirical results. Yeah, we're

948
00:45:24,520 --> 00:45:25,879
Speaker 6: okay with taking some.

949
00:45:26,080 --> 00:45:28,319
Speaker 5: Bet on this. You never want to put all your

950
00:45:28,320 --> 00:45:30,080
Speaker 5: money on one thing that's not a quant thing.

951
00:45:30,520 --> 00:45:31,960
Speaker 2: I'm going to try to connect a bunch of the

952
00:45:32,120 --> 00:45:35,279
Speaker 2: sort of decade long mega trends that we've been discussing.

953
00:45:35,360 --> 00:45:40,120
Speaker 2: But I'm thinking behavioral finance, gamification markets, big data, AI,

954
00:45:40,400 --> 00:45:45,920
Speaker 2: all those fun things combining into sports betting, which seems

955
00:45:45,920 --> 00:45:48,680
Speaker 2: to be getting bigger and bigger than ever. And the

956
00:45:48,760 --> 00:45:51,319
Speaker 2: reason I ask is because I was talking to someone

957
00:45:51,440 --> 00:45:53,000
Speaker 2: in a bar the other day and they said that

958
00:45:53,080 --> 00:45:55,440
Speaker 2: AQR was doing more sports betting stuff.

959
00:45:55,480 --> 00:45:57,959
Speaker 5: Someone in a bar was talking about AQR doing.

960
00:45:57,880 --> 00:46:00,640
Speaker 2: Sports Yeah, no, well, it probably says more about the

961
00:46:00,640 --> 00:46:02,520
Speaker 2: bars that I'm in and the people I'm talking to

962
00:46:02,640 --> 00:46:03,399
Speaker 2: than anything else.

963
00:46:03,440 --> 00:46:06,440
Speaker 6: But yeah, they mentioned I'm picturing the Star Wars canteena

964
00:46:06,560 --> 00:46:06,919
Speaker 6: right now.

965
00:46:07,160 --> 00:46:07,880
Speaker 5: I gotta tell you.

966
00:46:08,239 --> 00:46:10,839
Speaker 2: So, I'm just curious, is that a thing or are

967
00:46:10,880 --> 00:46:14,600
Speaker 2: you maybe interested in the sort of prediction market aspect

968
00:46:14,680 --> 00:46:17,759
Speaker 2: of things nowadays? Susquehanna is getting into it.

969
00:46:17,880 --> 00:46:21,400
Speaker 6: I think no one again tells you exactly what they're

970
00:46:21,719 --> 00:46:25,239
Speaker 6: they're doing my senses. They are into it and are

971
00:46:25,320 --> 00:46:29,600
Speaker 6: good at it. This is incipient for us. We are

972
00:46:29,760 --> 00:46:33,040
Speaker 6: considering and looking at it. We have a long history

973
00:46:33,320 --> 00:46:36,640
Speaker 6: Toby Moskowitz, who's a Yale professor and an AQR partner.

974
00:46:37,000 --> 00:46:38,719
Speaker 5: Great deal, by the way, you get paid for two

975
00:46:38,760 --> 00:46:40,760
Speaker 5: full time jobs that are eighty percent overlap.

976
00:46:40,880 --> 00:46:43,080
Speaker 6: I keep that in mind for the Toby's listening. Just

977
00:46:43,560 --> 00:46:46,600
Speaker 6: you know, you're a lucky man. He wrote a book

978
00:46:46,640 --> 00:46:50,759
Speaker 6: called score Casting that was all about sports. But the

979
00:46:50,840 --> 00:46:56,160
Speaker 6: sports analytics, the moneyball type stuff looks a lot like

980
00:46:56,239 --> 00:46:58,759
Speaker 6: what we do. It looks a lot like people are

981
00:46:58,760 --> 00:47:02,440
Speaker 6: not pricing this right people. I wrote my most downloaded

982
00:47:02,480 --> 00:47:05,359
Speaker 6: paper ever, which is really a little annoying because it's

983
00:47:05,400 --> 00:47:07,560
Speaker 6: like the only one I've written outside of my field,

984
00:47:08,480 --> 00:47:12,480
Speaker 6: was on when to pull the goaltender in a hockey game? Yeah,

985
00:47:12,520 --> 00:47:15,240
Speaker 6: and I wrote it with a colleague and friend, Aaron Brown.

986
00:47:15,640 --> 00:47:18,319
Speaker 6: We built a model and we came up with you

987
00:47:18,360 --> 00:47:20,200
Speaker 6: should be pulling the goalie if you're losing by one

988
00:47:20,239 --> 00:47:22,359
Speaker 6: with five or six minutes left, not with two minutes left.

989
00:47:22,360 --> 00:47:25,200
Speaker 6: And I'm pretty sure we're right. We spent a lot

990
00:47:25,200 --> 00:47:27,239
Speaker 6: of time in that paper saying why aren't they doing

991
00:47:27,320 --> 00:47:30,440
Speaker 6: it and making analogies to investing, you know, when people

992
00:47:30,520 --> 00:47:32,400
Speaker 6: know the right thing to do but can't do it

993
00:47:32,440 --> 00:47:35,520
Speaker 6: because the public opprobrium if they get it wrong will

994
00:47:35,560 --> 00:47:36,120
Speaker 6: be much worse.

995
00:47:36,400 --> 00:47:38,759
Speaker 4: Like aren't there saying like football teams should go forward

996
00:47:38,840 --> 00:47:40,400
Speaker 4: on fourth down a lot more than they do And

997
00:47:40,440 --> 00:47:41,000
Speaker 4: that's like one.

998
00:47:40,840 --> 00:47:43,480
Speaker 6: Of these things that happened and they have started to Yeah,

999
00:47:43,520 --> 00:47:46,200
Speaker 6: there is a feedback where some of these things are slow,

1000
00:47:46,280 --> 00:47:49,200
Speaker 6: some of them are fast. Baseball has largely absorbed a

1001
00:47:49,200 --> 00:47:51,520
Speaker 6: lot of these things. You know, on base percentage was

1002
00:47:51,560 --> 00:47:54,880
Speaker 6: one of the major insights of moneyball, and no nobody's

1003
00:47:54,920 --> 00:47:57,880
Speaker 6: missing that one anymore. But we still think, particularly in

1004
00:47:57,920 --> 00:48:00,960
Speaker 6: the betting markets, are not probably not as rational as

1005
00:48:01,440 --> 00:48:04,680
Speaker 6: Toby's scorecasting book. So we do think there might be

1006
00:48:04,719 --> 00:48:07,799
Speaker 6: opportunities there. But I don't want to overstate it just

1007
00:48:07,840 --> 00:48:11,360
Speaker 6: because I am cynical about online sports betting.

1008
00:48:11,480 --> 00:48:12,760
Speaker 5: I gotta tell you two things.

1009
00:48:13,400 --> 00:48:15,839
Speaker 6: I was always a very libertarian guy. I still think

1010
00:48:15,880 --> 00:48:19,120
Speaker 6: I am, but a couple things have made me less libertarian.

1011
00:48:20,080 --> 00:48:24,359
Speaker 6: Children The existence of my children will make you go, yeah,

1012
00:48:24,400 --> 00:48:26,479
Speaker 6: maybe people shouldn't be able to do anything they.

1013
00:48:26,360 --> 00:48:29,400
Speaker 5: Want when they turn twenty with rules and sports.

1014
00:48:29,040 --> 00:48:31,560
Speaker 6: Betting and maybe walking down the New York City streets

1015
00:48:31,600 --> 00:48:34,680
Speaker 6: and getting a contact high sports betting. We're already, you know,

1016
00:48:34,719 --> 00:48:37,120
Speaker 6: some of these scandals were starting to see how do

1017
00:48:37,160 --> 00:48:40,000
Speaker 6: you not have them? When you know some of these

1018
00:48:40,040 --> 00:48:41,680
Speaker 6: people make a ton of money, but they don't all

1019
00:48:41,719 --> 00:48:43,480
Speaker 6: make a ton of money, and there's a lot of

1020
00:48:43,480 --> 00:48:47,120
Speaker 6: money in sports betting. Anything you do for entertainment is

1021
00:48:47,160 --> 00:48:49,480
Speaker 6: fine if you do it entertainment size. If you go

1022
00:48:49,520 --> 00:48:51,840
Speaker 6: to Las Vegas and you go the odds are on

1023
00:48:51,880 --> 00:48:54,680
Speaker 6: the house's side. I'm gonna lose five hundred dollars over

1024
00:48:54,719 --> 00:48:58,160
Speaker 6: two days probably If I win, great, but it's gonna

1025
00:48:58,200 --> 00:48:58,600
Speaker 6: be fun.

1026
00:48:58,719 --> 00:49:01,160
Speaker 2: It's factored into the cost of a Vegas.

1027
00:49:01,200 --> 00:49:03,879
Speaker 6: But like, I have a whole bunch of twenty something

1028
00:49:04,000 --> 00:49:08,480
Speaker 6: year old mostly guys in my extended family. A lot

1029
00:49:08,520 --> 00:49:11,600
Speaker 6: of them are sports betters, and they're not even It

1030
00:49:11,600 --> 00:49:13,440
Speaker 6: even pisses me off that they're not even rooting for

1031
00:49:13,480 --> 00:49:14,400
Speaker 6: their teams anymore.

1032
00:49:14,600 --> 00:49:17,520
Speaker 5: They're kind of rooting for this player to score on

1033
00:49:17,560 --> 00:49:18,120
Speaker 5: their prop.

1034
00:49:18,280 --> 00:49:19,480
Speaker 2: It is a strange dynamic.

1035
00:49:19,640 --> 00:49:23,480
Speaker 6: Y So do I think that gamification is highly related.

1036
00:49:24,120 --> 00:49:25,680
Speaker 6: I think if you go look at the robin Hood

1037
00:49:25,719 --> 00:49:28,759
Speaker 6: app and fan duel. I think you will find they

1038
00:49:28,760 --> 00:49:32,080
Speaker 6: are far more similar on their feedback and how they

1039
00:49:32,160 --> 00:49:36,360
Speaker 6: treat things and in how their investors do on average,

1040
00:49:37,000 --> 00:49:40,920
Speaker 6: particularly sports betting. You know, investors lose on average because

1041
00:49:40,960 --> 00:49:44,720
Speaker 6: the sports betting companies make money. It's like staying on average.

1042
00:49:44,719 --> 00:49:46,480
Speaker 6: Insurance is a bad deal, you know how. I know

1043
00:49:46,640 --> 00:49:50,000
Speaker 6: because insurance companies are profitable doesn't mean it's stupid to

1044
00:49:50,040 --> 00:49:52,840
Speaker 6: do because maybe if it's in a risk you can't tolerate,

1045
00:49:53,160 --> 00:49:54,280
Speaker 6: that could be a fair trade.

1046
00:49:54,040 --> 00:49:56,160
Speaker 2: If there's a price you pay for a peace of mind,

1047
00:49:56,200 --> 00:49:58,239
Speaker 2: which is something I've come to appreciate over the course

1048
00:49:58,280 --> 00:49:58,800
Speaker 2: of ten years.

1049
00:49:58,800 --> 00:50:02,680
Speaker 6: There is zero chance that the average online sports better

1050
00:50:03,040 --> 00:50:03,719
Speaker 6: is making money.

1051
00:50:03,800 --> 00:50:06,600
Speaker 4: Yeah, I just have one last question, you know. I

1052
00:50:06,600 --> 00:50:10,680
Speaker 4: think if you looked at, like some particularly crazy times

1053
00:50:10,719 --> 00:50:13,759
Speaker 4: in markets, here's something that's changed very much since we

1054
00:50:13,800 --> 00:50:15,719
Speaker 4: started the podcast first ten years ago. I think if

1055
00:50:15,719 --> 00:50:18,240
Speaker 4: you look at some crazy times in markets, whether it's

1056
00:50:18,320 --> 00:50:20,640
Speaker 4: you know, twenty nineteen when some of these measures were

1057
00:50:20,640 --> 00:50:23,759
Speaker 4: getting extreme, we're just you know, the spack mania of

1058
00:50:23,800 --> 00:50:27,480
Speaker 4: twenty twenty one. One theory that one might have offered

1059
00:50:27,760 --> 00:50:30,400
Speaker 4: is zerp and he's like, oh, this is now we have.

1060
00:50:30,680 --> 00:50:32,960
Speaker 4: You know, we haven't been at zert for a long time,

1061
00:50:33,480 --> 00:50:37,200
Speaker 4: and yet some of this sort of speculative mania crazes

1062
00:50:37,239 --> 00:50:40,120
Speaker 4: has not gone away. How surprised are you that the

1063
00:50:40,200 --> 00:50:42,960
Speaker 4: move from zero percent to say five percent or whatever

1064
00:50:43,360 --> 00:50:46,200
Speaker 4: didn't have more of a sapping effect on some of

1065
00:50:46,239 --> 00:50:50,040
Speaker 4: the behavior and speculative fraud or just sort of animal

1066
00:50:50,080 --> 00:50:51,360
Speaker 4: spirits in these markets.

1067
00:50:51,400 --> 00:50:53,120
Speaker 5: I am mildly surprised.

1068
00:50:53,200 --> 00:50:55,839
Speaker 6: Okay, I left this out, but I actually had three

1069
00:50:56,280 --> 00:50:59,160
Speaker 6: possible reasons in my paper, and the third one was

1070
00:50:59,160 --> 00:51:01,279
Speaker 6: super low interest rates for a long time for why

1071
00:51:01,640 --> 00:51:05,600
Speaker 6: things can get crazy now. The counter argument is, once

1072
00:51:05,680 --> 00:51:09,719
Speaker 6: you break people's brains, they don't necessarily repair themselves instantly,

1073
00:51:10,160 --> 00:51:12,840
Speaker 6: So I think it was probably a contributory factor. Again,

1074
00:51:13,200 --> 00:51:16,360
Speaker 6: very hard to prove. A lot of people in nineteen

1075
00:51:16,400 --> 00:51:18,719
Speaker 6: and twenty when the spread's being cheap and expensive. We're here,

1076
00:51:18,880 --> 00:51:21,160
Speaker 6: We're saying it was a super low interest rate environment,

1077
00:51:21,480 --> 00:51:23,800
Speaker 6: and gross stocks have more cast flows in the future.

1078
00:51:24,239 --> 00:51:26,040
Speaker 6: Low interest rates means they're worth more.

1079
00:51:26,480 --> 00:51:27,600
Speaker 5: We did the math on that.

1080
00:51:27,719 --> 00:51:31,320
Speaker 6: It explained like two percent of the insuring value spread,

1081
00:51:31,640 --> 00:51:36,040
Speaker 6: and in ninety nine two thousand interest rates were quite hot,

1082
00:51:36,560 --> 00:51:40,120
Speaker 6: so it's not a unified field theory explanation. Do I

1083
00:51:40,160 --> 00:51:42,359
Speaker 6: think it helped kickstart them. And again we're in the

1084
00:51:42,400 --> 00:51:45,840
Speaker 6: soft guess work, but I think these are educated guesses

1085
00:51:46,400 --> 00:51:48,160
Speaker 6: and I listed it as one of my three. I

1086
00:51:48,160 --> 00:51:50,880
Speaker 6: think it's certainly kicked us off on some of these things,

1087
00:51:50,920 --> 00:51:52,960
Speaker 6: certainly loosen the bounds of rationality.

1088
00:51:53,040 --> 00:51:55,120
Speaker 5: Absolutely, free money, we'll do that.

1089
00:51:55,960 --> 00:51:58,239
Speaker 6: I don't think it's not human nature that they take

1090
00:51:58,280 --> 00:52:01,040
Speaker 6: away zup and everything comes back. Would I have thought

1091
00:52:01,040 --> 00:52:03,600
Speaker 6: it was a bigger effect in going back to you know,

1092
00:52:03,760 --> 00:52:05,759
Speaker 6: at one point we hit about five percent on the

1093
00:52:05,760 --> 00:52:07,040
Speaker 6: ten year almost five percent.

1094
00:52:07,320 --> 00:52:08,919
Speaker 5: Would I have thought that would have mattered more.

1095
00:52:09,560 --> 00:52:12,359
Speaker 6: Yeah, only in twenty twenty two did we see one

1096
00:52:12,440 --> 00:52:15,400
Speaker 6: ugly year over that. But it has mattered less than

1097
00:52:15,440 --> 00:52:19,279
Speaker 6: I thought. It doesn't mean it will never matter.

1098
00:52:19,520 --> 00:52:21,959
Speaker 2: All right, Cliff Astnes, thank you so much for coming

1099
00:52:21,960 --> 00:52:25,120
Speaker 2: off all the lots kicking off our ten year anniversary

1100
00:52:25,280 --> 00:52:26,439
Speaker 2: celebration again.

1101
00:52:26,560 --> 00:52:27,759
Speaker 3: Yeah, thank you so much.

1102
00:52:28,040 --> 00:52:29,160
Speaker 2: That was really a pleasure.

1103
00:52:29,960 --> 00:52:43,239
Speaker 3: Thank you, Joe.

1104
00:52:43,320 --> 00:52:44,160
Speaker 2: That was so much fun.

1105
00:52:44,239 --> 00:52:44,799
Speaker 3: It was great.

1106
00:52:45,000 --> 00:52:47,319
Speaker 2: Literally the perfect guest, literally the perfect guest.

1107
00:52:47,480 --> 00:52:47,719
Speaker 5: One.

1108
00:52:47,840 --> 00:52:50,320
Speaker 2: Well, there's so many things that stuck out from that conversation,

1109
00:52:50,440 --> 00:52:52,520
Speaker 2: but one of the things that stuck out from the conversation.

1110
00:52:52,680 --> 00:52:56,240
Speaker 2: It's this idea about the thing that flips the wisdom

1111
00:52:56,440 --> 00:52:59,040
Speaker 2: of crowds into the madness of crowds, right, And the

1112
00:52:59,120 --> 00:53:02,560
Speaker 2: idea that maybe wisdom of crowd's theory works as long

1113
00:53:02,600 --> 00:53:05,680
Speaker 2: as everyone is sort of isolated and independent and making

1114
00:53:05,680 --> 00:53:08,640
Speaker 2: their own choice off of the information available to them.

1115
00:53:09,040 --> 00:53:11,920
Speaker 2: But it starts to fall apart when everyone is tied

1116
00:53:11,960 --> 00:53:15,240
Speaker 2: to everyone else and sort of in the same social network.

1117
00:53:15,320 --> 00:53:17,239
Speaker 2: And you know, if you think about one of the

1118
00:53:17,239 --> 00:53:19,920
Speaker 2: dominant themes in markets in recent years, it has been

1119
00:53:19,920 --> 00:53:22,200
Speaker 2: people hurting into the same positions. Right.

1120
00:53:22,520 --> 00:53:24,640
Speaker 4: I found that to be really fascinating. I think it

1121
00:53:24,719 --> 00:53:27,359
Speaker 4: makes it a lot of intuitive sense. It probably can

1122
00:53:27,440 --> 00:53:30,799
Speaker 4: explain a lot of things about the world of politics,

1123
00:53:30,800 --> 00:53:32,680
Speaker 4: about the world of markets, et cetera.

1124
00:53:33,160 --> 00:53:34,920
Speaker 3: This idea that we're all just sort.

1125
00:53:34,719 --> 00:53:39,799
Speaker 4: Of one connected global village, as Marshall mccluan put.

1126
00:53:39,719 --> 00:53:41,960
Speaker 2: It, we're all just gossiping with each other all.

1127
00:53:41,800 --> 00:53:43,839
Speaker 3: The time, just constant talk, talk talking.

1128
00:53:43,880 --> 00:53:46,800
Speaker 4: No, that's a very interesting idea, and also those interesting

1129
00:53:47,400 --> 00:53:50,960
Speaker 4: the idea of length of draw down versus depth of

1130
00:53:51,040 --> 00:53:54,520
Speaker 4: draw down and the former being more painful, which strikes

1131
00:53:54,600 --> 00:53:57,359
Speaker 4: me is something I hadn't really heard anyone talk about

1132
00:53:57,360 --> 00:54:00,960
Speaker 4: that before, but especially from the perspective of a manager

1133
00:54:01,000 --> 00:54:04,400
Speaker 4: of other people's money, it's a very highly intuitive that

1134
00:54:04,480 --> 00:54:07,239
Speaker 4: makes a lot of sense to me that Okay, like, yeah,

1135
00:54:07,239 --> 00:54:09,640
Speaker 4: you had a bad quarter or whatever. Eventually you're like, oh,

1136
00:54:09,840 --> 00:54:11,640
Speaker 4: your ideas are just out of date. It's been three

1137
00:54:11,719 --> 00:54:14,040
Speaker 4: years since you've made money. Maybe time to rethink some

1138
00:54:14,080 --> 00:54:15,799
Speaker 4: of your fundamental assumptions people.

1139
00:54:17,160 --> 00:54:20,040
Speaker 2: That's one thing I've learned over the course of ten years.

1140
00:54:20,160 --> 00:54:20,440
Speaker 3: Yeah.

1141
00:54:20,480 --> 00:54:23,480
Speaker 2: The other thing was the idea of markets not necessarily

1142
00:54:23,520 --> 00:54:27,200
Speaker 2: being an arbitrage mechanism, which I think is very counterintuitive

1143
00:54:27,239 --> 00:54:29,719
Speaker 2: to the way a lot of people will think about markets,

1144
00:54:29,719 --> 00:54:32,760
Speaker 2: and this idea that like, well, sometimes you can't compress

1145
00:54:32,840 --> 00:54:35,239
Speaker 2: the price all the way to where it should be

1146
00:54:35,560 --> 00:54:39,440
Speaker 2: rationally or logically or according to EMH or whatever, because

1147
00:54:39,880 --> 00:54:42,719
Speaker 2: the reward just isn't necessarily there to get to that

1148
00:54:42,880 --> 00:54:44,360
Speaker 2: like final ten percent.

1149
00:54:44,680 --> 00:54:48,680
Speaker 4: It's interesting to think about the sort of link between

1150
00:54:49,120 --> 00:54:51,960
Speaker 4: patterns and interpretability why something works.

1151
00:54:52,280 --> 00:54:53,080
Speaker 3: And in our.

1152
00:54:52,960 --> 00:54:55,719
Speaker 4: Conversation a couple of weeks ago with Ian Dunning of

1153
00:54:55,800 --> 00:54:57,680
Speaker 4: Hudson River Trading, is like, they do not put a

1154
00:54:57,760 --> 00:55:01,759
Speaker 4: lot of emphasis on interpretability there's a pattern and some

1155
00:55:01,760 --> 00:55:04,640
Speaker 4: reasons to establish that the pattern works, it makes money.

1156
00:55:05,000 --> 00:55:07,080
Speaker 4: The idea that they then have to also come up

1157
00:55:07,120 --> 00:55:10,200
Speaker 4: with an economic story about why it works is not

1158
00:55:10,280 --> 00:55:12,799
Speaker 4: so important to them. Maybe that's because it has to

1159
00:55:12,800 --> 00:55:16,320
Speaker 4: do with time frames. Obviously, cliffs trading time frame is

1160
00:55:16,320 --> 00:55:18,400
Speaker 4: going to be very different than a high frequency trading

1161
00:55:18,440 --> 00:55:19,920
Speaker 4: firm like HRT.

1162
00:55:20,400 --> 00:55:22,880
Speaker 3: But it is interesting. And then it's interesting to think

1163
00:55:23,280 --> 00:55:24,359
Speaker 3: that even in the.

1164
00:55:24,280 --> 00:55:28,719
Speaker 4: Most established quant patterns like why do cheap stocks outperform

1165
00:55:28,760 --> 00:55:30,640
Speaker 4: more expensive stocks over the long term?

1166
00:55:30,719 --> 00:55:32,080
Speaker 3: Right, even there.

1167
00:55:31,840 --> 00:55:34,640
Speaker 4: There's dispute about why this pattern holds, even though it

1168
00:55:34,680 --> 00:55:37,839
Speaker 4: feels a little bit more intuitive. So much interesting stuff here.

1169
00:55:37,960 --> 00:55:41,080
Speaker 2: It's also I guess it sort of warms my old

1170
00:55:41,200 --> 00:55:45,080
Speaker 2: cynical heart that maybe maybe the edge for humans will

1171
00:55:45,120 --> 00:55:48,680
Speaker 2: be spotting the regime change, right, which, you know, at

1172
00:55:48,760 --> 00:55:50,839
Speaker 2: least there's something left for us to do if it's

1173
00:55:50,920 --> 00:55:54,719
Speaker 2: not just pure pattern recognition. There's that one very difficult

1174
00:55:54,719 --> 00:55:57,000
Speaker 2: to good luck to us. Yeah, all right, shall we

1175
00:55:57,080 --> 00:55:57,400
Speaker 2: leave it there.

1176
00:55:57,520 --> 00:55:58,200
Speaker 3: Let's leave it there.

1177
00:55:58,239 --> 00:56:00,520
Speaker 2: This has been another episode of the All Thoughts podcast.

1178
00:56:00,520 --> 00:56:03,560
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway.

1179
00:56:03,360 --> 00:56:06,240
Speaker 4: And I'm Jill Wisenthal. You can follow me at the Stalwart,

1180
00:56:06,360 --> 00:56:09,920
Speaker 4: follow our guest Cliff Asnest He's at Clifford Asni. Follow

1181
00:56:09,920 --> 00:56:13,160
Speaker 4: our producers Carmen Rodriguez at Carmen armand dash O Bennett

1182
00:56:13,160 --> 00:56:16,640
Speaker 4: at Dashbod and Kelbrooks at Kelbrooks. From our odd Logs content,

1183
00:56:16,680 --> 00:56:18,800
Speaker 4: go to Bloomberg dot com slash odd Lots with the

1184
00:56:18,920 --> 00:56:21,480
Speaker 4: daily newsletter and all of our episodes, and you can

1185
00:56:21,520 --> 00:56:23,600
Speaker 4: chat about all of these topics twenty four to seven

1186
00:56:23,680 --> 00:56:27,600
Speaker 4: in our discord discord dot gg slash od Lots And.

1187
00:56:27,640 --> 00:56:30,000
Speaker 2: If you enjoy odd Lots, if you like it when

1188
00:56:30,040 --> 00:56:32,319
Speaker 2: we speak to guys like Cliff Asnaes, then please leave

1189
00:56:32,400 --> 00:56:35,720
Speaker 2: us a positive review on your favorite podcast platform. And remember,

1190
00:56:35,800 --> 00:56:38,440
Speaker 2: if you are a Bloomberg subscriber, you can listen to

1191
00:56:38,560 --> 00:56:41,120
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1192
00:56:41,200 --> 00:56:43,720
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1193
00:56:43,719 --> 00:57:02,920
Speaker 2: and follow the instructions there. Thanks for listening in