Speaker 1: Bloomberg Audio Studios, podcasts, radio news.
Speaker 2: I was hoping to be like one of those like
Speaker 2: clips on TikTok you see.
Speaker 1: Fake that's the thing you look like you're on a
Speaker 1: podcast pads on home.
Speaker 2: Now it just looks like, you know, instead.
Speaker 3: Of we just randomly got together the chat and microphones
Speaker 3: part of it with Bloomberg Podcasts behind us.
Speaker 1: Yeah. Right, I guess we've conveyed podcasts efficiently with all
Speaker 1: of the Bloomberg podcasts.
Speaker 2: Joe.
Speaker 4: Part of the reason this has to be on video
Speaker 4: is because Matt shaved. Matt has had a beard for
Speaker 4: the past I don't know that.
Speaker 1: I've had a winter beard from like Christmas break through memorild.
Speaker 3: That I shaved over COVID for the first time in
Speaker 3: about thirty years and my kids freaked out.
Speaker 1: Yeah, they were like, you're an alien. Yeah, my kids
Speaker 1: didn't care that much. But one of my sons said,
Speaker 1: it's a different daddy.
Speaker 3: Well, you also have hair on the top of your head.
Speaker 3: When you shave the beard and you don't have any hair,
Speaker 3: Suddenly you're mister clean.
Speaker 1: You just does mister clean?
Speaker 2: No, I don't think so, mister clean. You know clean?
Speaker 3: Come on, I think that's a fair point.
Speaker 1: It's the Money Stuff podcast. We have a guest, Cliff
Speaker 1: has runs AQR. Thanks for coming in, Thanks for having me.
Speaker 1: I always like to ask how to my managers, like
Speaker 1: what do you do for a living? Like what is
Speaker 1: it like economic function of like the business that you run?
Speaker 3: Okay, those are to me slightly different questions. Right.
Speaker 1: One sounds like it's about you. One sounds like it's
Speaker 1: about AQR.
Speaker 3: I'm more interested in even if it's about a q R.
Speaker 3: What you do for a living is And people might
Speaker 3: not like this phraseology, but you're trying to predict what
Speaker 3: happens to securities. You're trying to buy ones that go
Speaker 3: up either in the absolute or more than some benchmark,
Speaker 3: and and sell ones that do the opposite. The broader question,
Speaker 3: which I think is behind what you're saying, is what
Speaker 3: do you do for the world by doing that? And
Speaker 3: they overlap, but they're not exactly the same thing. Something
Speaker 3: can have a net positive effect on the world even
Speaker 3: if you're not waking up. And I'll admit this, I
Speaker 3: don't think most people in their jobs waked up and
Speaker 3: always think that way, and certainly not active managers just
Speaker 3: go I am just making the world a better place.
Speaker 3: They're thinking, is in Nvidia undervalued? Is it overvalued? The
Speaker 3: things that I think you do for the world is
Speaker 3: first take the other side of positions other people disagree
Speaker 3: with you on or don't want to bear. That can
Speaker 3: take two forms. That can mean one of you is
Speaker 3: biased and wrong. You hope it's not you. But in
Speaker 3: that case, what you do for the world is you
Speaker 3: move prices back towards not necessarily all the way too
Speaker 3: in the abstract, the correct price. That's hard to define, actually,
Speaker 3: but something is mispriced and you take the other side
Speaker 3: of that. It could be a risk premium. Other people
Speaker 3: don't want to bear in a lot of strategies necessarily
Speaker 3: that they're making an error. If a merger's announced and
Speaker 3: three quarters of the pop you should get if it
Speaker 3: closes happens, a lot of people might not want to
Speaker 3: stick around for that last quarter. And if you're willing
Speaker 3: to take the other side of that, maybe you get
Speaker 3: paid a little for doing that, and that is a
Speaker 3: service to the market. People want to get out and
Speaker 3: you're helping. That's kind of the positive side. But again
Speaker 3: it's not what you're thinking about when you do the trade.
Speaker 3: There are positions active managers will take that are not
Speaker 3: about that. Let me put it this way. If what
Speaker 3: you're trying to do is predict returns, you can predict
Speaker 3: returns because the price is moving towards truth. But you
Speaker 3: can also make money if you predict the price moves
Speaker 3: further away from truth. You know, if you're a momentum
Speaker 3: memestock investor. And doesn't mean you can't get that right,
Speaker 3: you know. I think of that a little more as
Speaker 3: trading than investing. But they all come together, and it
Speaker 3: even gets complicated within some famous quantitative factors. One famous
Speaker 3: quant factor is the momentum factor.
Speaker 1: Asked the finance professor, I should I have asked? And
Speaker 1: he said, you should ask him if momentum trading makes
Speaker 1: markets more or less efficient.
Speaker 3: We don't fully know, but I can tell you the framework.
Speaker 3: There are two competing explanations in academia and a general
Speaker 3: world that cares about these things. For why momentum on
Speaker 3: average works, there's always a third that it will never
Speaker 3: work again and it was just random and it was
Speaker 3: just luck. But if it's true, why does it work?
Speaker 3: I actually think these can both coexist, so it's not
Speaker 3: truly embarrassing, but it sounds embarrassing that the two major
Speaker 3: explanations one is under reaction and the other is over reaction.
Speaker 3: When you've narrowed it down to two things that at
Speaker 3: least feel like the opposites, you should feel a little shame.
Speaker 1: For a second, there's the inituation, just like there's a
Speaker 1: correct price, and momentum is trading from below to above the.
Speaker 3: Credit friends, Well, I'll give you two scenarios. Information comes
Speaker 3: out and people have a behavioral bias that behavioral psychologists
Speaker 3: would call anchoring an adjustment. They move towards all the
Speaker 3: way to the new information, and I think that fits
Speaker 3: a lot of intuition in the short run that can
Speaker 3: make momentum work. If you're following fundamentals or prices, good
Speaker 3: news comes out or the price moves. If good news
Speaker 3: comes out, on average, the price goes up, but not enough.
Speaker 3: If the price moves, it may on average be responding
Speaker 3: to good news, and simply by observing the price move,
Speaker 3: you can say, Okay, sometimes it's wrong, but on average
Speaker 3: it doesn't quite move enough. If that's the reason, and
Speaker 3: I think the weight of the opinion in academia I
Speaker 3: believe is towards this underreaction explanation. Then, even though you're
Speaker 3: trading on momentum, you're still moving the price towards kind
Speaker 3: of truth or equilibrium or something. But the flip side
Speaker 3: is overreaction. Do you think of that more as just
Speaker 3: your classic positive feedback loop. Someone's buying something just for
Speaker 3: you know, fomo. It's been going up in there, and
Speaker 3: well that could be a negative reason, or they're just
Speaker 3: predicting more people buy it because of fomo. In that sense,
Speaker 3: if you buy some weird meme coin, you could do
Speaker 3: that for a rational reason, not that you are a
Speaker 3: long term holder, but you just believe it's gonna keep going.
Speaker 1: So did you make money on GameStop?
Speaker 3: This the God's honest truth. You won't believe me. I
Speaker 3: have no idea if we were long or short game
Speaker 3: stop during the whole thing.
Speaker 2: You never went back and check.
Speaker 3: No, I never did. I did have an episode where
Speaker 3: I mentioned on perhaps a different TV network that we
Speaker 3: were short, AMC.
Speaker 2: What does your Twitter look like after that?
Speaker 3: It was very ugly. Yeah, I certainly knew of that world,
Speaker 3: even though we're quants. I watch the markets all day,
Speaker 3: even even if I don't do anything about it. It's
Speaker 3: like the old joke about the weather. Everyone talks about it,
Speaker 3: but nobody does anything about it.
Speaker 2: Best thing to talk about.
Speaker 3: So it's not like everything that went on with GameStop
Speaker 3: Melvin Capitol. You know, I'm watching it every day, but
Speaker 3: we take relevant tiny positions in every stock. There was
Speaker 3: nothing weird in our P and L. And yes, I
Speaker 3: was not even curious. It probably wasn't even in our
Speaker 3: universe at that point of things. We trade. But then
Speaker 3: I'm going on this other network. It's allowed, and in
Speaker 3: kind of a pre call of what are we going
Speaker 3: to talk about? You guys know, you don't want to
Speaker 3: get on there and have absolutely nothing to talk about.
Speaker 3: You want to have some not necessarily the answers worked out,
Speaker 3: but agreed upon topics. They're like everyone in this segment
Speaker 3: gives us some lungs and shorts. But I'm saying, as
Speaker 3: a quant that's kind of silly. They're not indicative, and
Speaker 3: we kind of made a deal where they'd let me
Speaker 3: briefly explain that doesn't make a whole lot of sense
Speaker 3: for quantum, but it might be fun. And my way
Speaker 3: of saying it was, if I give you a few
Speaker 3: names long and a few names short, you could look
Speaker 3: in six months later and think we had a fantastic
Speaker 3: year or a terrible year and be terribly wrong in
Speaker 3: either direction because they're tiny. So I went through an
Speaker 3: AMC was I think I'm accidentally doing it again. We'll
Speaker 3: keep going hopefully they don't listen to you, guys. But
Speaker 3: it was bad on every single thing in our model practically,
Speaker 3: which is hard to do. It was expensive, unprofitable, high beta.
Speaker 3: They were issuing shares, not buying backshit. There are more examples,
Speaker 3: and so I said that, but then I added that
Speaker 3: were only short twelve basis points, so the crazy people
Speaker 3: could be right and it doesn't really matter. I discovered
Speaker 3: two things. They're not going to like a short period,
Speaker 3: and crazy people don't always like being called crazy. Yeah,
Speaker 3: I had to discover that from myself. So my Twitter
Speaker 3: got ugly for a while. You may have noticed. I've
Speaker 3: gotten I think a fair amount better at this, but
Speaker 3: I used to be pretty bad about responding to ugly,
Speaker 3: which you learn your lesson on that.
Speaker 2: You always feed the trolls less so than.
Speaker 1: I used to.
Speaker 3: At least I think I'm better. Maybe I'm wrong, but
Speaker 3: I became public enemy number three to the meme stock
Speaker 3: crowd for a while, I did not really. Yes, Kenny
Speaker 3: Ken Griffin was number one. I don't believe Ken did this,
Speaker 3: but it's the whole poll, the Bible thing. And oddly enough,
Speaker 3: Gary Gensler was clearly number two, Yeah, because they thought
Speaker 3: he was covering for the manipulators and the naked shorts
Speaker 3: and whatnot. I've met both, I know, and a little
Speaker 3: better than Gary. I don't think there are any two
Speaker 3: people on Earth less likely to be in cahoots than
Speaker 3: those two. I think they're on the opposite side of
Speaker 3: most issues. But that was the theory. But both Ken
Speaker 3: and Gary are too smart to respond to them on Twitter,
Speaker 3: so I certainly became the most actively engaged. And then
Speaker 3: I never did that again before today, when I've accidentally
Speaker 3: done it.
Speaker 4: Before we get back to stuff that matters, can I
Speaker 4: just say on AMC, I tweeted, when did June two
Speaker 4: come out?
Speaker 2: It was like last summer.
Speaker 3: Yeah, year ago.
Speaker 4: I tweeted that I fell asleep during Dune two, and
Speaker 4: it reawakened that crowd, at least on my Twitter, because
Speaker 4: I've the same.
Speaker 3: Crowds crowd because he's dissing a movie, don't you understand
Speaker 3: you have to like every movie or else. You're anti America.
Speaker 4: And then there were a lot of conspiracy theories about
Speaker 4: Bloomberg reporter lies about falling asleep in doom too because
Speaker 4: she hates AMC.
Speaker 2: It would be, but it wasn't.
Speaker 3: Oh yeah, you do innocuous that I was obnoxious, so
Speaker 3: I'd kind of deserved it. You didn't deserve Thank you
Speaker 3: for saying that you didn't deserve it.
Speaker 1: I haven't sent. That's not doing what I was good.
Speaker 2: That's all too. It's pretty good.
Speaker 3: No you didn't, Actually I didn't. You were asleep.
Speaker 2: Well no, not the whole time, not the whole.
Speaker 1: Time, but also like you and and they were like,
Speaker 1: give me some shorts, and you gave them some lungs
Speaker 1: and shorts. Would you have known that or did you
Speaker 1: have to be like I got it.
Speaker 3: I would not have known that, right, So you don't know,
Speaker 3: you're ar It would have been better for me if
Speaker 3: we didn't have the call, because then I could have
Speaker 3: just said I don't know. I rarely know individual stocks,
Speaker 3: and if I do know, I'm probably not happy I know.
Speaker 3: And even then, it's like we lost twenty BIPs on
Speaker 3: that today, which is a giant number for us to
Speaker 3: lose on one stock, and even then I probably don't notice.
Speaker 1: Verse twenty bit. If someone comes to you and says
Speaker 1: that's not.
Speaker 3: One, I might be told about it. For us, it's
Speaker 3: whether these seven hundred fifty stocks be these seven hundred
Speaker 3: and fifty stocks. Yeah, I don't memorize fifteen hundred stocks. Now,
Speaker 3: we take a fair amount of risks, some funds more,
Speaker 3: some funds designed to be less. And that's about the
Speaker 3: size of these two positions. So when I say small,
Speaker 3: I'm not saying we're not both taking risk and trying
Speaker 3: to generate pretty decent returns. But if you just think
Speaker 3: about it, a quant is playing the odds. They're saying,
Speaker 3: affirm a company with these characteristics. And this can be
Speaker 3: old school factor quants from the nineteen nineties, these can
Speaker 3: be modern machine learning. But with these characteristics tend to
Speaker 3: beat these characteristics. If that's all you know, and it
Speaker 3: is all we know, why on earth would you take
Speaker 3: a lot of risk in any one company. AMC really
Speaker 3: could have done well. It could have been bad on
Speaker 3: every single thing that on average doesn't work, and it
Speaker 3: could be a special situation that we don't understand. Something
Speaker 3: could be good on everything and the CEO can embezzle
Speaker 3: all the money. We don't want to take a lot
Speaker 3: of risk on any one thing because we have no
Speaker 3: insight in that it's risk for no return.
Speaker 1: What thing you've written is that like over time, the
Speaker 1: quant like factor model has moved closer to being what
Speaker 1: Gramm and dot investors do. Like are you like an
Speaker 1: abstract like Metagram and Dada investors? I like the way
Speaker 1: to think of what you do.
Speaker 3: I think it's moved closer. There are still differences and
Speaker 3: maybe some of the like momentum if it's overreaction, if
Speaker 3: you're riding momentum. I don't think a Graham and DoD
Speaker 3: manager does that. So I don't want to push the analogy.
Speaker 3: But this came out very very early in my career.
Speaker 3: This is like thirty years ago. This is like my
Speaker 3: Goldman Sax days. I started hearing a lot of active
Speaker 3: stock pickers, some I'm still still friends with one guy
Speaker 3: in particular. I was laughing at them, and I was
Speaker 3: telling my friend, you all say the same thing. You
Speaker 3: all say you're looking for valuation plus a catalyst. It's
Speaker 3: like a I don't know if everyone says it, but
Speaker 3: I've heard it many times and I'm making fun of him,
Speaker 3: and at some point he looks at me and goes,
Speaker 3: you do value on momentum, and it was a gotcha.
Speaker 3: He won that round because I'm like, okay, I see
Speaker 3: your point. So even back then, you can think of
Speaker 3: those two together. We literally add them up. But if
Speaker 3: you think of him as this holistic system, we're looking
Speaker 3: for cheap things that are starting to get better in
Speaker 3: price or fundamentals over time. And now I'm I'm not
Speaker 3: talking the really modern stuff, alternative data, machine learning. I'm
Speaker 3: talking just classic quant stuff that's been academia and then
Speaker 3: imports over to applied. Profitability is a factor Robert nov
Speaker 3: Mark's fort a great paper that we all incorporated, where
Speaker 3: also equal give invaluation and momentum. If a company is
Speaker 3: more profitable on some famous scale's gross profitability ROA Roe,
Speaker 3: that's some degree a positive low beta investing. Two of
Speaker 3: my colleagues, Andrea Ferzini and los A. Peterson, resurrecting stuff
Speaker 3: Fisher Black did, and they're very good about saying Fisher
Speaker 3: did it first. That lower beta stocks. If you're famously
Speaker 3: a capital asset pricing model person, they're supposed to sell
Speaker 3: on average underperform higher beta stocks. That's the main output
Speaker 3: of that model. It doesn't work. I mean, it's one
Speaker 3: of the largest empirical failures ever. It doesn't work in
Speaker 3: any kind even outside of stocks people tested in other places. Therefore,
Speaker 3: it kind of makes low beta stocks a little bit
Speaker 3: of a free lunch because they are lower risk and
Speaker 3: they keep up. If you actually go read Graham and DoD,
Speaker 3: they're not just buying low multiples. They're much more holistic
Speaker 3: than that. You know, high quality companies that have a moat,
Speaker 3: that have some kind of margin of safety I think
Speaker 3: was the term they use. Margin of safety and looking
Speaker 3: for low risk doesn't sound so so different. So over
Speaker 3: time I've thought at least a core amount of what
Speaker 3: quants and academics, if you take them as a whole,
Speaker 3: are finding, is the full paneplyy of stuff that a
Speaker 3: Graham and DoD investor. We do it very differently. Again,
Speaker 3: we're betting on the concept working on average. They are
Speaker 3: using it in a soft or a hard sense as
Speaker 3: a screen to look for candidates, and then they're trying
Speaker 3: to learn a lot about that situation. They're upside as
Speaker 3: if they learn a lot about that situation, they could
Speaker 3: be more reliable than my You know, hey, we could
Speaker 3: be wrong about AMC, and their downside is they better
Speaker 3: be right. The concept can work, and they can still
Speaker 3: lose if they're wrong about the specific So over time
Speaker 3: I've gotten a little less hubrious about this. I think
Speaker 3: quants caused the problem, by the way. I think when
Speaker 3: Gene fam and Ken French started looking at like price
Speaker 3: to book in the late eighties and early nineties, and
Speaker 3: there were other people who did it too, I'm just
Speaker 3: Chicago guy, so I'm going to just go with Fama
Speaker 3: and French. They did it best. In my very biased opinion.
Speaker 3: I don't think i'd have to go back and check,
Speaker 3: but I don't think the first few papers use the
Speaker 3: word value investing. Over time, low multiple investing in the
Speaker 3: quant world came to be called value investing, and in
Speaker 3: the Gramm and Dodd world they get kind of mad
Speaker 3: at that, and they'd be like, it's not value investing.
Speaker 3: There are plenty of low multiple companies that deserve to
Speaker 3: be low multiple, and there are plenty of high multiple
Speaker 3: companies that deserve it, and I think over time, the
Speaker 3: quantitative process agreed with them more and more. So I
Speaker 3: still think it's a communication problem because they'll still talk
Speaker 3: about the value factor. In the quant world, that's just
Speaker 3: low multiples. And if a Gramm and Dodd gets mad
Speaker 3: or any old school active stock picker gets mad at that,
Speaker 3: I'll just say, you're right, because value implies a more
Speaker 3: holistic thing.
Speaker 1: But isn't like modern quantit and what you're doing now
Speaker 1: kind of just that more holistic thing, like you're just
Speaker 1: ingesting more data plants and you have a less linear model,
Speaker 1: and it's like moving towards that.
Speaker 3: Anyway, if you look at machine learning, where either to
Speaker 3: construct factors. One of the best uses of mL we
Speaker 3: found is a subset of mL called natural language processing,
Speaker 3: where you take textual data and you try to say,
Speaker 3: is this good news or bad news? Quants have kind
Speaker 3: of done this forever. You get transcript of an earnings call.
Speaker 3: And the old school way to do this for a
Speaker 3: long time was you count up good words and bad words,
Speaker 3: good phrases and bad phrases, so increasing plus one and
Speaker 3: you tech parse the whole thing, and sure, you guys
Speaker 3: immediately see the problem. If the actual sentence was massive
Speaker 3: embezzlement is increasing, then you were off on that plus one.
Speaker 3: Quantitative stuff can survive doing some horrifically stupid things in isolation,
Speaker 3: If fifty three percent of the time increasing is a
Speaker 3: good word, than forty percent of the time you were stupid,
Speaker 3: turns out natural language processing or NLP, if we want
Speaker 3: to sound like the cool kids. That is, taking that
Speaker 3: same data and training a mL model to say what
Speaker 3: predicts and what doesn't predict, and of course never gets
Speaker 3: near perfect at the end of the day, though we
Speaker 3: believe it does a lot better than the word count
Speaker 3: methods and is additive to a model. But importantly, and
Speaker 3: I think this was your point, Matt, it's not qualitatively different.
Speaker 3: We've looked at both price and fundamental momentum forever. Fundamental
Speaker 3: momentum the classic measures of things like our earnings being
Speaker 3: revised and you want the revision. You want the new
Speaker 3: news up faster or slower. Our earning surprise is coming
Speaker 3: in positive or negative. And this is this anchoring and
Speaker 3: adjustment idea that if that's good or bad news, you
Speaker 3: can make money trading on it. Because it's not fully
Speaker 3: incorporated in the stock price parsing and earnings call poorly
Speaker 3: as in the past or better now, we think of
Speaker 3: it as just another form of our good things happening
Speaker 3: in the new year term, and if so, they're probably
Speaker 3: under appreciated. So yes, I don't think it's changed dramatically.
Speaker 3: It's much more of an evolution rather than a spirit change.
Speaker 3: But we got bigger tools in the tool chest now.
Speaker 1: I mean, like, you know, you ask, like what a
Speaker 1: sort of like traditional old school fundamental manager would do.
Speaker 1: One thing they do is listen to their next call
Speaker 1: and like talk to management. So it's like your machines
Speaker 1: are moving in the direction of being an old school analyst.
Speaker 3: Yeah, as someone with four kids in or around college age,
Speaker 3: my wife and I and she's done a lot more
Speaker 3: of this, has spent a lot of time trying to
Speaker 3: figure out what careers will not be utterly destroyed by MLO,
Speaker 3: and it's not an easy question. A podcaster is probably
Speaker 3: a good one for a while. They have those fakes,
Speaker 3: so it will all be podcasters. But yeah, it's another example.
Speaker 3: I always say, these useless statements like them sufficiently long horizon,
Speaker 3: we're all replaced by machine learning. It's a question of
Speaker 3: what we're getting way too philosophical, but the transition to
Speaker 3: that can be very painful and weird. But a world
Speaker 3: of abundance and leisure. Maybe our horrible fate in the
Speaker 3: long term, I'll take it.
Speaker 5: Yeah.
Speaker 1: I want to talk about market timing because I was
Speaker 1: reading the Virtue of Complexity paper from Brian Kelly at All.
Speaker 3: Which is I will say he may be one of
Speaker 3: the only hosts of a podcast to read that paper.
Speaker 3: This is not a simple paper. It's like he writes,
Speaker 3: very clue.
Speaker 1: It's the sort of like it's the notion of taking
Speaker 1: like a sort of simple factor model and blowing it
Speaker 1: up into like a non linear AI model, and one
Speaker 1: almost throwaway sentence in the pieces like this, like simplified
Speaker 1: AI model that he built for like illustrative purposes, lowered
Speaker 1: its risk before fourteen of the last fifteen recessions. And
Speaker 1: I always thought the naive like the best way to
Speaker 1: invest would be just market timing, just like you know,
Speaker 1: have all your money in the stock market before the
Speaker 1: market goes up and not before it goes down, if
Speaker 1: you can do it. My impression is that like respectable
Speaker 1: headgsplot managers, respectable clime managers, respectable academics say the hardest
Speaker 1: thing in the world is market timing, and like no
Speaker 1: one claims to get alpha from at and it's not
Speaker 1: a thing. Is that changed, and is that changed due
Speaker 1: to like machine learning?
Speaker 3: It has changed a big Do we do trend following
Speaker 3: on macro assets old school CTA stuff. We think we've
Speaker 3: made a new school by incorporating fundamental momentum by doing
Speaker 3: a lot of more esoteric market so we think even
Speaker 3: that's had a march of progress to it, But all
Speaker 3: else equal. I wrote my dissertation on momentum in individual
Speaker 3: stocks for some reason that I cannot explain. If you're
Speaker 3: using past returns to predict the future, just what's going
Speaker 3: up will keep going up, and vice versa to pick
Speaker 3: individual stocks. The whole industry calls momentum. And if you
Speaker 3: think markets tend to keep going in the same direction,
Speaker 3: everyone calls it trend falling. Same thing. Starting in I
Speaker 3: think two thousand and eight, we started. We always had
Speaker 3: it in our macro models, but we started formally offering
Speaker 3: separate trend falling products. It doesn't take hero bets on
Speaker 3: anyone market. It's not Gazarelli selling all the stocks a
Speaker 3: minute before October nineteenth of eighty seven. I'm dating myself.
Speaker 3: My wife's birthday is October nineteenth, which always always gets
Speaker 3: a little amused. Man crash eighty seven meant did you
Speaker 3: really crashed them together? Yeah, there have been some jokes
Speaker 3: over over time. So trend following it's essentially market timing,
Speaker 3: but it's highly diverse, many small bets. Whatever's been happening
Speaker 3: tends to keep happening. That's the main way we'll do
Speaker 3: market timing.
Speaker 1: You won't to like your main equity fun is like
Speaker 1: sometimes that.
Speaker 3: That was kind of a toy model to illustrate a point.
Speaker 3: I know we're not taking a lot of risks on
Speaker 3: that model. Market timing I still think is quite hard.
Speaker 3: Might there be advances in it in the future, absolutely,
Speaker 3: but are we taking significant risk in it now aside
Speaker 3: from trend following, not really.
Speaker 1: You probably sit been more papers to financial journals than
Speaker 1: the average like asset.
Speaker 3: Manager, by like one hundred percent.
Speaker 1: I want to ask two questions. One is like, I
Speaker 1: want to learn about your relationship with academia, because I
Speaker 1: think it is fascinating that like you employ half of
Speaker 1: the l faculty the finance PhD pipeline runs mainly to
Speaker 1: AQR and then too because I think of like what
Speaker 1: HEGEMA manager does as sort of like finding anomalies, finding
Speaker 1: like market inefficiencies, finding factors that are predictive every turns,
Speaker 1: and I think of what a finance academic does is
Speaker 1: mainly also that when do you publish and when do
Speaker 1: you just trade on it?
Speaker 3: Oh, there's so much to talk about here. First, there
Speaker 3: are a lot of reasons we do it. One is
Speaker 3: just personal consumption. We grew up in this world. We
Speaker 3: liked being part of it. We were interested in this
Speaker 3: stuff in a purely academic sense before we got seduced
Speaker 3: into making money.
Speaker 1: I mean, I would like pause it that you think
Speaker 1: there is a value to employing the finance PhDs to
Speaker 1: like build your models, and the way to attract fancy
Speaker 1: finance PhDs is to offer them the most academia like
Speaker 1: possible working.
Speaker 3: And to letting them publish. There are people, including our
Speaker 3: two Yale professors. I don't don't know if would have
Speaker 3: come take you are if we said you can never
Speaker 3: write about this. We do a crass calculation, though, if
Speaker 3: we think there is something that we are reletively unique
Speaker 3: on entirely unique, or a very small handful of people
Speaker 3: know this, we won't publish it. The optimal the optimal
Speaker 3: time to publish a paper is after you've made money
Speaker 3: from something for eleven years and an hour and a
Speaker 3: half before someone else is going to publish the paper,
Speaker 3: and we cannot get that right. I can think of
Speaker 3: one example of momentum in factors, the fact that factors themselves,
Speaker 3: like value, profitability, also exhibit momentum as something that we've
Speaker 3: traded on for many years that we've always refrained from
Speaker 3: writing a paper on because no one else has. And again,
Speaker 3: we knew we couldn't be the only people who do this,
Speaker 3: but we didn't think the cat was out of the bag.
Speaker 3: And then someone else wrote the paper. And I'm sure
Speaker 3: we've done this to other people too, because you never
Speaker 3: know what they're doing internally. So life, you know, you
Speaker 3: do it long enough, life works out kind of fair,
Speaker 3: but that is the goal.
Speaker 1: But you were mad there because, like as academics, you
Speaker 1: wanted credit for that paper.
Speaker 3: It's fun. You could call it childish, but it's human.
Speaker 3: It's it's fun to discover something.
Speaker 1: Well, you also mad because publishing that reduces the value
Speaker 1: of the signal or maybe a.
Speaker 3: Little bit, maybe a little bit. So far, it's still
Speaker 3: worked wonderfully. The trend is still your friend when it
Speaker 3: comes to two factors, but it would be a fireable
Speaker 3: offense for make you are to publish something that we
Speaker 3: thought was truly proprietary. Let me give you my favorite
Speaker 3: example of this, because it came up recently. One of
Speaker 3: the things that we've gotten into in a fairly big way,
Speaker 3: as have some other quants, is what's called alternative data.
Speaker 3: Those are new data sets that put people put together
Speaker 3: with sweat equity. The classic example and the only one.
Speaker 3: And this is the point that I'm allowed to talk
Speaker 3: about our credit card receiver data were a credit cards
Speaker 3: because that's been discussed a million times.
Speaker 1: Alternative there.
Speaker 3: Yeah, yeah, Well that's the point is this stuff is arbitrageble.
Speaker 3: I mean, it can go away. Value, be it the
Speaker 3: narrow Quan sense of value or the more broad gram
Speaker 3: and Odd sense of value, is trying to take advantage
Speaker 3: of I think basic human nature. I think spreads being
Speaker 3: cheap and expensive are wider still than the historical average,
Speaker 3: not tighter. I don't think there's a lot of evidence
Speaker 3: that so much capital is in that that it's making
Speaker 3: you go away. If you get a short term information
Speaker 3: advantage because you have put the time in or paid
Speaker 3: someone who's put the time in to create a new
Speaker 3: data set that other people don't have. They're going to
Speaker 3: have it eventually. So I am talking to the Australian
Speaker 3: Financial Review. I don't even know if we had discussed
Speaker 3: alternative data beforehand, but he asked me about it, and
Speaker 3: I said to him that our head of stock selection,
Speaker 3: a fellow named Andrea Ferzini, has asked me, I might
Speaker 3: have said, told me, but has asked me not to
Speaker 3: talk about these things. There are things I'll talk about,
Speaker 3: but there are things we think are you know, overused word,
Speaker 3: but our true alpha that the world doesn't know, and
Speaker 3: I think it's reasonable, So I really can't talk about him.
Speaker 3: He writes the article and what it says is mostly that,
Speaker 3: but instead of saying he's asked me not to talk
Speaker 3: about it, it says Andrea Ferzini won't tell me what
Speaker 3: we're doing in alternative data, which it has a slightly
Speaker 3: different connotation, and that's a connotation of adult old man,
Speaker 3: don't worry about it. We're doing some stuff here. So
Speaker 3: again it's an example where there certainly are things we
Speaker 3: won't talk about.
Speaker 1: Is that ever true? Are you like most cutting edge
Speaker 1: machine learning people like that don't or are that or
Speaker 1: do you read all the papers?
Speaker 3: I read most of the papers. I don't read all
Speaker 3: of the papers. I at least skim all the papers.
Speaker 3: I know the gist. We do a lot of different
Speaker 3: things at this point. Once you're a quant you want
Speaker 3: more factors, you want to trade it in more places.
Speaker 3: And I will admit that the week doesn't go by
Speaker 3: where I don't and ask someone for a review of
Speaker 3: what we're doing. Somewhere at some point I approved it,
Speaker 3: so Pastcliff had some understanding. Yeah, but also just you know,
Speaker 3: some of this is pretty decent math, and the old
Speaker 3: mathematicians do their best work in their twenties. I can
Speaker 3: tell you I'm probably still decent in math, but I'm
Speaker 3: not what I was when I was twenty two. Wisdom
Speaker 3: has hopefully replaced some of mathematical ability.
Speaker 1: Because the personnel at AQR different from a like I
Speaker 1: don't know who you think of as like quantity competitors,
Speaker 1: but like, my impression is that like you have many
Speaker 1: more finance PhDs than like other places that might have
Speaker 1: more like pure math people or math undergrads or something like.
Speaker 3: Yeah, I think there's some truth to that.
Speaker 1: What makes them better or worse?
Speaker 3: First, I think there's going to be a correlation that
Speaker 3: the closer you get to the high frequency world, the
Speaker 3: more you're going to be more in the pure math realm.
Speaker 3: My very tortured analogy is quantum mechanics versus Newtonian physics.
Speaker 3: When you get into the high frequency world, first you
Speaker 3: have a ton of data. By nature, you just have
Speaker 3: a lot more instances and a lot less theory. So
Speaker 3: turning yourself over to the data more so than having
Speaker 3: an economic rationale is far more rational, and that becomes
Speaker 3: a more mathematical exercise. We tend to think in our
Speaker 3: long seven hundred short seven hundred stock portfolios, average holding
Speaker 3: period is maybe nine months, maybe closer a year. Times.
Speaker 3: That's nowhere near high frequency.
Speaker 2: Yeah, high frequent.
Speaker 3: Two. When I wrote my dissertation and I did have
Speaker 3: this in there was a monthly contrarian strategy. Now we're talking,
Speaker 3: you know, sub seconds kind of thing. I think when
Speaker 3: you're gonna have a medium holding period strategy, as I
Speaker 3: think of us, we're not Warren Buffett, but we're not HFT.
Speaker 3: Even the machine learning stuff needs some economics too. You
Speaker 3: simply don't have enough data and there's too much of
Speaker 3: a dimensionality. Even with Brian's virtue of complexity, you need
Speaker 3: to give it some structure or else it's going to
Speaker 3: overfit and go mad. So I think I think that's the reason,
Speaker 3: not saying we'll never do something in a higher frequency world,
Speaker 3: in which case we'd probably have to shift more. But
Speaker 3: in our world, being a mathematician, being an excellent programmer,
Speaker 3: but also having the economics behind it is kind of
Speaker 3: what we're looking for.
Speaker 1: I think of like renaissances famously employing exclusively people who
Speaker 1: have never thought about markets or financer economics, and they
Speaker 1: come to it pure, and I feel that you are
Speaker 1: very much like people who have math shops, but like
Speaker 1: have economic intuition and think about it as an economy.
Speaker 3: I think that's accurate. I do have a couple of
Speaker 3: Renaissance observations. Okay. One of my favorite questions people asked
Speaker 3: me was howd they do it? And I love that
Speaker 3: question because I get to respond. So your hypothesis is,
Speaker 3: I know, how they took a few billion dollars and
Speaker 3: still take a few billion dollars out of the market
Speaker 3: to share among a relevant tiny group of people every
Speaker 3: year would apparently very low risk, and I choose not to.
Speaker 3: I have some inklings of the general whether they've worked on.
Speaker 3: My biggest guess is that they were ten fifteen years
Speaker 3: ahead of everyone else on most of this stuff and
Speaker 3: are just have developed more sophisticated systems over time. I
Speaker 3: think natural language processing they were very early on.
Speaker 1: It came from like from.
Speaker 3: A few other things. John Leu and I, my one
Speaker 3: of my founding partners, wrote when Fama and Schiller shared
Speaker 3: the Nobel Prize, we wrote a whole overview of market
Speaker 3: efficiency and the debate about it, and I brought them
Speaker 3: up as an example. The Medallion Fund has almost nothing
Speaker 3: to say about market efficiency. It says these guys can
Speaker 3: extract a toll on the market with reliable consistency. But
Speaker 3: in terms of market size it's a giant number for
Speaker 3: a few people to make each year. It's a tiny
Speaker 3: number in terms of whether the markets are efficient, and
Speaker 3: apparently the rest of us can't do it quite as
Speaker 3: well as them. They're the goat when it comes to this. Yeah.
Speaker 1: I think of like, you know, like Jane Street can
Speaker 1: rely excite somebody from the market, and I think of
Speaker 1: that as like a few first service ye, like a
Speaker 1: market maker, rather than being like a predictor of asset precesce.
Speaker 3: I'm gonna guess, and again it's a purely guess that
Speaker 3: not all of it, but a fair amount of Renaissance
Speaker 3: had a similar similar flavor.
Speaker 1: I think when you talk to people who run pod shops, like,
Speaker 1: some of what they do they conceive of as it's
Speaker 1: not literally market making, but it has that sort of flavor.
Speaker 3: And the constraint on those things is typically capacity. Now
Speaker 3: it's a ton of money for those firms. They're amazing firms,
Speaker 3: but give you an example of Renaissance. I've complimented them
Speaker 3: like crazy, so hopefully they won't be mad at me
Speaker 3: for this. One last comment. Everyone talks about the Medallion Fund,
Speaker 3: but no one's allowed to invest in that. They keep
Speaker 3: that for themselves. Another favorite question I get is this
Speaker 3: doesn't happen much anymore, but every once in a while
Speaker 3: I would get an investor saying, so, Medallion Fund better
Speaker 3: than you guys. I'd be like, oh, hell yes, but
Speaker 3: they won't take your money or my money, and I
Speaker 3: think we're pretty darn good and we will. So why
Speaker 3: is that relevant? But Renaissance also runs a fair amount
Speaker 3: of money in more open institutional products where they look
Speaker 3: very good. I'm not gonna to ride them at all,
Speaker 3: but they don't look better than us. They look like
Speaker 3: really good, solid, regular quants. So what they cannot do,
Speaker 3: I think it's kind of obvious, is take the medallion
Speaker 3: process and scale it up many, many times bigger. They've
Speaker 3: discovered a way, as you put it, to just take
Speaker 3: a certain amount out to provide a service. I called
Speaker 3: it taking a toll, and that's amazing. But they've not
Speaker 3: discovered a way to do that at institutional scale, which
Speaker 3: thank god, because that leaves something for the rest of
Speaker 3: us to do.
Speaker 1: I'll get back to something you talked about the very beginning,
Speaker 1: because you mentioned that paper you wrote with John Leu,
Speaker 1: which you talking about like the two possible explanations for
Speaker 1: making money for anomolies for whatever, which are sort of
Speaker 1: behavioral irrationality or whatever, and you were bearing a risk
Speaker 1: for someone. Everything you write, and like what you said
Speaker 1: here you have, you seem sort of like agnostic about
Speaker 1: which one or what combination there's like do you prefer one,
Speaker 1: is it more reliable to get paid for taking your
Speaker 1: risk or.
Speaker 3: Well forgetting which one you think is really going on?
Speaker 3: They do have different characteristics. The positive behind a risk
Speaker 3: premium is you may be able to get it forever
Speaker 3: because it's rational that you get it. The negative behind
Speaker 3: it is it's a risk premium. You have to figure
Speaker 3: out why. If the risk premium is most of the
Speaker 3: time this is fine, but it has depression risk. It's
Speaker 3: going to do particularly bad in a depression. Well, that's
Speaker 3: not a very pleasant risk to have. You may get
Speaker 3: paid for doing that. The positive of behavioral is it's
Speaker 3: essentially over the long term. Over the short term it
Speaker 3: can be very very painful lunch because the noise work.
Speaker 3: But if on average it works long term, it's a
Speaker 3: free lunch and that you're not taking additional systematic risk.
Speaker 3: The negative is it can go away. It can be
Speaker 3: arbitraged away if that ra stops being made, if too
Speaker 3: much capital chases it, it can be arbitraged away. It's
Speaker 3: hard to arbitrage some of them away, like basic valuation.
Speaker 3: It's very easy to arbitrage some others, like alternative data,
Speaker 3: where the point is to be quicker to getting a
Speaker 3: data set and to trading the day to set. I
Speaker 3: will say this, and I hope teen Fama isn't listening.
Speaker 3: I was probably seventy five twenty five. For risk guy
Speaker 3: thirty years ago, I'm probably seventy five twenty five.
Speaker 1: A behavioral is that GameStop or something not.
Speaker 3: Well, the game stop maybe the unfairly extreme example. It's
Speaker 3: not fair to pick the most extreme craziness is to
Speaker 3: make your point, But yeah, it's real life experience. It's
Speaker 3: watching the spread between cheap and expensive stocks in ninety
Speaker 3: nine two thousand. We started our firm in late ninety eight,
Speaker 3: so right before the real crazy part of what's called
Speaker 3: now the dot com bubble, the spread between how we
Speaker 3: define cheap and expensive, either just using quant multiples or
Speaker 3: adjusting for forms of growth. Set records. We had fifty
Speaker 3: seventy five years of data and we lived through it,
Speaker 3: blowing past those. Then after that, I'm like, all right,
Speaker 3: that was a once in fifty year event. We survived it.
Speaker 3: We ended up being right. We ended up making money
Speaker 3: round trip that was excruciating on the first leg. If
Speaker 3: you ask me back then, am I ever going to
Speaker 3: see that in my career again? I hope I'd be
Speaker 3: smart enough not to say never. None of us in
Speaker 3: our field, yours or mine, should say never. Markets are
Speaker 3: pretty hard things to say never about, but I think
Speaker 3: I would have said it's highly unlikely. For one, it
Speaker 3: was literally the most extreme event in at least fifty years. Second,
Speaker 3: the question presupposes I and people like me will still
Speaker 3: be around. Unpresumably if we're still around, we're in more
Speaker 3: supervisory and authoritative positions. So how's it going to happen again?
Speaker 3: And then it happened again almost exactly twenty years later,
Speaker 3: from twenty eighteen through twenty twenty, you can point to COVID.
Speaker 3: It went absolutely mad during COVID. You guys remember for
Speaker 3: the six month period after COVID when the only two
Speaker 3: stocks you're supposed to own were Peloton and Tesla. One
Speaker 3: of them has still worked out mostly for you. One
Speaker 3: of them did not. But even before COVID, though you
Speaker 3: cannot pin it on COVID, by late nineteen early twenty,
Speaker 3: we were approaching tech bubble levels, which again had not
Speaker 3: been seeing the prior fifty years before. The tech bubble again,
Speaker 3: painful period for us, again made money round trip. I'm
Speaker 3: going to brag about that we survived it. I think
Speaker 3: we're stronger than we were beforehand. But if the first
Speaker 3: one didn't make you start to go this behavioral stuff
Speaker 3: may be real and maybe bigger than it used to be. Yeah,
Speaker 3: and I wrote a piece called the less efficient market
Speaker 3: hypothesis markets getting less efficient. That may be true, but
Speaker 3: I think it's more accurate to say there what my
Speaker 3: evidence is there prone to some extreme bouts of craziness
Speaker 3: on occasion. It's not quite the same as in steady
Speaker 3: state always being less efficient, but probably some of both.
Speaker 3: But I do think that has happened. I probably was
Speaker 3: not giving behavioral enough credit early on, and I think
Speaker 3: it's gotten to be a bigger part.
Speaker 1: I want to read something from the Johnlely paper from
Speaker 1: twenty fourteen. Suppose you imagine some investors get joy from
Speaker 1: owning particular stocks, for example, being able to brag out
Speaker 1: a cocktail party about the growth stocks that they own
Speaker 1: that have done well. One way to describe this some
Speaker 1: investors have a taste for growth stocks beyond simply their
Speaker 1: effect on their portfolios. It can be rational for them
Speaker 1: to accept somewhat lower returns for this pleasure. But even
Speaker 1: if rational to the individuals who have this taste, if
Speaker 1: some investors are willing to give up returns to others
Speaker 1: because they care about cocktail party bragging, can we really
Speaker 1: call that a rational market and feel this statement is useful.
Speaker 1: I feel like that was like a little prescient about
Speaker 1: how I have experienced some of the last five years,
Speaker 1: which is that, like it seems to me that it
Speaker 1: is hard to explain some of the stuff I write about,
Speaker 1: which is maybe not like the most important thing in
Speaker 1: the world. It's often like I think the most important too,
Speaker 1: but it's like, you know, it's maybe not like the biggest,
Speaker 1: you know, dollar, the test LIS's pretty big. It does
Speaker 1: seem like a thing that is regularly happening is that
Speaker 1: people have a taste for stocks beyond any rational or
Speaker 1: irrational calculation about how we'll fight their portfolio.
Speaker 3: Particularly the whole meme world. Meme coins are clearly a
Speaker 3: political statement slash.
Speaker 1: Clearly there is a non economic motivation for some of that,
Speaker 1: and you can callude rational.
Speaker 3: But it's odd, well a few things that paragraph. Even
Speaker 3: writing it, I think we went a little too far
Speaker 3: because I don't think these people, we kind of write
Speaker 3: it like they're consciously doing it, like I just love
Speaker 3: owning these I know I'm gonna lose money. I think
Speaker 3: they kind of end up they resolve cognitive dissonance by
Speaker 3: saying I'm gonna make money even if that's what's really
Speaker 3: going on.
Speaker 1: Right, I think this is maybe a little harsh on
Speaker 1: growth investors in twenty fourteen, but on MEME investors.
Speaker 3: And now instead on meme investors. We were also given
Speaker 3: a little mild shot to some academics who kind of
Speaker 3: try to save market efficiency by saying it's more meta
Speaker 3: efficient if you count people just have tastes for this.
Speaker 3: That argument's been made. I think that's kind of a
Speaker 3: cop out. You know, if we've done market efficiency down
Speaker 3: to that, what do we actually even mean by market efficiency?
Speaker 3: If you go, I know, I'm gonna do terrible on this,
Speaker 3: but it's fun to me, that's functionally a fairly inefficient market.
Speaker 3: So we proposed in that piece which did not catch
Speaker 3: on that classically market efficiency and the testing of it
Speaker 3: has a joint hypothesis problem. You're testing whether markets are efficient,
Speaker 3: but you also need a model for how prices are set,
Speaker 3: and if that model for how prices are set includes
Speaker 3: this is fun to own, then you've pushed it too far.
Speaker 3: It has to be a reasonable hypothesiz.
Speaker 1: This is what I read about this is I want
Speaker 1: someone and it's probably not me. To write a finance
Speaker 1: textbook that incorporates the factor of this is fun to
Speaker 1: own and gives some guidance how to price that.
Speaker 3: Be and how long it's going to continue.
Speaker 1: And people like someone is making money on this, oh yeah,
Speaker 1: and like not just like fading it. Someone is like,
Speaker 1: I have a model for which meme coins will go up,
Speaker 1: And well.
Speaker 2: People have tried to do that.
Speaker 4: You think about all the different buzzy strategies, something about
Speaker 4: ETFs real cool, you can do it. Yeah, but you
Speaker 4: know there have been attempts if you scraped social media,
Speaker 4: et cetera and find out what people are buzzing about.
Speaker 3: But it's the old value manager's lament. But it's also
Speaker 3: I think true if ultimately it's a bad investment in
Speaker 3: return sense that will happen eventually. I do think that
Speaker 3: time horizon and the extremes have lengthened.
Speaker 1: A lot of the point of this way, Why will
Speaker 1: it happen eventually?
Speaker 3: Why will it happen eventually? Well, if you buy a
Speaker 3: company that continues to perform poorly and you paid a
Speaker 3: ton for it, I think there's only so long that
Speaker 3: can go on. I think it gets more and more obvious.
Speaker 3: For one thing.
Speaker 1: I think a lesson of the meme stack and frankly,
Speaker 1: if crypto is that fundamental setay flora and valuation. But
Speaker 1: you know, you know he's like doing LBI, right. But
Speaker 1: I don't want to just be mean to bitcoin for
Speaker 1: no reason. But like, you can certainly have a model
Speaker 1: in which you say the fundamentals of bitcoin are nothing,
Speaker 1: and you could then say and in fifty years it'll
Speaker 1: go to zero, but like that's a weird thing to say. Now,
Speaker 1: I don't know.
Speaker 3: Well, let me be clear. I one short Bitcoin with
Speaker 3: my worst enemies portfolio, and I one hundred percent sure
Speaker 3: I'll never say money is a little bit of magic.
Speaker 3: What becomes money is a little bit of magic. But
Speaker 3: I think most of the probability is eventually zero, but
Speaker 3: it may be a very long time arizon. I think
Speaker 3: what we've learned watching this stuff is that time horizon
Speaker 3: is lengthened.
Speaker 1: Okay.
Speaker 3: In my piece on this, I try to hypothesize, and
Speaker 3: I admit it's real opinionated hypothesizing. You cannot prove if
Speaker 3: I'm right that markets are somewhat less efficient. Why gets
Speaker 3: even harder. But one of my favorite explanations is an
Speaker 3: old man complaining about social media and twenty four to
Speaker 3: seven gamified trading. I don't think most people need a
Speaker 3: lot of convincing that this stuff has made say, our
Speaker 3: politics worse, made us more in bubbles hate each other
Speaker 3: more when it was supposed to make us love each
Speaker 3: other more. Marcus are just voting mechanisms. I don't think
Speaker 3: it's any different than politics. So this notion that things
Speaker 3: get crazier and can go on for longer, and I
Speaker 3: have a very cynical view of your statement that this
Speaker 3: stuff might take fifty years. I think the more absolutely
Speaker 3: unsubstantiated by anything something is the longer the craziness can
Speaker 3: go on.
Speaker 1: Right. I think that if you compare the longevity of
Speaker 1: bitkind to like the game Stop premium, I think right.
Speaker 3: But go back to the tech bubble. Cisco Systems great company,
Speaker 3: but was selling at a very stupid price, selling it about
Speaker 3: one hundred PE when the e was gigantic. You can
Speaker 3: have a small startup company it's growing super rapidly, sell
Speaker 3: at one hundred pee. One of the largest companies in
Speaker 3: the world with some of the largest earnings in the
Speaker 3: world selling at one hundred pe. You needed some very
Speaker 3: heroic and I would argue near I'll never say impossible,
Speaker 3: but near impossible assumptions even if the tech bubble didn't
Speaker 3: break in March of two thousand when it did, and
Speaker 3: by the way, I still don't know why it broke
Speaker 3: in March of two thousand and not a year earlier
Speaker 3: or a year later. Once you're well passed what I
Speaker 3: would consider rational saying you know exactly where but the
Speaker 3: time horizon, you can imagine that going on for when
Speaker 3: the growth is good, maybe even great, but not nearly
Speaker 3: enough to justify that price. It gets more and more
Speaker 3: obvious if something is based completely on air. One of
Speaker 3: the weird of down Matt's point that there's there's no
Speaker 3: arm to the upside, there's no LBO mechanism, and shorting
Speaker 3: it is just frankly too dangerous. It's a weird way
Speaker 3: to say. Sometimes people say markets are efficient because you
Speaker 3: can't make money from these things, and I'm like, it's
Speaker 3: another weird way to defend efficient markets to go they
Speaker 3: can be so friggin stupid that they're terrifying to make efficient.
Speaker 3: That may be totally true, but it also is a
Speaker 3: weird way to argue that markets are efficient.
Speaker 4: What does it mean for AQR if markets are less efficient.
Speaker 3: Well, any active management is an inherently arrogant act. You
Speaker 3: cannot tell the average person we should all be active managers,
Speaker 3: we should all have podcasts.
Speaker 2: But I agree, So it's.
Speaker 3: It's top to believe you should be an active manager
Speaker 3: and to believe you're doing something good for your clients,
Speaker 3: you have to believe two things. That you have alpha
Speaker 3: and that you're not charging the full extent of that
Speaker 3: alpha through your fees. And we do believe that, and
Speaker 3: a lot of active management managers believe it. But it's
Speaker 3: an inherently arrogant act. It's consistent to say most people
Speaker 3: shouldn't do this, but we should, though the arrogance is obvious.
Speaker 1: What percent of your alpha should in your face?
Speaker 3: That is a super hard question. I've thought about writing
Speaker 3: about this at one point. It kind of depends on
Speaker 3: how unique your alpha.
Speaker 1: Is, Okay, because I would think that if you got
Speaker 1: a podshot manager really drunk, they would say, no.
Speaker 3: That's what they can charge for their fees, not what
Speaker 3: they should charge. I don't think they'd ever admit them
Speaker 3: an algie now, like will versus shall.
Speaker 1: Deep in their soul, they want one hundred and ten percent.
Speaker 3: It's a great question, but it's a hard question if
Speaker 3: your alpha is doing FOM and French price to book.
Speaker 3: By the way, I still think Farm and French are
Speaker 3: going to be right in the next thirty years. They
Speaker 3: haven't been right in a while, but spreads have gotten
Speaker 3: wider and wider, and that's been a wind in their face.
Speaker 3: I wrote a piece on this saying the long run
Speaker 3: is lying to you, saying that x the spread widening
Speaker 3: values to the simple Farm of French value has delivered alpha.
Speaker 3: It's just lost on the repricing. But what you can
Speaker 3: actually charge for doing price to book should be very low,
Speaker 3: a very small fraction of the expected return. That's alpha data, right,
Speaker 3: But that's kind of what we're saying. Everything exists on
Speaker 3: a spectrum from one to the other. If you have
Speaker 3: discovered and built the database yourself, an alternative data source
Speaker 3: that you have built. This is extreme and I can't
Speaker 3: think of an example at AQR that fits this. But
Speaker 3: you can charge nearly one hundred percent of that alpha
Speaker 3: because what they're getting is still absolutely unrelated to everything
Speaker 3: else what they're doing. There's some deminimus notion that if
Speaker 3: you charge ninety nine percent, maybe no one would bother
Speaker 3: to do it. But you can charge a large fraction
Speaker 3: of the alpha because what you're delivering is still ultimately
Speaker 3: net returns that you can not get elsewhere that aren't
Speaker 3: correlated to the rest of the portfolio are worth it.
Speaker 1: We can talk abstractly about what percentage of your alpha
Speaker 1: you should charge, but like, no one could send a
Speaker 1: bill for like ninety percent on the alpha, right, Like
Speaker 1: is pricing sort of like set by just like anchored norms.
Speaker 3: Anchored norms is another way of saying, what are other
Speaker 3: people charge or in the ballpark of what you're doing?
Speaker 3: So of course that matters, right if you are way
Speaker 3: off the anchor, way off on the high side, no
Speaker 3: one should invest with you. If you're way off on
Speaker 3: the low side.
Speaker 1: People who charge really high fees and are pretty good
Speaker 1: at demonstrating they have alpha, and oh.
Speaker 3: Some of the block shops charge insane fees and have
Speaker 3: been very good, And that goes to that are they
Speaker 3: doing something unique? And I think to some extent they are.
Speaker 3: I think their problem becomes kind of in the direction
Speaker 3: of Medallion without going all the way. I don't think
Speaker 3: they've rebuilt medallion. Nothing is that, but they should charge
Speaker 3: a higher percent if they're doing something very unique, and
Speaker 3: they do. I have a very you can you know
Speaker 3: violin playing I think rosy view of how we think
Speaker 3: about fees. We're building a long term business. We think
Speaker 3: we have business value. We do not think we're just
Speaker 3: a hedge fund. We run a lot of traditional assets too,
Speaker 3: even the hedge funds. We were a big pioneer in
Speaker 3: doing the more obvious strategies and considerably lower fees, starting
Speaker 3: in merger, ARB and TREND. Following the way we broke
Speaker 3: into some of those as standalone products, not as part
Speaker 3: of our multistrats was charging less and saying, you know,
Speaker 3: this is real and it's good. But you know you
Speaker 3: can do one of every merger and make a fair
Speaker 3: amount of money. But that's not magic, and we shouldn't
Speaker 3: charge magic fees for it. But in that kind of kumbay,
Speaker 3: a big picture sense, charging fees such that clients are
Speaker 3: happy with the long term results is probably how you
Speaker 3: build business value if you step.
Speaker 4: Back from just AQR though, I'm curious to hear your
Speaker 4: thoughts on your industry overall and whether alternative strategies in
Speaker 4: general are too expensive.
Speaker 3: Yes, they are.
Speaker 4: I have some numbers to back up that statement. There's
Speaker 4: a new study out there. My understanding of it is
Speaker 4: that you basically take a sixty to forty since two
Speaker 4: thousand and eight, you add Alt's exposure to it in
Speaker 4: various proportions, and that blended portfolio basically trails that benchmark,
Speaker 4: basically in close proportions to the fees that are charged
Speaker 4: by some of the ALTS managers.
Speaker 2: And I don't know.
Speaker 4: I read that and I was just kind of wondering.
Speaker 4: I mean, you could make the case that why are
Speaker 4: we charging so much?
Speaker 3: Well, this is almost a mathematical certainty. Yeah, forget about
Speaker 3: ALTS for a second. You cannot tell me the average
Speaker 3: active portfolio beats the market after fees and costs. The
Speaker 3: average active adds up to the market because for every
Speaker 3: deviation one way, there's deviation the other way. When I
Speaker 3: said it to inherently an arrogant act to be an
Speaker 3: active manager, it means you think you got it, even
Speaker 3: though if you buy one of each you can't have it.
Speaker 3: I mean a subset of the market like could. But
Speaker 3: I think a lot of that still applies. It's inherently
Speaker 3: arrogant act. But we've been saying this for at least
Speaker 3: twenty four years. We wrote a paper, and yes, I
Speaker 3: started a lot of sentences with We wrote a paper
Speaker 3: in two thousand and one called do hedge funds Hedge
Speaker 3: where we took the known indices of hedge funds and
Speaker 3: we tried to take out just the market beta. You
Speaker 3: could argue for a more sophisticated risk model, and that could,
Speaker 3: as usual, go down that rabbit hole. But we found
Speaker 3: that betas were First of all, this is more mundane
Speaker 3: but statistically underestimated, partly because a lot of hedge funds
Speaker 3: do some stuff that is not of perfect liquidity. And
Speaker 3: this was a small early version of what I got
Speaker 3: into at the private world later on. But if something
Speaker 3: doesn't trade all the time and you try to estimate
Speaker 3: its correlation with the market, you will underestimate it because
Speaker 3: one great way to look uncorrelated is to have a
Speaker 3: three day leg, and when you trade, your returns will
Speaker 3: be off. So the betas were underestimated by earlier studies.
Speaker 3: Given the correct betas, there was pretty much no alpha
Speaker 3: to the hedge fund world. First of all, I was
Speaker 3: a lot younger than a lot less well known, so
Speaker 3: I cared. Nowadays, I quite obviously court controversy. But back
Speaker 3: then I probably had ten famous managers call me and
Speaker 3: yell at me about that paper. Yeah. The first time,
Speaker 3: I was, of course obnoxious. They called and said, why
Speaker 3: did you write this? And I gave the obnoxious answer
Speaker 3: because we think it's true. Now it was apparently not
Speaker 3: an acceptable answer. I will only call out one person
Speaker 3: on the positive side, Richard Perry, famous hedge fund manager.
Speaker 3: He called me up. And I already been yelled at
Speaker 3: by a whole bunch of people. And so when I
Speaker 3: heard Richard Perry on the phone, people like Richard Perry
Speaker 3: didn't call people like me. Back then. He was big,
Speaker 3: I was small. So I'm like, I know what this is.
Speaker 3: He's just gonna yell at me. He gets on the phone,
Speaker 3: he goes, that paper you wrote, that's just correct, good job, right,
Speaker 3: And I'm only telling that. I'm not giving the names.
Speaker 3: And I do remember him, of course, and in a
Speaker 3: book somewhere, it's all. I have a list of Vedettas
Speaker 3: in my brain pursuit.
Speaker 1: Wait, so you've been you've been making fun of private
Speaker 1: aquery managers on some lines recently. Do you get calls
Speaker 1: from them?
Speaker 3: No, they're so fat and happy. They don't even care
Speaker 3: about me making fun. No, I get yelled at by
Speaker 3: some usually friends. I live in Greenwich connection, so you
Speaker 3: all hang.
Speaker 1: Out the country club. They're like, I, I'm.
Speaker 3: Actually a country but the old often used in a
Speaker 3: very bad way. Some of my best friends are, so
Speaker 3: I'm off the hook. I have some good friends who
Speaker 3: are private equity managers. Most of them can accept the
Speaker 3: you're right about the industry but not our firm, which
Speaker 3: is essentially what I'm saying. So I can't not being
Speaker 3: mean about this.
Speaker 1: Right, You're not like your criticism is volatility, lunder Right,
Speaker 1: Your criticism is that private equity seems to have a
Speaker 1: higher sharp because it has a lower of volatility because
Speaker 1: it doesn't report.
Speaker 3: Yes, that is my main criticism, which I think is
Speaker 3: quite obviously true. That's just me. Some people do disagree.
Speaker 3: The best disagreement I've heard, and I have some sympathy
Speaker 3: for this because I do not think markets are perfect,
Speaker 3: is we are right about the valuations. You are right
Speaker 3: that we move at a highly damped version of the market,
Speaker 3: but market moves too much. We are right. My response
Speaker 3: to that is, why don't we get to do that?
Speaker 3: It's fair, you know, when we've had a tough year
Speaker 3: because the market's gone crazy and we were on the
Speaker 3: wrong side of that. I have to tell my clients
Speaker 3: we're down twelve percent. I don't get to tell my
Speaker 3: clients we're up based on where I'd mark the portfolio.
Speaker 3: So why one group? And by the way, they could
Speaker 3: market just like we market. They're brilliant at valuing companies
Speaker 3: and they can tell you where they could sell it
Speaker 3: for today. So it's like an institutional legal quirk that
Speaker 3: they get to do it one way and we have
Speaker 3: to do it another. Everyone can mark their portfolio at
Speaker 3: what they could sell it for today or what they
Speaker 3: think it's worth, and one side gets to do it
Speaker 3: one one gets to do the other. So that's a
Speaker 3: reasonable argument, but I still think it leads to an
Speaker 3: unreasonable conclusion. I will say ninety percent of my critique
Speaker 3: is about the volatility or the beta, is about saying
Speaker 3: these things are low risk. Ten percent is about percent
Speaker 3: if not trailing future returns, because if I'm right about
Speaker 3: the volatility laundering, it has implications for returns going forward.
Speaker 3: If when David Swenson was pioneering, which is what he
Speaker 3: called his book, Private Equity is part of an institutional
Speaker 3: endownment portfolio. He's quite clear. It's a lot of it.
Speaker 3: It's an ill liquidity premium that no one wants. Illiquidity.
Speaker 3: Everyone's scared of it. So if you're willing to do that,
Speaker 3: you get paid extra. If I'm right that people love
Speaker 3: the fact that they don't have to look at the volatility,
Speaker 3: that means illiquidity is no longer a bug. It is
Speaker 3: now a feature and very simple model for how expective
Speaker 3: returns are set. You get paid a higher expective return
Speaker 3: in something if you have to bear a bug nobody
Speaker 3: wants and you pay through a lower expector return. If
Speaker 3: you have a feature everyone wants, then you have to
Speaker 3: pay up for it. So the chance that that is
Speaker 3: going on going forward, I think is quite high. Whether
Speaker 3: it means there's no edge to private equity or a
Speaker 3: negative edge or a smaller positive edge can tell you that.
Speaker 3: But I do think if you think of risk ajusy
Speaker 3: return as numerator of return denominator of risk, I think
Speaker 3: my statements are mostly about the denominator, but they're ten
Speaker 3: percent now about the numerator. Going forward, you're.
Speaker 1: In my conservative private equity private markets by which is
Speaker 1: that it seems to me that like there is a
Speaker 1: ton of fee pressure in public markets and everyone has
Speaker 1: kind of like learned the gospel of like Bilow cost
Speaker 1: index funds and even charging for farma French factors is
Speaker 1: not a two and twenty business. And private markets because
Speaker 1: they're not indexible because it's harder to like extract factors
Speaker 1: because they're not liquid, don't have those problems, and you
Speaker 1: can charge two and twenty for a lot of private stuff.
Speaker 1: And so like it seems to me that there is
Speaker 1: a move to you put a lot of stuff that
Speaker 1: would have previously been public into private markets, and to
Speaker 1: say we can put privates into four oh one ks
Speaker 1: and there's a good economic grastionals we're putting private assets
Speaker 1: into four one case because you don't need liquidity. But
Speaker 1: there's also like this like really overwhelming if.
Speaker 3: There's a positive illiquidity premium.
Speaker 1: Yeah too right, sorry, go no. I just like you know,
Speaker 1: you've written for years ago like criticizing people for charging
Speaker 1: alpha fees for beta, and it seems to me that
Speaker 1: like the reprivatization on the market is a way to
Speaker 1: sneak some alpha fies onto beta.
Speaker 3: I think a tremendous amount of the private world is
Speaker 3: charging massive alpha fees for beta. I won't mince words
Speaker 3: about that. If they outperform or underperform on net after
Speaker 3: all these massive fees, and if performance going forward, it
Speaker 3: is tougher. And by the way, your point about the
Speaker 3: fee compression in the public world only makes my hypothesis
Speaker 3: that they won't beat the public world by the same
Speaker 3: amount going forward stronger. I think privates have a big
Speaker 3: function in the world. I don't think they're going away
Speaker 3: what you started out with me, what do you guys
Speaker 3: do for the world. There are things that are in
Speaker 3: between what should be public and what's mom and pop.
Speaker 3: But I think where we are now, I think a
Speaker 3: lot of institutions are giving up some amount of expected
Speaker 3: return for the ease of limit, of reducing their agency
Speaker 3: problem of sticking with something. Now, if they're going to
Speaker 3: be terrible and not stick with things, it might be
Speaker 3: rational to give up some return. But you can't double
Speaker 3: count and say we're making ourselves better investors giving up
Speaker 3: some expected return and oh yeah, our expected returns are going.
Speaker 1: To be higher.
Speaker 3: If that's the rationale, then you've accepted my argument, and
Speaker 3: I think you have to say, this is what we
Speaker 3: get paid for by making your life easier.
Speaker 4: I am curious what you make of the push to
Speaker 4: put privates into more retail accessible writers. I'm talking about
Speaker 4: et avs, but I guess I'm also talking about interval
Speaker 4: funds a little bit.
Speaker 2: It just seems like that's where the world is headed.
Speaker 3: I'm going to hedge this and say it very carefully.
Speaker 3: I think it's a terrible idea.
Speaker 5: Okay, go on.
Speaker 3: There are things that end up in retail in a
Speaker 3: very good way eventually, but we've done some of this
Speaker 3: when we introduce mutual funds. I can't say always going
Speaker 3: to retail is a bad thing. You can price it reasonably,
Speaker 3: you can say these are strategies you've never had before.
Speaker 3: This feels a little bit more like we've exhausted the institutions.
Speaker 3: I think I saw a number saying endown. It's like
Speaker 3: forty three percent for a number I can't verify. That's
Speaker 3: wildly specific, but that's the number I remember. So it
Speaker 3: has a feel of who else we're going to get
Speaker 3: to own this stuff.
Speaker 1: It seems so explicitly, and it's really wild.
Speaker 3: I think it's explicitly that it's one of these things
Speaker 3: that for the cynics, I don't think they'll ever be
Speaker 3: a satisfying moment where we get to say we were right.
Speaker 3: It'll just be somewhat worse over the next decade. It's
Speaker 3: not one of these things like a tech bubble in
Speaker 3: ninety nine two thousand that at least, I don't think
Speaker 3: there are scenarios where things get worse rapidly and the
Speaker 3: secondary sales. We got close to some of that in
Speaker 3: the GFC. I was on some investment committees where we
Speaker 3: were talking we didn't do it, but we were talking
Speaker 3: about whether we would have to do that. So I'm
Speaker 3: not saying it's a chance of ugliness, but I think
Speaker 3: the meat of the probability is it's just somewhat worse.
Speaker 3: But it does cause me some worry sadness to say, yeah,
Speaker 3: what we really need is to stick a liquids in
Speaker 3: four to one case.
Speaker 2: Yeah.
Speaker 3: So yeah, bluntly, I think it's a bad idea.
Speaker 4: Well, I just wanted to talk about and I don't
Speaker 4: know if this goes too far, but it feels like
Speaker 4: you had a change of heart when it comes to
Speaker 4: machine learning and AI in general. This is something we've
Speaker 4: spoken about before.
Speaker 3: I did well.
Speaker 4: Was that like a light bulb moment or was that
Speaker 4: just you know, maybe people on your team wearing you
Speaker 4: down over time?
Speaker 3: It was more the ladder. Yeah, the ladder is the
Speaker 3: second one, right, I gotta think that through every time
Speaker 3: I tell that people this, I've said in a lot
Speaker 3: of public arenas I sell it to clients. I think
Speaker 3: I probably slowed us down by a couple of years
Speaker 3: in machine learning. I think it probably costs us some
Speaker 3: money because the stuff has worked pretty well. We've always
Speaker 3: described ourselves and we were getting into this a little
Speaker 3: bit earlier. As a blend of data, back tests are nice,
Speaker 3: out of sample long periods are even nicer. But also theory.
Speaker 3: Theory can be a formal economic theory, but it can
Speaker 3: also just mean a common sense story where you think
Speaker 3: you understand why you're making money. And there's no way
Speaker 3: to say exactly what percentage of both. But I've often
Speaker 3: described it as an attempt to be fifty to fifty.
Speaker 3: We want something to make sense to us as economists
Speaker 3: and have strong data when you move into the machine
Speaker 3: learning world. I don't think you have to abandon theory,
Speaker 3: and this is something I think we do a little
Speaker 3: different than most. I think there are some in the
Speaker 3: machine learning world to kind of throw theory out the window.
Speaker 3: We're still using common sense and theory to kind of
Speaker 3: limit the scope, but you are leaning more on the data.
Speaker 3: And again I'm making up these numbers. But if regular
Speaker 3: stuff is fifty to fifty, machine learning seventy five twenty
Speaker 3: five data even for us, that was uncomfortable for me.
Speaker 3: When you've been telling a story for twenty five years
Speaker 3: and it's worked for you and your clients, it's not
Speaker 3: easy to move. I also think it's not improper for
Speaker 3: the role for the old man of the firm to go,
Speaker 3: let's just slow down. You guys come in here doing
Speaker 3: this with a Southern droll and your new fangled machine learning. No,
Speaker 3: that's the villainous rule.
Speaker 2: Oh no, I think it's kind of cool.
Speaker 3: Yeah, okay, so I actually can defend it as entirely appropriate.
Speaker 3: But yeah, I had to be convinced. It wasn't a
Speaker 3: light bulb moment. It was people like Brian Kelly, Andrea Ferzini,
Speaker 3: Laura Serb and all partners of mine presenting great results
Speaker 3: that made increase it and I did a lot more reading.
Speaker 3: I was probably I programmed in a programming language called
Speaker 3: LISP in the nineteen eighties. That was an early machine
Speaker 3: learning or AI language. I'm not even machine learning, and
Speaker 3: then I didn't do anything about that for the next
Speaker 3: thirty years, so I think part of it was just
Speaker 3: me getting up to speed. Frankly, the thing I.
Speaker 1: Find compelling in the Kelly paper is like you feed
Speaker 1: like a sort of toy set of factors into like
Speaker 1: a ten thousand neuron model, and what he argues is
Speaker 1: basically you might get a better view of the actual
Speaker 1: like function that generates the results than if you just
Speaker 1: try to like use your economic intuition in a lineary aggression. Right.
Speaker 1: There's like theory behind it. In the theory is like
Speaker 1: things are not as linear as you know traditional methods.
Speaker 3: And it's basically saying it's still a sin to only
Speaker 3: look for patterns. You still need some economics, but machine
Speaker 3: learning is better at balancing those trade offs. So the
Speaker 3: idea of throwing more at it when you have a
Speaker 3: better technique for that, you lean in that direction. I
Speaker 3: did have a better title, I think than him. I
Speaker 3: wanted him that's not better. His titles great pretty well, no,
Speaker 3: but I wanted to call it simply Ackham was wrong. Okay,
Speaker 3: all right and maybe not bad, but I still want
Speaker 3: him to use that for maybe a follow up paper.
Speaker 1: All right, Cliff, thanks for coming.
Speaker 3: Oh this was fun.
Speaker 2: Thanks for video with us.
Speaker 1: And that was the Money Stuff Podcast.
Speaker 6: I'm Matt Levi and I'm Katie Grifeld.
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