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.
Speaker 3: Instead of we just randomly got together the.
Speaker 2: Chat and microphonest.
Speaker 3: It is like 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 5: That I shaved over COVID for the first time in
Speaker 5: about thirty years. Okay, and my kids freaked out. Yeah,
Speaker 5: they were like, you're an alien.
Speaker 1: Yeah, my kids didn't care that much. But one of
Speaker 1: my sons said, it's a different daddy.
Speaker 5: Well, you also have hair on the top of your
Speaker 5: head when you shave the beard and you don't have
Speaker 5: any hair, 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 2: 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 a q R. Thanks for coming in, Thanks
Speaker 1: for having me. I always like to ask how to
Speaker 1: my managers, like what do you do for a living?
Speaker 1: Like what is it like economic function of like the
Speaker 1: business that you run.
Speaker 3: Okay, those are to me slightly different questions.
Speaker 1: Right. One sounds like it's about you. One sounds like
Speaker 1: it's about AQR.
Speaker 5: I'm more interested in even if it's about a q R.
Speaker 5: What you do for a living is And people might
Speaker 5: not like this phraseology, but you're trying to predict what
Speaker 5: happens to securities. You're trying to buy ones that go
Speaker 5: up either in the absolute or more than some benchmark,
Speaker 5: and and sell ones that do the opposite. The broader question,
Speaker 5: which I think is behind what you're saying, is what
Speaker 5: do you do for the world by doing that? And
Speaker 5: they overlap, but they're not exactly the same thing. Something
Speaker 5: can have a net positive effect on the world even
Speaker 5: if you're not waking up. And I'll admit this, I
Speaker 5: don't think most people in their jobs waked up and
Speaker 5: always think that way, and certainly not active managers just
Speaker 5: 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.
Speaker 5: Is first, take the other side of positions other people
Speaker 5: disagree with you on or don't want to bear. That
Speaker 5: can take two forms. That can mean one of you
Speaker 5: is biased and wrong.
Speaker 3: You hope it's not you.
Speaker 5: But in that case, what you do for the world
Speaker 5: is you move prices back towards not necessarily all the
Speaker 5: way too in the abstract, the correct price. That's hard
Speaker 5: to define, actually, but something is mispriced and you take
Speaker 5: the other side of that.
Speaker 3: It could be a risk premium. Other people don't want
Speaker 3: to bear in a lot of strategies necessarily that they're
Speaker 3: making an error.
Speaker 5: If a merger's announced and three quarters of the pop
Speaker 5: you should get if it closes happens, a lot of
Speaker 5: people might not want to stick around for that last quarter.
Speaker 3: And if you're willing to.
Speaker 5: Take the other side of that, maybe you get paid
Speaker 5: a little for doing that, and that is a service
Speaker 5: to the market. People want to get out and you're helping.
Speaker 5: That's kind of the positive side, But again it's not
Speaker 5: what you're thinking about.
Speaker 3: When you do the trade.
Speaker 5: There are positions active managers will take that are not
Speaker 5: about that.
Speaker 3: Let me put it this way.
Speaker 5: If what you're trying to do is predict returns, you
Speaker 5: can predict returns because the price.
Speaker 3: Is moving towards truth.
Speaker 5: But you can also make money if you predict the
Speaker 5: price moves further away from truth.
Speaker 3: You know, if you're a.
Speaker 5: Momentum memestock investor. And doesn't mean you can't get that right,
Speaker 5: you know. I think of that a little more as
Speaker 5: trading than investing. But they all come together, and it
Speaker 5: even gets complicated within some famous quantitative factors. One famous
Speaker 5: quant factor is the momentum factor.
Speaker 1: I asked the finance professor I should I have asked?
Speaker 1: And he said, you should ask him if momentum trading
Speaker 1: makes markets more or less efficient.
Speaker 3: We don't fully know, but I can tell you the framework.
Speaker 3: There are two.
Speaker 5: Competing explanations in academia and a general world that cares
Speaker 5: about these things. For why momentum on average.
Speaker 3: Works, there's always a third that it will never.
Speaker 5: Work again and it was just random and it was
Speaker 5: just luck. But if it's true, why does it work?
Speaker 5: I actually think these can both coexist, so it's not
Speaker 5: truly embarrassing. But it sounds embarrassing that the two major
Speaker 5: explanations one is under reaction and the other is over reaction.
Speaker 5: 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 situation 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.
Speaker 5: Information comes out and people have a behavioral bias that
Speaker 5: behavioral psychologists would call anchoring an adjustment.
Speaker 3: They move towards all the way to the.
Speaker 5: New information, and I think that fits a lot of
Speaker 5: intuition in the short run that can make momentum work.
Speaker 5: If you're following fundamentals or prices, good news comes out
Speaker 5: or the price moves. If good news comes out, on average,
Speaker 5: the price goes up but not enough. If the price moves,
Speaker 5: it may on average be responding to good news, and
Speaker 5: simply by observing the price move, you can say, Okay,
Speaker 5: sometimes it's wrong, but on average it doesn't quite move enough.
Speaker 5: If that's the reason, And I think the weight of
Speaker 5: the opinion in academia I believe is towards this underreaction explanation. Then,
Speaker 5: even though you're trading on momentum, you're still moving the
Speaker 5: price towards kind of truth or equilibrium or something. But
Speaker 5: the flip side is overreaction. Do you think of that
Speaker 5: more as just your classic positive feedback loop. Someone's buying
Speaker 5: something just for you know, fomo. It's been going up
Speaker 5: in there, and well that could be a negative reason,
Speaker 5: or they're just predicting more people buy it because of Fomo.
Speaker 5: In that sense, if you buy some weird meme coin,
Speaker 5: you could do that for a rational reason, not that
Speaker 5: you are a long term holder, but you just believe
Speaker 5: it's going to keep going.
Speaker 1: So did you make money on GameStop?
Speaker 3: This the god's honest truth.
Speaker 5: You won't believe me. I have no idea if we
Speaker 5: were long or short game stop during the whole thing.
Speaker 2: You never went back and check.
Speaker 5: No, I never did. I did have an episode where
Speaker 5: I mentioned on perhaps a different TV network that we
Speaker 5: were short, AMC.
Speaker 2: What does your Twitter look like after that?
Speaker 5: It was very ugly. Yeah, I certainly knew of that world,
Speaker 5: even though we're quants. I watch the markets all day,
Speaker 5: even even if I don't do anything about it. It's
Speaker 5: like the old joke about the weather. Everyone talks about it,
Speaker 5: but nobody does anything about it.
Speaker 2: Best thing to talk about.
Speaker 5: So it's not like everything that went on with GameStop
Speaker 5: Melvin Capitol. You know, I'm watching it every day, but
Speaker 5: we take relevant tiny positions in every stock. There was
Speaker 5: nothing weird in our P and L. And yes, I
Speaker 5: was not even curious. It probably wasn't even in our
Speaker 5: universe at that point of things.
Speaker 3: We trade.
Speaker 5: But then I'm going on this other network it's allowed
Speaker 5: and in kind of a pre call of what are
Speaker 5: we going to talk about?
Speaker 3: You guys know, you.
Speaker 5: Don't want to get on there and have absolutely nothing
Speaker 5: to talk about. You want to have some not necessarily
Speaker 5: the answers worked out, but agreed upon topics. They're like
Speaker 5: everyone in this segment gives us some longs and shorts,
Speaker 5: but I'm saying, as a quant that's kind of silly.
Speaker 5: They're not indicative, and we kind of made a deal
Speaker 5: where they'd let me briefly explain that doesn't make a
Speaker 5: whole lot of sense for quantum, but it might be fun.
Speaker 5: And my way of saying it was, if I give
Speaker 5: you a few names long and a few names short,
Speaker 5: you could look in six months later and think we
Speaker 5: had a fantastic year or a terrible year and be
Speaker 5: terribly wrong in either direction because they're tiny. So I
Speaker 5: went through an AMC was I think I'm accidentally doing
Speaker 5: it again.
Speaker 2: We'll keep going hopefully.
Speaker 3: They don't listen to you, guys.
Speaker 5: But it was bad on every single thing in our
Speaker 5: model practically, which is hard to do. It was expensive, unprofitable,
Speaker 5: high beta. They were issuing shares, not buying backshit. There
Speaker 5: are more examples, and so I said that, but then
Speaker 5: I added that were only short twelve basis points, so
Speaker 5: the crazy people could be right and it doesn't really matter.
Speaker 5: I discovered two things. They're not going to like a
Speaker 5: short period, and crazy people don't always like being called crazy. Yeah,
Speaker 5: I had to discover that from myself. So my Twitter
Speaker 5: got ugly for a while. You may have noticed. I've
Speaker 5: gotten I think a fair amount better at this, But
Speaker 5: I used to be pretty bad about responding to ugly,
Speaker 5: which you learn your lesson on that.
Speaker 2: You always feed the trolls.
Speaker 3: Less so than I used to. At least I think
Speaker 3: I'm better.
Speaker 5: Maybe I'm wrong, but I became public enemy number three
Speaker 5: to the meme.
Speaker 3: Stock crowd for a while, I did.
Speaker 5: Not really Yes, Kenny Ken Griffin was number one. I
Speaker 5: don't believe Ken did this, but it's the whole poll,
Speaker 5: the Bible. And oddly enough, Gary Gensler was clearly number two. Yeah,
Speaker 5: because they thought he was covering for the manipulators and
Speaker 5: the naked shorts and whatnot. I've met both, I know,
Speaker 5: and a little better than Gary. I don't think there
Speaker 5: are any two people on Earth less likely to be
Speaker 5: in cahoots than those two. I think they're on the
Speaker 5: opposite side of most issues. But that was the theory.
Speaker 5: But both Ken and Gary are too smart to respond
Speaker 5: to them on Twitter, so I certainly became the most
Speaker 5: actively engaged, and then I never did that again before today,
Speaker 5: when I've accidentally done it.
Speaker 4: Before we get back to stuff that matters, can I
Speaker 4: just on AMC, I tweeted, when did June two come out?
Speaker 2: It was like last summer.
Speaker 3: Yeah, year ago.
Speaker 2: I tweeted that I.
Speaker 4: Fell asleep during Dune two, and it reawakened that crowd,
Speaker 4: at least on my Twitter, because I've the same.
Speaker 3: Crowds crowd because he's dissing a movie.
Speaker 5: Don't understand you have to like every movie or else
Speaker 5: here anti America, And then there were.
Speaker 4: A lot of conspiracy theories about Bloomberg reporter lies about
Speaker 4: falling asleep in.
Speaker 2: Doom too because she hates AMC. It would be, but
Speaker 2: it wasn't to.
Speaker 5: Oh, yeah, you do innocuous that I was obnoxious, so
Speaker 5: I'd kind of deserved it.
Speaker 3: You didn't deserve Thank you for saying that you didn't
Speaker 3: 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.
Speaker 5: You were asleep, well no, not the whole time, not
Speaker 5: 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.
Speaker 5: Know, you're ar It would have been better for me
Speaker 5: if we didn't have the call, because then I could
Speaker 5: have just said I don't know. I rarely know individual stocks,
Speaker 5: and if I do know, I'm probably not happy I know.
Speaker 5: And even then it's like we lost twenty BIPs on
Speaker 5: that today, which is a giant number for us to
Speaker 5: lose on one stock, and even then I probably don't
Speaker 5: notice twenty bit.
Speaker 1: If someone comes to you and says that's not.
Speaker 3: One, I might be told about it.
Speaker 5: For us, it's whether these seven hundred and fifty stocks be
Speaker 5: these seven hundred and fifty stocks. Yeah, I don't memorize
Speaker 5: fifteen hundred stocks. Now, we take a fair amount of risks,
Speaker 5: some funds more, some funds designed to be less. And
Speaker 5: that's about the size of these two positions. So when
Speaker 5: I say small, I'm not saying we're not both taking
Speaker 5: risk and trying to generate pretty decent returns. But if
Speaker 5: you just think about it, a quant is playing the odds.
Speaker 5: They're saying, affirm a company with these characteristics. And this
Speaker 5: can be old school factor quants from the nineteen nineties,
Speaker 5: these can be modern machine learning. But with these characteristics
Speaker 5: tend to beat these characteristics. If that's all you know,
Speaker 5: and it is all we know, why on earth would
Speaker 5: you take a lot of risk in any one company.
Speaker 3: AMC really could have done well.
Speaker 5: It could have been bad on every single thing that
Speaker 5: on average doesn't work, and it could be a special
Speaker 5: situation that we don't understand. Something could be good on
Speaker 5: everything and the CEO can embezzle all the money. We
Speaker 5: don't want to take a lot of risk on any
Speaker 5: one thing because we have no insight in that it's
Speaker 5: risk for no return.
Speaker 1: One 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.
Speaker 5: There are still differences and maybe some of the like
Speaker 5: momentum if it's overreaction, if you're riding momentum. I don't
Speaker 5: think a Graham and DoD manager does that. So I
Speaker 5: don't want to push the analogy. But this came out
Speaker 5: very very early in my career. This is like thirty
Speaker 5: years ago. This is like my Goldman Sax days. I
Speaker 5: started hearing a lot of active stock pickers, some I'm
Speaker 5: still still friends with one guy in particular. I was
Speaker 5: laughing at them, and I was telling my friend, you
Speaker 5: all say the same thing. You all say you're looking
Speaker 5: for valuation plus a catalyst. It's like a I don't
Speaker 5: know if everyone says it, but I've heard it many
Speaker 5: times and I'm making fun of him, and at some
Speaker 5: point he looks at me and goes, you do value
Speaker 5: on momentum, and it was a gotcha. He won that
Speaker 5: round because I'm like, okay, I see your point. So
Speaker 5: even back then, you can think of those two together.
Speaker 3: We literally add them up.
Speaker 5: But if you think of him as this holistic system,
Speaker 5: we're looking for cheap things that are starting to get
Speaker 5: better in price or fundamentals over time. And now I'm
Speaker 5: I'm not talking the really modern stuff, alternative data, machine learning.
Speaker 5: I'm talking just classic quant stuff that's been academia and
Speaker 5: then imports over to applied.
Speaker 3: Profitability is a factor.
Speaker 5: Robert nov Mark's wrote a great paper that we all
Speaker 5: incorporated where also equal give evaluation and momentum.
Speaker 3: If a company is more profitable on some.
Speaker 5: Famous scale's gross profitability ROA Roe, that's some degree a
Speaker 5: positive low beta investing. Two of my colleagues, Andrea Frazini
Speaker 5: and los A. Peterson, resurrecting stuff Fisher Black did, and
Speaker 5: they're very good about saying Fisher did it first. That
Speaker 5: lower beta stocks. If you're famously a capital asset pricing
Speaker 5: model person, they're supposed to sell on average underperform higher
Speaker 5: beta stocks. That's the main output of that model. It
Speaker 5: doesn't work. I mean, it's one of the largest empirical
Speaker 5: failures ever. It doesn't work in any kind even outside
Speaker 5: of stocks people tested in other places. Therefore, it kind
Speaker 5: of makes low beta stocks a little bit of a
Speaker 5: free lunch because they are lower risk and they keep up.
Speaker 5: If you actually go read Graham and DoD, they're not
Speaker 5: just buying low multiples. They're much more holistic than that.
Speaker 5: You know, high quality companies that have a moat, that
Speaker 5: have some kind of margin of safety I think was
Speaker 5: the term they use. Margin of safety and looking for
Speaker 5: low risk doesn't sound so so different. So over time
Speaker 5: I've thought at least a core amount of what quants
Speaker 5: and academics, if you take them as a whole, are finding,
Speaker 5: is the full paneplyy of stuff that a Graham and
Speaker 5: DoD investor. We do it very differently. Again, we're betting
Speaker 5: on the concept working on average.
Speaker 3: They are using it.
Speaker 5: In a soft or a hard sense as a screen
Speaker 5: to look for candidates, and then they're trying to learn
Speaker 5: a lot about that situation. They're upside as if they
Speaker 5: learn a lot about that situation, they could be more
Speaker 5: reliable than my You know, hey, we could be wrong
Speaker 5: about AMC, and their downside is they better be right.
Speaker 5: The concept can work, and they can still lose if
Speaker 5: they're wrong about the specific So over time I've gotten
Speaker 5: a little less hubrious about this. I think quants caused
Speaker 5: the problem, by the way. I think when Gene fam
Speaker 5: and Ken French started looking at like price to book
Speaker 5: in the late eighties and early nineties, and there were
Speaker 5: other people who did it too, I'm just Chicago guy,
Speaker 5: so I'm going to just go with Fama and French.
Speaker 5: They did it best. In my very biased opinion. I
Speaker 5: don't think i'd have to go back and check, but
Speaker 5: I don't think the first few papers use the word
Speaker 5: value investing. Over time, low multiple investing in the quant
Speaker 5: world came to be called value investing, and in the
Speaker 5: Gramm and Dodd world they get kind of mad at that,
Speaker 5: and they'd be like, it's not value investing. There are
Speaker 5: plenty of low multiple companies that deserve to be low multiple,
Speaker 5: and there are plenty of high multiple companies that deserve it,
Speaker 5: and I think over time, the quantitative process agreed with them.
Speaker 3: More and more.
Speaker 5: So I still think it's a communication problem because they'll
Speaker 5: still talk about the value factor. In the quant world,
Speaker 5: that's just low multiples. And if a Gramm and Dodd
Speaker 5: gets mad or any old school active stock picker gets
Speaker 5: mad at that, I'll just say you're right, because value
Speaker 5: implies a more 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 plans and you have a less linear model,
Speaker 1: and it's like moving towards that.
Speaker 5: Anyway, if you look at machine learning, where either to
Speaker 5: construct factors. One of the best uses of mL we
Speaker 5: found is a subset of mL called natural language processing,
Speaker 5: where you take textual data and you try to say
Speaker 5: is this good news or bad news? Quants have kind
Speaker 5: of done this forever. You get transcript of an earnings call.
Speaker 5: And the old school way to do this for a
Speaker 5: long time was you count up good words and bad words,
Speaker 5: good phrases and bad phrases, so increasing plus one and
Speaker 5: you tech parse the whole thing, and sure, you guys
Speaker 5: immediately see the problem. If the actual sentence was massive
Speaker 5: embezzlement is increasing, then you were off on that plus one.
Speaker 5: Quantitative stuff can survive doing some horrifically stupid things in isolation,
Speaker 5: If fifty three percent of the time increasing is a
Speaker 5: good word, than forty percent of the time you were stupid,
Speaker 5: turns out natural language processing or NLP, if we want
Speaker 5: to sound like the cool kids. That is, taking that
Speaker 5: same data and training a mL model to say what
Speaker 5: predicts and what doesn't predict, and of course never gets
Speaker 5: near perfect at the end of the day, though we
Speaker 5: believe it does a lot better than the word count
Speaker 5: methods and is additive to a model. But importantly, and
Speaker 5: I think this was your point, Matt, it's not qualitatively different.
Speaker 5: We've looked at both price and fundamental momentum forever. Fundamental
Speaker 5: momentum the classic measures of things like our earnings being
Speaker 5: revised and you want the revision.
Speaker 3: You want the new news.
Speaker 5: Up faster or slower. Our earning surprise is coming in
Speaker 5: positive or negative. And this is this anchoring and adjustment
Speaker 5: idea that if that's good or bad news, you can
Speaker 5: make money trading on it. Because it's not fully incorporated
Speaker 5: in the stock price parsing and earnings call poorly as
Speaker 5: in the past or better now, we think of it
Speaker 5: as just another form of our good things happening in
Speaker 5: the new year term, and if so, they're probably under appreciated.
Speaker 5: So yes, I don't think it's changed dramatically. It's much
Speaker 5: 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 5: Yeah, as someone with four kids in or around college age,
Speaker 5: my wife and I and she's done a lot more
Speaker 5: of this, has spent a lot of time trying to
Speaker 5: figure out what careers will not be utterly destroyed by MLO,
Speaker 5: and it's not an easy question.
Speaker 3: A podcaster is probably a good one.
Speaker 5: For a while.
Speaker 3: They have those fakes, so it will all be podcasters.
Speaker 5: But yeah, it's another example. I always say, these useless
Speaker 5: statements like them sufficiently long horizon, we're all replaced by
Speaker 5: machine learning.
Speaker 3: It's a question of what.
Speaker 5: We're getting way too philosophical, but the transition to that
Speaker 5: can be very painful and weird. But a world of
Speaker 5: abundance and leisure. Maybe our horrible fate in the long term,
Speaker 5: I'll take it.
Speaker 6: 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 5: Which is I will say he may be one of
Speaker 5: 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 nonlinear AI model, and one almost
Speaker 1: throwaway sentence in the pieces like this, like simplified AI
Speaker 1: model that he built for like illustrative purposes, lowered its
Speaker 1: risk before fourteen of the last fifteen recessions. And I
Speaker 1: always thought the naive like the best way to invest
Speaker 1: would be just market timing, just like you know, have
Speaker 1: all your money in the stock market before the market
Speaker 1: goes up and not before it goes down, if you
Speaker 1: can do it. My impression is that like respectable headgeplot managers,
Speaker 1: respectable clime managers, respectable academics say the hardest thing in
Speaker 1: the world is market timing, and like no one claims
Speaker 1: to get alpha from at and it's not a thing.
Speaker 1: Is that changed, and is that changed due to like
Speaker 1: machine learning?
Speaker 5: It has changed a big Do we do trend following
Speaker 5: on macro assets old school CTA stuff. We think we've
Speaker 5: made a new school by incorporating fundamental momentum by doing
Speaker 5: a lot of more esoteric market so we think even
Speaker 5: that's had a march of progress to it, But all
Speaker 5: else equal. I wrote my dissertation on momentum in individual
Speaker 5: stocks for some reason that I cannot explain. If you're
Speaker 5: using past returns to predict the future, just what's going
Speaker 5: up will keep going up, and vice versa to pick
Speaker 5: individual stocks. The whole industry calls momentum. And if you
Speaker 5: think markets tend to keep going in the same direction,
Speaker 5: everyone calls it trend falling.
Speaker 3: Same thing. Starting in I think two thousand and eight,
Speaker 3: we started.
Speaker 5: We always had it in our macro models, but we
Speaker 5: started formally offering separate trend falling products.
Speaker 3: It doesn't take hero bets on anyone market.
Speaker 5: It's not Gazarelli selling all the stocks a minute before
Speaker 5: October nineteenth of eighty seven. I'm dating myself. My wife's
Speaker 5: birthday is October nineteenth, which always always gets a little amused.
Speaker 3: Crash eighty seven meant to.
Speaker 1: Be Did you really crashed them together?
Speaker 5: Yeah, there have been some jokes over over time. So
Speaker 5: trend following it's essentially market timing, but it's highly diverse,
Speaker 5: many small bets. Whatever's been happening tends to keep happening.
Speaker 5: That's the main way we'll do market timing.
Speaker 1: You want to like your main equity fund is like sometimes.
Speaker 5: That that was kind of a toy model to illustraate
Speaker 5: a point. I know we're not taking a lot of
Speaker 5: risks on that model. Market timing I still think is
Speaker 5: quite hard. Might there be advances in it in the future, absolutely,
Speaker 5: but are we taking significant risk in it now aside
Speaker 5: from trend following, not really.
Speaker 1: You probably sim in 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 5: Oh, there's so much to talk about here. First, there
Speaker 5: are a lot of reasons we do it. One is
Speaker 5: just personal consumption. We grew up in this world. We
Speaker 5: liked being part of it. We were interested in this
Speaker 5: stuff in a purely academic sense before we got seduced
Speaker 5: into making money.
Speaker 3: I mean, I.
Speaker 1: Would like pause it that you think there is a
Speaker 1: value to employing the finance PhDs to like build your models,
Speaker 1: and the way to attract fancy finance PhDs is to
Speaker 1: offer them the most academia like possible working.
Speaker 5: And to letting them publish. There are people, including our
Speaker 5: two Yale professors. I don't don't know if would have
Speaker 5: come take you are if we said you can never
Speaker 5: write about this. We do a crass calculation, though, if
Speaker 5: we think there is something that we are relatively unique
Speaker 5: on entirely unique, or a very small handful of people
Speaker 5: know this, we won't publish it. The optimal the optimal
Speaker 5: time to publish a paper is after you've made money
Speaker 5: from something for eleven years and an hour and a
Speaker 5: half before someone else is going to publish the paper,
Speaker 5: and we cannot get that right. I can think of
Speaker 5: one example of momentum in factor. It's the fact that
Speaker 5: factors themselves, like value, profitability, also exhibit momentum as something
Speaker 5: that we've traded on for many years that we've always
Speaker 5: refrained from writing a paper on because no one else has.
Speaker 5: And again, we knew we couldn't be the only people
Speaker 5: who do this, but we didn't think the cat was
Speaker 5: out of the bag. And then someone else wrote the paper.
Speaker 5: And I'm sure we've done this to other people too,
Speaker 5: because you never know what they're doing internally. So life,
Speaker 5: you know, you do it long enough, life works out
Speaker 5: kind of fair, but that.
Speaker 3: 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.
Speaker 5: You could call it childish, but it's human. It's it's
Speaker 5: 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.
Speaker 5: So far, it's still worked wonderfully. The trend is still
Speaker 5: your friend when it comes to two factors, but it
Speaker 5: would be a fireable offense for make you are to
Speaker 5: publish something that we thought was truly proprietary.
Speaker 3: Let me give you my favorite example of this, because
Speaker 3: it came up recently.
Speaker 5: One of the things that we've gotten into in a
Speaker 5: fairly big way, as have some other quants, is what's
Speaker 5: called alternative data. Those are new data sets that put
Speaker 5: people put together with sweat equity. The classic example and
Speaker 5: the only one. And this is the point that I'm
Speaker 5: allowed to talk about our credit card receiver data were
Speaker 5: a credit cards because that's been discussed.
Speaker 3: A million times.
Speaker 1: Anative there.
Speaker 5: Yeah, yeah, Well that's the point is this stuff is arbitrageble.
Speaker 3: I mean, it can go away.
Speaker 5: Value, be it the narrow Quan sense of value or
Speaker 5: the more broad gram and Odd sense of value, is
Speaker 5: trying to take advantage of I think basic human nature.
Speaker 5: I think spreads being cheap and expensive are wider still
Speaker 5: than the historical average, not tighter. I don't think there's
Speaker 5: a lot of evidence that so much capital is in
Speaker 5: that that.
Speaker 3: It's making you go away.
Speaker 5: If you get a short term information advantage because you
Speaker 5: have put the time in or paid someone who's put
Speaker 5: the time in to create a new data set that
Speaker 5: other people don't have. They're going to have it eventually.
Speaker 5: So I am talking to the Australian Financial Review. I
Speaker 5: don't even know if we had discussed alternative data before him,
Speaker 5: but he asked me about it, and I said to
Speaker 5: him that our head of stock selection, a fellow named
Speaker 5: Andrea Ferzini, has asked me, I might have said, told me,
Speaker 5: but has asked me not to talk.
Speaker 3: About these things.
Speaker 5: There are things I'll talk about, but there are things
Speaker 5: we think are you know, overused word, but our true
Speaker 5: alpha that the world doesn't know, and I think it's reasonable,
Speaker 5: So I really can't talk about him. He writes the
Speaker 5: article and what it says is mostly that, but instead
Speaker 5: of saying he's asked me not to talk about it,
Speaker 5: it says Andrea Ferzini won't tell me what we're doing
Speaker 5: in alternative data, which it has a slightly different connotation,
Speaker 5: and that's a connotation of adult old man, don't worry
Speaker 5: about it.
Speaker 3: We're doing some stuff here.
Speaker 5: So again it's an example where there certainly are things
Speaker 5: we won't talk about.
Speaker 1: Is that ever true? Are you like most cutting edge
Speaker 1: machine learning people like that? Or are that or do
Speaker 1: you read all the papers.
Speaker 5: I read most of the papers. I don't read all
Speaker 5: of the papers. I at least skim all the papers.
Speaker 5: I know the gist. We do a lot of different
Speaker 5: things at this point. Once you're a quant you want
Speaker 5: more factors and you want to trade it in more places.
Speaker 5: And I will admit that the week doesn't go by
Speaker 5: where I don't and ask someone for a review of
Speaker 5: what we're doing. Somewhere at some point I approved it,
Speaker 5: so Pastcliff had some understanding. Yeah, but also just you know,
Speaker 5: some of this is pretty decent math, and the old
Speaker 5: mathematicians who their best work in their twenties, I can
Speaker 5: tell you I'm probably still decent in math, but I'm
Speaker 5: not what I was when I was twenty two. Wisdom
Speaker 5: has hopefully replaced some of mathematical ability because.
Speaker 1: The personnel at AQR different from a like I don't
Speaker 1: know who you think of as like quantity competitors, but like,
Speaker 1: my impression is that like you have many more finance
Speaker 1: PhDs than like other places that might have more like
Speaker 1: 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.
Speaker 5: There's going to be a correlation that the closer you
Speaker 5: get to the high frequency world, the more you're going
Speaker 5: to be more in the pure math realm. My very
Speaker 5: tortured analogy is quantum mechanics versus Newtonian physics. When you
Speaker 5: get into the high frequency world, first you have a
Speaker 5: ton of data. By nature, you just have a lot
Speaker 5: more instances.
Speaker 3: And a lot less theory.
Speaker 5: So turning yourself over to the data more so than
Speaker 5: having an economic rationale is far more rational, and that
Speaker 5: becomes a more mathematical exercise.
Speaker 3: We tend to think in our long.
Speaker 5: Seven hundred short seven hundred stock portfolios, average holding period
Speaker 5: is maybe nine months, maybe closer a year.
Speaker 3: Times. That's nowhere near high frequency. Yeah, high frequent. Two.
Speaker 5: When I wrote my dissertation and I did have this
Speaker 5: in there was a monthly contrarian strategy. Now we're talking,
Speaker 5: you know, sub seconds kind of thing. I think when
Speaker 5: you're gonna have a medium holding period strategy, as I
Speaker 5: think of us, we're not Warren Buffett, but we're not HFT.
Speaker 5: Even the machine learning stuff needs some economics too. You
Speaker 5: simply don't have enough data and there's too much of
Speaker 5: a dimensionality even with Brian's virtue of complexity, you need
Speaker 5: to give it some structure or else it's going to
Speaker 5: overfit and go mad. So I think think that's the reason,
Speaker 5: not saying we'll never do something in a higher frequency world,
Speaker 5: in which case we'd probably have to shift more. But
Speaker 5: in our world being a mathematician, being an excellent programmer,
Speaker 5: but also having the economics behind it, it's kind of
Speaker 5: 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 like
Speaker 1: they come to it pure, and I feel that you
Speaker 1: are very much like people who have math shops, but
Speaker 1: like have economic intuition and think about it as an economy.
Speaker 5: I think that's accurate. I do have a couple of
Speaker 5: Renaissance observations.
Speaker 1: Okay.
Speaker 5: One of my favorite questions people asked me was howd
Speaker 5: they do it? And I love that question because I
Speaker 5: get to respond. So your hypothesis is, I know how
Speaker 5: they took a few billion dollars and still take a
Speaker 5: few billion dollars out of the market to share among
Speaker 5: a relevant tiny group of people every year would apparently
Speaker 5: very low risk, and I choose not to I have
Speaker 5: some inklings of the general whether they've worked on. My
Speaker 5: biggest guess is that they were ten fifteen years ahead
Speaker 5: of everyone else on most of this stuff and are
Speaker 5: just have developed more sophisticated systems over time. I think
Speaker 5: natural language processing they were very early on.
Speaker 1: It came from like from.
Speaker 3: A few other things.
Speaker 5: John Leu and I, my one of my founding partners,
Speaker 5: wrote when Fama and Schiller shared the Nobel Prize, we
Speaker 5: wrote a whole overview of market efficiency and the debate
Speaker 5: about it, and I brought them up as an example.
Speaker 5: The Medallion Fund has almost nothing to say about market efficiency.
Speaker 5: It says, these guys can extract a toll on the
Speaker 5: market with reliable consistency, But in terms of market size
Speaker 5: it's a giant number for a few people to make
Speaker 5: each year. It's a tiny number in terms of whether
Speaker 5: the markets are efficient, and apparently the rest of us
Speaker 5: can't do it quite as well as them. They're the
Speaker 5: goat when it comes to this.
Speaker 1: Yeah, I think of like, you know, like Jane Street
Speaker 1: can rely excite somebody from the market, and I think
Speaker 1: of that as like a few first service ye, like
Speaker 1: a market maker, rather than being like a predictor of
Speaker 1: asset precesce.
Speaker 5: I'm gonna guess, and again it's a purely guess that
Speaker 5: not all of it, but a fair amount of Renaissance
Speaker 5: 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 5: And the constraint on those things is typically capacity. Now
Speaker 5: it's a ton of money for those firms. They're amazing firms,
Speaker 5: but give you an example of Renaissance. I've complimented them
Speaker 5: like crazy, so hopefully they won't be mad at me
Speaker 5: for this.
Speaker 3: One last comment.
Speaker 5: Everyone talks about the Medallion Fund, but no one's allowed
Speaker 5: to invest in that. They keep that for themselves. Another
Speaker 5: favorite question I get is this doesn't happen much anymore,
Speaker 5: but every once in a while I would get an
Speaker 5: investor saying, so, Medallion Fund better than you guys. I'd
Speaker 5: be like, oh, hell yes, but they won't take your
Speaker 5: money or my money, and I think we're pretty darn
Speaker 5: good and we will. So why is that relevant? But
Speaker 5: Renaissance also runs a fair amount of money in more
Speaker 5: open institutional products where they look very good. I'm not
Speaker 5: gonna to ride them at all, but they don't look
Speaker 5: better than us. They look like really good, solid, regular quants.
Speaker 5: So what they cannot do, I think it's kind of obvious,
Speaker 5: is take the medallion process and scale it up many,
Speaker 5: many times bigger. They've discovered a way, as you put it,
Speaker 5: to just take a certain amount out to provide a service.
Speaker 5: I called it taking a toll, and that's amazing. But
Speaker 5: they've not discovered a way to do that at institutional scale,
Speaker 5: which thank god, because that leaves something for the rest
Speaker 5: of 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 5: Well forgetting which one you think is really going on?
Speaker 5: They do have different characteristics The positive behind a risk
Speaker 5: premium is you may be able to get it forever
Speaker 5: because it's rational that you get it. The negative behind
Speaker 5: it is it's a risk premium. You have to figure
Speaker 5: out why. If the risk premium is most of the
Speaker 5: time this is fine, but it has depression risk. It's
Speaker 5: going to do particularly bad in a depression. Well, that's
Speaker 5: not a very pleasant risk to have. You may get
Speaker 5: paid for doing that. The positive of behavioral is it's
Speaker 5: essentially over the long term. Over the short term it
Speaker 5: can be very very painful lunch because the noise work.
Speaker 5: But if on average it works long term, it's a
Speaker 5: free lunch and that you're not taking additional systematic risk.
Speaker 3: The negative is it can go away.
Speaker 5: It can be arbitraged away if that ra stops being made,
Speaker 5: If too much capital chases it, it can be arbitraged away.
Speaker 5: It's hard to arbitrage some of them away, like basic valuation.
Speaker 5: It's very easy to arbitrage some others, like alternative data,
Speaker 5: where the point is to be quicker to getting a
Speaker 5: data set and to trading the day to set. I
Speaker 5: will say this, and I hope teen Fama isn't listening.
Speaker 5: I was probably seventy five twenty five. For risk guy
Speaker 5: thirty years ago, I'm probably seventy five twenty five.
Speaker 1: A behavioral is that GameStop or something not?
Speaker 5: Well, the GameStop maybe the unfairly extreme example. It's not
Speaker 5: fair to pick the most extreme. Craziness is to make
Speaker 5: your point, But yeah, it's real life experience. It's watching
Speaker 5: the spread between cheap and expensive stocks in ninety nine
Speaker 5: two thousand.
Speaker 3: We started our firm in late ninety.
Speaker 5: Eight, so right before the real crazy part of what's
Speaker 5: called now the dot com bubble, the spread between how
Speaker 5: we define cheap and expensive, either just using quant multiples
Speaker 5: or adjusting for forms of growth, set records. We had
Speaker 5: fifty seventy five years of data and we lived through it,
Speaker 5: blowing past those. Then after that, I'm like, all right,
Speaker 5: that was a once in fifty year event.
Speaker 3: We survived it. We ended up being right.
Speaker 5: We ended up making money round trip that was excruciating
Speaker 5: on the first leg. If you ask me back then,
Speaker 5: am I ever going to see that in my career again?
Speaker 5: I hope I'd be smart enough not to say never.
Speaker 5: None of us in our field, yours or mine, should
Speaker 5: say never. Markets are pretty hard things to say never about.
Speaker 5: But I think I would have said it's highly unlikely.
Speaker 5: For one, it was literally the most extreme event in
Speaker 5: at least fifty years. Second, the question presupposes I and
Speaker 5: people like me will still be around. Unpresumably, if we're
Speaker 5: still around, we're in more supervisory and authoritative positions. So
Speaker 5: how's it going to happen again? And then it happened
Speaker 5: again almost exactly twenty years later, from twenty eighteen through
Speaker 5: twenty twenty. You can point to COVID. It went absolutely
Speaker 5: mad during COVID. You guys remember for the six month
Speaker 5: period after COVID when the only two stocks you're supposed
Speaker 5: to own were Peloton and Tesla. One of them has
Speaker 5: still worked out mostly for you.
Speaker 3: One of them did not.
Speaker 5: But even before COVID, though you cannot pin it on COVID,
Speaker 5: by late nineteen early twenty we were approaching tech bubble levels,
Speaker 5: which again had not been seeing the prior fifty years before.
Speaker 5: The tech bubble again, painful period for us, again made
Speaker 5: money round trip. I'm going to brag about that we
Speaker 5: survived it.
Speaker 3: I think we're.
Speaker 5: Stronger than we were beforehand. But if the first one
Speaker 5: didn't make you start to go this behavioral stuff may
Speaker 5: be real and maybe bigger than it.
Speaker 3: Used to be. Yeah, and I wrote a piece.
Speaker 5: Called the less efficient market hypothesis markets getting less efficient.
Speaker 5: That may be true, but I think it's more accurate
Speaker 5: to say there what my evidence is there prone to
Speaker 5: some extreme bouts of craziness on occasion. It's not quite
Speaker 5: the same as in steady state always being less efficient,
Speaker 5: but probably some of both. But I do think that
Speaker 5: has happened. I probably was not giving behavioral enough credit
Speaker 5: early on, and I think it's gotten to be a
Speaker 5: 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
Speaker 1: do too, but like it's like, you know, it's maybe
Speaker 1: not like the biggest, you know, dollar, the test LIS's
Speaker 1: pretty big. It does seem like a thing that is
Speaker 1: regularly happening is that people have a taste for stocks
Speaker 1: beyond any rational or irrational calculation about how we'll fight
Speaker 1: their portfolio.
Speaker 5: Well, particularly the whole meme world. Meme coins are clearly
Speaker 5: a 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.
Speaker 5: Even writing it, I think we went a little too
Speaker 5: far because I don't think these people, we kind of
Speaker 5: write it like they're consciously doing it, like I just
Speaker 5: love owning these I know I'm gonna lose money. I
Speaker 5: think they kind of end up they resolve cognitive dissonance
Speaker 5: by saying I'm gonna make money even if that's what's
Speaker 5: really 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 5: And now instead on meme investors. We were also given
Speaker 5: a little mild shot to some academics who kind of
Speaker 5: try to save market efficiency by saying it's more meta
Speaker 5: efficient if you count people just have tastes for this.
Speaker 5: That argument's been made. I think that's kind of a
Speaker 5: cop out. You know, if we've done market efficiency down
Speaker 5: to that, what do we actually even mean by market efficiency?
Speaker 5: If you go, I know, I'm gonna do terrible on this,
Speaker 5: but it's fun to me, that's functionally a fairly inefficient market.
Speaker 5: So we proposed in that piece which did not catch
Speaker 5: on that classically market efficiency and the testing of it
Speaker 5: has a joint hypothesis problem. You're testing whether markets are efficient,
Speaker 5: but you also need a model for how prices are set,
Speaker 5: and if that model for how prices are set includes
Speaker 5: 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, people have.
Speaker 2: 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.
Speaker 5: But it's also I think true if ultimately it's a
Speaker 5: bad investment in return sense that will happen eventually.
Speaker 3: I do think that time horizon and the extremes have
Speaker 3: lengthened a lot of the point of this way, Why
Speaker 3: will it happen eventually? Why will it happen eventually?
Speaker 5: Well, if you buy a company that continues to perform
Speaker 5: poorly and you paid a ton for it, I think
Speaker 5: there's only so long that can go on. I think
Speaker 5: 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 sety floor 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 5: Well, let me be clear, one short bitcoin with my
Speaker 5: worst enemies portfolio, and I one hundred percent sure I'll
Speaker 5: never say money is a little bit of magic. What
Speaker 5: becomes money is a little bit of magic. But I
Speaker 5: think most of the probability is eventually zero, but it
Speaker 5: may be a very long time arizon. I think what
Speaker 5: we've learned watching this stuff is that time horizon is lengthened.
Speaker 1: Okay.
Speaker 5: In my piece on this, I try to hypothesize, and
Speaker 5: I admit it's real opinionated hypothesizing. You cannot prove if
Speaker 5: I'm right that markets are somewhat less efficient. Why gets
Speaker 5: even harder. But one of my favorite explanations is an
Speaker 5: old man complaining about social media and twenty four to
Speaker 5: seven gamified trading. I don't think most people need a
Speaker 5: lot of convincing that this stuff has made, say, our
Speaker 5: politics worse, made us more in bubbles hate each other
Speaker 5: more when it was supposed to make us love each
Speaker 5: other more. Marcus are just voting mechanisms. I don't think
Speaker 5: it's any different than politics. So this notion that things
Speaker 5: get crazier and can go on for longer, and I
Speaker 5: have a very cynical view of your statement that this
Speaker 5: stuff might take fifty years. I think the more absolutely
Speaker 5: unsubstantiated by anything something is, the longer the craziness can
Speaker 5: 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.
Speaker 5: Cisco Systems great company, but was selling at a very
Speaker 5: stupid price, selling it about one hundred PE when the
Speaker 5: e was gigantic. You can have a small startup company
Speaker 5: it's growing super rapidly, sell at one hundred pee.
Speaker 3: One of the largest companies in the.
Speaker 5: World with some of the largest earnings in the world
Speaker 5: selling at one hundred pe. You needed some very heroic
Speaker 5: and I would argue near. I'll never say impossible, but
Speaker 5: near impossible assumptions even if the tech bubble didn't break
Speaker 5: in March of two thousand when it did, and by
Speaker 5: the way, I still don't know why it broke in
Speaker 5: March of two thousand and not a year earlier or
Speaker 5: a year later. Once you're well passed what I would
Speaker 5: consider rational saying you know exactly where but the time horizon,
Speaker 5: you can imagine that going on for when the growth
Speaker 5: is good, maybe even great, but not nearly enough to
Speaker 5: justify that price. It gets more and more obvious if
Speaker 5: something is based completely on air. One of the weird
Speaker 5: of down Matt's point that there's there's no arm to
Speaker 5: the upside, there's no LBO mechanism, and shorting it is
Speaker 5: just frankly too dangerous.
Speaker 3: It's a weird way to say.
Speaker 5: Sometimes people say markets are efficient because you can't make
Speaker 5: money from these things, and I'm like, it's another weird
Speaker 5: way to defend efficient markets to go they can be
Speaker 5: so friggin stupid that they're terrifying to make efficient. That
Speaker 5: may be totally true, but it also is a weird
Speaker 5: way to argue that markets are efficient.
Speaker 4: What does it mean for AQR if markets are less efficient.
Speaker 5: Well, any active management is an inherently arrogant act. You
Speaker 5: cannot tell the average person we should all be active managers,
Speaker 5: we should all have podcasts.
Speaker 7: But I agree, So it's it's top to believe you
Speaker 7: should be an active manager and to believe you're doing
Speaker 7: something good for your clients, you have to believe two things.
Speaker 5: That you have alpha, and that you're not charging the
Speaker 5: full extent of that alpha through your fees. And we
Speaker 5: do believe that, and a lot of active management managers
Speaker 5: believe it. But it's an inherently arrogant act. It's consistent
Speaker 5: to say most people shouldn't do this, but we should,
Speaker 5: though the arrogance is obvious.
Speaker 1: What percent of your alpha should in your face?
Speaker 5: That is a super hard question. I've thought about writing
Speaker 5: about this at one point. It kind of depends on
Speaker 5: 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 5: That's what they can charge for their fees, not what
Speaker 5: they should charge. I don't think they'd ever admit them
Speaker 5: at ALGI, like Wilvers shall.
Speaker 1: Deef in their soul. They want one hundred and ten percent.
Speaker 5: It's a great question, but it's a hard question if
Speaker 5: your alpha is doing FOM and French price to book.
Speaker 5: By the way, I still think Farm and French are
Speaker 5: going to be right in the next thirty years. They
Speaker 5: haven't been right in a while, but spreads have gotten
Speaker 5: wider and wider, and that's been a wind in their face.
Speaker 5: I wrote a piece on this saying the long run
Speaker 5: is lying to you, saying that x the spread widening
Speaker 5: values to the simple Farm of French value has delivered alpha.
Speaker 3: It's just lost on the repricing.
Speaker 5: But what you can actually charge for doing price to
Speaker 5: book should be very low, a very small fraction of
Speaker 5: the expected return.
Speaker 1: That's not alpha data, right, But.
Speaker 5: That's kind of what we're saying. Everything exists on a
Speaker 5: spectrum from one to the other. If you have discovered
Speaker 5: and built the database yourself, an alternative data source that
Speaker 5: you have built. This is extreme and I can't think
Speaker 5: of an example at AQR that fits this. But you
Speaker 5: can charge nearly one hundred percent of that alpha because
Speaker 5: what they're getting is still absolutely unrelated to everything else
Speaker 5: what they're doing. There is some deminimous notion that if
Speaker 5: you charge ninety nine percent, maybe no one would bother
Speaker 5: to do it. But you can charge a large fraction
Speaker 5: of the alpha because what you're delivering is still ultimately
Speaker 5: net returns that you can not get elsewhere that aren't
Speaker 5: 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 5: Anchored norms is another way of saying, what are other
Speaker 5: people charge or.
Speaker 3: In the ballpark of what you're doing?
Speaker 5: So of course that matters, right if you are way
Speaker 5: off the anchor, way off on the high side, no
Speaker 5: one should invest with you.
Speaker 3: If you're way off on 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 5: Some of the block shops charge insane fees and have
Speaker 5: been very good, And that goes to that are they
Speaker 5: doing something unique?
Speaker 3: And I think to some extent they are.
Speaker 5: I think their problem becomes kind of in the direction
Speaker 5: of Medallion without going all the way. I don't think
Speaker 5: they've rebuilt medallion. Nothing is that, but they should charge
Speaker 5: a higher percent if they're doing something very unique, and
Speaker 5: they do. I have a very you can you know
Speaker 5: violin playing I think rosy view of how we think
Speaker 5: about fees. We're building a long term business. We think
Speaker 5: we have business value. We do not think we're just
Speaker 5: a hedge fund. We run a lot of traditional assets too,
Speaker 5: even the hedge funds.
Speaker 3: We were a big.
Speaker 5: Pioneer in doing the more obvious strategies and considerably lower fees,
Speaker 5: starting in merger, ARB and TREND. Following the way we
Speaker 5: broke into some of those as standalone products, not as
Speaker 5: part of our multistrats was charging less and saying, you know,
Speaker 5: this is real and it's good. But you know you
Speaker 5: can do one of every merger and make a fair
Speaker 5: amount of money. But that's not magic, and we shouldn't
Speaker 5: charge magic fees for it. But in that kind of kumbay,
Speaker 5: a big picture sense, charging fees such that clients are
Speaker 5: happy with the long term results is probably how you
Speaker 5: 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 5: Well, this is almost a mathematical certainty. Yeah, forget about
Speaker 5: ALTS for a second. You cannot tell me the average
Speaker 5: active portfolio beats the market after fees and costs.
Speaker 3: The average active adds up to the.
Speaker 5: Market because for every deviation one way, there's deviation the
Speaker 5: other way. When I said it to inherently an arrogant
Speaker 5: act to be an active manager, it means you think
Speaker 5: you got it, even though if you buy one of
Speaker 5: each you can't have it. I mean a subset of
Speaker 5: the market like could. But I think a lot of
Speaker 5: that still applies. It's inherently arrogant act. But we've been
Speaker 5: saying this for at least twenty four years. We wrote
Speaker 5: a paper, and yes, I started a lot of sentences
Speaker 5: with We wrote a paper in two thousand and one
Speaker 5: called do hedge funds Hedge where we took the known
Speaker 5: indices of hedge funds and we tried to take out
Speaker 5: just the market beta. You could argue for a more
Speaker 5: sophisticated risk model, and that could as usual and go
Speaker 5: down that rabbit hole. But we found that betas were.
Speaker 5: First of all, this is more mundane but statistically underestimated,
Speaker 5: partly because a lot of hedge funds do some stuff
Speaker 5: that is not of perfect liquidity.
Speaker 3: And this was a small.
Speaker 5: Early version of what I got into at the private
Speaker 5: world later on. But if something doesn't trade all the
Speaker 5: time and you try to estimate its correlation with the market,
Speaker 5: you will underestimate it because one great way to look
Speaker 5: uncorrelated is to have a three day leg, and when
Speaker 5: you trade, your returns will be off. So the betas
Speaker 5: were underestimated by earlier studies. Given the correct betas, there
Speaker 5: was pretty much no alpha to the hedge fund world.
Speaker 5: First of all, I was a lot younger than a
Speaker 5: lot less well known, so I cared Nowadays, I quite
Speaker 5: obviously court controversy. But back then I probably had ten
Speaker 5: famous managers call me and yell at me about that paper.
Speaker 3: Yeah. The first time, I was, of course obnoxious.
Speaker 5: They called and said, why did you write this? And
Speaker 5: I gave the obnoxious answer because we think it's true.
Speaker 5: Now it was apparently not an acceptable answer. I will
Speaker 5: only call out one person on the positive side, Richard Perry,
Speaker 5: famous hedge fund manager.
Speaker 3: He called me up.
Speaker 5: And I already been yelled at by a whole bunch
Speaker 5: of people. And so when I heard Richard Perry on
Speaker 5: the phone, people like Richard Perry didn't call people like
Speaker 5: me back then.
Speaker 3: He was big, I was small. So I'm like, I
Speaker 3: know what this is.
Speaker 5: He's just gonna yell at me. He gets on the phone,
Speaker 5: he goes, that paper you wrote.
Speaker 3: That's just correct.
Speaker 5: Good job, right, And I'm only telling that. I'm not
Speaker 5: giving the names. And I do remember him, of.
Speaker 3: Course, in a book somewhere. It's all I have a
Speaker 3: list of Vedettas and my BRAINSU.
Speaker 1: Wait, so you've been You've been making fun of private
Speaker 1: equity managers on some lines recently. Do you get calls
Speaker 1: from them?
Speaker 5: No, they're so fat and happy, they don't even care
Speaker 5: about me making fun. No, I get yelled at by
Speaker 5: some usually friends. I live in Greenwich connection, so you
Speaker 5: all hang out.
Speaker 1: The country club. They're like, I'm.
Speaker 5: Actually a country club, but the old often used in
Speaker 5: a very bad way. Some of my best friends are
Speaker 5: so I'm off the hook. I have some good friends
Speaker 5: who are private equity managers. Most of them can accept
Speaker 5: the you're right about the industry but not our firm,
Speaker 5: which is.
Speaker 3: Essentially what I'm saying. So I can't not being mean
Speaker 3: 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 5: Yes, that is my main criticism, which I think is
Speaker 5: quite obviously true.
Speaker 3: That's just me. Some people do disagree.
Speaker 5: The best disagreement I've heard, and I have some sympathy
Speaker 5: for this because I do not think markets are perfect,
Speaker 5: is we are right about the valuations. You are right
Speaker 5: that we move at a highly damped version of the market,
Speaker 5: but market moves too much.
Speaker 3: We are right.
Speaker 5: My response to that is, why don't we get to
Speaker 5: do that?
Speaker 1: It's fair?
Speaker 5: You know when we've had a tough year because the
Speaker 5: market's gone crazy and we were on the wrong side
Speaker 5: of that. I have to tell my clients we're down
Speaker 5: twelve percent. I don't get to tell my clients we're
Speaker 5: up based on where I'd mark the portfolio. So why
Speaker 5: one group? And by the way, they could market just
Speaker 5: like we market. They're brilliant at valuing companies and they
Speaker 5: can tell you where they could sell it for today.
Speaker 5: So it's like an institutional legal quirk that they get
Speaker 5: to do it one way and we have to do
Speaker 5: it another. Everyone can mark their portfolio at what they
Speaker 5: could sell it for today or what they think it's worth,
Speaker 5: and one side gets to do it one one gets
Speaker 5: to do the other. So that's a reasonable argument, but
Speaker 5: I still think it leads to an unreasonable conclusion. I
Speaker 5: will say ninety percent of my critique is about the
Speaker 5: volatility or the beta, is about saying these things are
Speaker 5: low risk.
Speaker 3: Ten percent is.
Speaker 5: About percent if not trailing future returns, because if I'm
Speaker 5: right about the volatility laundering, it has implications for returns
Speaker 5: going forward. If when David Swenson was pioneering, which is
Speaker 5: what he called his book, Private equity is part of
Speaker 5: an institutional endownment portfolio.
Speaker 3: He's quite clear. It's a lot of it. It's an
Speaker 3: ill liquidity premium that no one wants.
Speaker 5: Illiquidity. Everyone's scared of it. So if you're willing to
Speaker 5: do that, you get paid extra. If I'm right that
Speaker 5: people love the fact that they don't have to look
Speaker 5: at the volatility, that means illiquidity is no longer a bug.
Speaker 5: It is now a feature and very simple model for
Speaker 5: how expective returns are set. You get paid a higher
Speaker 5: expective return in something if you have to bear a
Speaker 5: bug nobody wants and you pay through a lower expector return.
Speaker 5: If you have a feature everyone wants, then you have
Speaker 5: to pay up for it. So the chance that that
Speaker 5: is going on going forward, I think is quite high.
Speaker 5: Whether it means there's no edge to private equity or
Speaker 5: a negative edge or a smaller positive edge can tell
Speaker 5: you that. But I do think if you think of
Speaker 5: risk ajusy return as numerator of return denominator of risk,
Speaker 5: I think my statements are mostly about the denominator, but
Speaker 5: they're ten percent now about the numerator.
Speaker 1: Going forward. You're in my concernami private equity, private markets
Speaker 1: by which is that it seems to me that like
Speaker 1: there is a ton of fee pressure in public markets
Speaker 1: and everyone has kind of like learned the gospel of
Speaker 1: like Bilow cost index funds and even charging for farma
Speaker 1: French factors is not a two and twenty business. And
Speaker 1: private markets because they're not indexible because it's harder to
Speaker 1: like extract factors because they're not liquid, don't have those problems,
Speaker 1: and you can charge two and twenty for a lot
Speaker 1: of private stuff. And so like it seems to me
Speaker 1: that there is a move to you put a lot
Speaker 1: of stuff that would have previously been public into private markets,
Speaker 1: and to say we can put privates into four oh
Speaker 1: one ks and there's a good economic grastionals we're putting
Speaker 1: private assets into four one case because you don't need liquidity.
Speaker 1: But there's also like this like really overwhelming if.
Speaker 8: There's a positive illiquidity premium. Yeah too right, sorry, go no.
Speaker 8: I just like you know, you've written for years ago
Speaker 8: like criticizing people for charging alpha fees for beta, and
Speaker 8: it seems to me that like the reprivatization on the
Speaker 8: market is a way to sneak some alpha fies onto beta.
Speaker 5: I think a tremendous amount of the private world is
Speaker 5: charging massive alpha fees for beta.
Speaker 3: I won't mince words about that.
Speaker 5: If they outperform or underperform on net after all these
Speaker 5: massive fees, and if performance going forward, it is tougher.
Speaker 5: And by the way, your point about the fee compression
Speaker 5: in the public world only makes my hypothesis that they
Speaker 5: won't beat the public world by the same amount going
Speaker 5: forward stronger. I think privates have a big function in
Speaker 5: the world. I don't think they're going away what you
Speaker 5: started out with me, what do you guys do for
Speaker 5: the world. There are things that are in between what
Speaker 5: should be public and what's mom and pop. But I
Speaker 5: think where we are now, I think a lot of
Speaker 5: institutions are giving up some amount of expected return for
Speaker 5: the ease of limit, of reducing their agency problem of
Speaker 5: sticking with something. Now, if they're going to be terrible
Speaker 5: and not stick with things, it might be rational to
Speaker 5: give up some return. But you can't double count and
Speaker 5: say we're making ourselves better investors giving up some expected
Speaker 5: return and oh yeah, our expected returns are going.
Speaker 1: To be higher.
Speaker 5: If that's the rationale, then you've accepted my argument, and
Speaker 5: I think you have to say, this is what we
Speaker 5: 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 wrappers. 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 5: I'm going to hedge this and say it very carefully.
Speaker 5: I think it's a terrible idea.
Speaker 6: Okay, go on.
Speaker 5: There are things that end up in retail in a
Speaker 5: very good way eventually, but we've done some of this
Speaker 5: when we introduce mutual funds. I can't say always going
Speaker 5: to retail is a bad thing. You can price it reasonably,
Speaker 5: you can say these are strategies you've never had before.
Speaker 5: This feels a little bit more like we've exhausted the institutions.
Speaker 5: I think I saw a number saying endown. It's like
Speaker 5: forty three percent for a number I can't verify. That's
Speaker 5: wildly specific, but that's the number I remember. So it
Speaker 5: has a feel of who else we're going to get
Speaker 5: to own this stuff.
Speaker 1: It seems so explicitly, and it's really wild.
Speaker 5: I think it's explicitly that it's one of these things
Speaker 5: that for the cynics, I don't think they'll ever be
Speaker 5: a satisfying moment where we get to say we were right.
Speaker 5: It'll just be somewhat worse over the next decade. It's
Speaker 5: not one of these things like a tech bubble in
Speaker 5: ninety nine two thousand that at least, I don't think
Speaker 5: there are scenarios where things get worse rapidly and secondary sales.
Speaker 5: We got close to some of that in the GFC.
Speaker 5: I was on some investment committees where we were talking
Speaker 5: we didn't do it, but we were talking about whether
Speaker 5: we would have to do that. So I'm not saying
Speaker 5: it's a chance of ugliness, but I think the meat
Speaker 5: of the probability is is just somewhat worse. But it
Speaker 5: does cause me some worry sadness to say, yeah, what
Speaker 5: we really need is to stick a liquids in four.
Speaker 3: 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.
Speaker 2: This is something we've spoken about before.
Speaker 4: I did well, was that like a light bulb moment
Speaker 4: or was that just you know, maybe people on your
Speaker 4: team wearing you down over time?
Speaker 3: It was more the ladder.
Speaker 5: Yeah, the ladder is the second one, right, I gotta
Speaker 5: think that through every time I tell that people this.
Speaker 5: I've said in a lot of public arenas I sell
Speaker 5: it to clients. I think I probably slowed us down
Speaker 5: by a couple of years in machine learning. I think
Speaker 5: it probably costs us some money because the stuff has
Speaker 5: worked pretty well. We've always described ourselves and we were
Speaker 5: getting into this a little bit earlier. As a blend
Speaker 5: of data, back tests are nice out of sample long
Speaker 5: periods are even nicer, but also theory.
Speaker 3: Theory can be a.
Speaker 5: Formal economic theory, but it can also just mean a
Speaker 5: common sense story where you think you understand why you're
Speaker 5: making money. And there's no way to say exactly what
Speaker 5: percentage of both. But I've often described it as an
Speaker 5: attempt to be fifty to fifty. We want something to
Speaker 5: make sense to us as economists and have strong data
Speaker 5: when you move into the machine learning world. I don't
Speaker 5: think you have to abandon theory, and this is something
Speaker 5: I think we do a little different than most I
Speaker 5: think there are some in the machine learning world to
Speaker 5: kind of throw theory out the window. We're still using
Speaker 5: common sense and theory to kind of limit the scope,
Speaker 5: but you are leaning more on the data. And again
Speaker 5: I'm making up these numbers. But if regular stuff is
Speaker 5: fifty to fifty, machine learning seventy five twenty five data
Speaker 5: even for us, that was uncomfortable for me.
Speaker 3: When you've been telling a story.
Speaker 5: For twenty five years and it's worked for you and
Speaker 5: your clients, it's not easy to move. I also think
Speaker 5: it's not improper for the role for the old man
Speaker 5: of the firm to go, let's.
Speaker 3: Just slow down.
Speaker 5: You guys come in here doing this with a Southern
Speaker 5: droll and your new fangled machine learning. No, that's the
Speaker 5: villainous rule.
Speaker 2: Oh no, I think it's kind of cool.
Speaker 5: Yeah, okay, so I actually can defend it as entirely appropriate.
Speaker 5: But yeah, I had to be convinced. It wasn't a
Speaker 5: light bulb moment. It was people like Brian Kelly, Andrea Ferzini,
Speaker 5: Laura Serb and all partners of mine presenting great results
Speaker 5: that made increase it and I did a lot more reading.
Speaker 3: I was probably I.
Speaker 5: Programmed in a programming language called LISP in the nineteen eighties.
Speaker 5: That was an early machine learning or AI language, not
Speaker 5: even machine learning, and then I didn't do anything about
Speaker 5: that for the next thirty years. So I think part
Speaker 5: of it was just me getting up to speed.
Speaker 1: Frankly, the thing I find compelling in the Kelly paper
Speaker 1: is like you seed like a sort of toy set
Speaker 1: of factors into like a ten thousand neuron model, And
Speaker 1: what he argues is basically you might get a better
Speaker 1: view of the actual like function that generates the results
Speaker 1: than if you just try to like use your economic
Speaker 1: intuition in a lineary aggression.
Speaker 3: 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, and.
Speaker 5: It's basically saying it's still a sin to only look
Speaker 5: for patterns. You still need some economics, but machine learning
Speaker 5: is better at balancing those trade offs. So the idea
Speaker 5: of throwing more at it when you have a better
Speaker 5: technique for that, you lean in that direction. I did
Speaker 5: have a better title, I think than him. I wanted
Speaker 5: him that's not better. His titles great pretty well, no,
Speaker 5: but I wanted to call it simply. Ackham was wrong.
Speaker 3: Okay, all right, and maybe not bad, but I still
Speaker 3: want 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 2: I'm Matt Levi and I'm Katie Grifeld.
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