Tracy Alloway (1:59)

Oh, we started doing it because we wanted to have a podcast and talk to interesting people. I think we were hashtag blessed that no one was listening for a very, very long time, which gave us a long Runway to figure things out. So we got lucky. That said, you know, 10 years, it is in fact a long time to be doing this. And a lot has changed in that period.

Joe Weisenthal (2:19)

A lot has changed in that period. Sometimes mind blowing. And we've talked about this before, for sure, but the things that we were covering is capital and news at the time are now capital H history. And it's like these things that are we sort of take for granted. Everyone was there. It's like no children. Let us tell you what it was like in the old days when people were worried the world was going to come to an End because, you know, Greece is sovereign debt and all this stuff that we just sort of part of the landscape is people don't remember it.

Tracy Alloway (2:48)

No. One of those things has to be the idea of value or fundamental investing. Right. Like, let us tell you about the days when price actually mattered, had a limit to what investors would pile into.

Joe Weisenthal (2:59)

Yeah, that's exactly right. Let us talk about the days when people used to Talk. Talk about PE ratios. And this stock. Oh, it's at a 25 PE. We better sell it and buy the stock at a 15 PE or whatever. Yes. That feels quaint. Maybe it'll be back there one day. But for now, given how many things in the market seem to be the Graham and Dodd kind of stuff, feels a little old.

Tracy Alloway (3:20)

A little old fashioned.

Joe Weisenthal (3:21)

A little old fashioned.

Tracy Alloway (3:22)

Yeah. Okay, so one of the big themes that has emerged in the 10 years that we've been doing this podcast is everyone seems to have grown more stupid. I would say hopefully that's not related. It's not like a correlation thing.

Joe Weisenthal (3:33)

It might.

Tracy Alloway (3:34)

Mm. It could be. All right. But you know, we have all this gamification of investing. People betting on lines going up.

Joe Weisenthal (3:41)

Yeah.

Tracy Alloway (3:41)

Or down. People betting on random meme coins, things like that. And I think, you know, we talk about it a lot on the podcast, but this is actually a fundamental shift in the market. If you think about the market as something that's supposed to be about capital allocation.

Joe Weisenthal (3:55)

Yeah.

Tracy Alloway (3:55)

Alignment of incentives. People are investing in something because they think it's going to be profitable in the future at the right price. And now people are just sort of piling into stuff because other people are doing it. And again, line go up.

Joe Weisenthal (4:08)

Deep down, I still believe that the value of a stock should reflect the net present value of all. I know you're an EMH guy, but I've. It's been a little bit hard with some of these things. And you know, the other thing too that is sort of change is that like half of our episodes these days are kind of AI related in some way. And so I think there's a lot of interesting stuff going on, particularly at the intersection of tech and applying tech to both investing in tech, but then the application of tech to investing and so forth to. So, yes, much has changed.

Tracy Alloway (4:38)

A lot has changed. And we have the perfect guest to talk about how everyone has grown more stupid over time. We're going to be speaking with someone we've wanted on the show for a really, really long time. I am very excited about this. It's Cliff Asness, the co Founder and CIO of aqr. Thank you so much for coming on. All thoughts.

Cliff Asness (4:55)

Thank you for having me.

Tracy Alloway (4:56)

What's it like to be a rational person living in an irrational world? Yeah, you've been doing it for a long time.

Cliff Asness (5:02)

Well, a rational person is not always how I'm described. But there's rational investing and then there's rat conduct in your personal life. So let's just distinguish those. You guys in your intro said like it's a little, it's a little scary for the next hour because you said like a third of the things I want to say.

Tracy Alloway (5:18)

Oh, shoot.

Cliff Asness (5:19)

Okay. I wrote a rather gigantic piece in the Journal of Portfolio Management. They were having a 50th anniversary. None of us were quite old enough to have been there in the first issue. But they were looking for the old guys to write kind of retrospectives. And the piece I wrote was called the Less Efficient Market. Yeah, hypothesis. Now I think you guys probably know this, but my dissertation advisor was a little known guy named Eugene Fama. We've heard of him. I was his TA for two years. I grew up in emh. I love that you guys just say EMH and your audience knows.

Joe Weisenthal (5:51)

Yeah.

Cliff Asness (5:51)

What you're talking about. That is not the norm for me when I talk about these things. I was not a perfect efficient marketer even back then. Neither is Gene, by the way. Gene's not a zealot about third week of class. I know this because I took the class three times. I didn't fail. But as the ta, I sat through it three full years. And like the third week, he always tells the class, markets are almost certainly not perfectly efficient because Gene's a brilliant guy and recognizes that perfection is a really stupid idea.

Tracy Alloway (6:22)

He's been on the show, by the way.

Cliff Asness (6:23)

Oh, I didn't know that. And I don't know if that came up. But he's always very honest about that. He probably thinks they're considerably more efficient than I do these days. And I think, I probably think it's. They're more efficient than maybe the active average retail trader. But I wrote a dissertation for him on the success of price momentum. That is not a very Gene Fama dissertation. A Gene Fama dissertation is I've studied price momentum and it loses gobs of money and these idiots on Wall street do it anyway. That's kind of a fishing markets, look how silly they are. And he was great about it. I remember I kind of mumbled, I'm like, I want to write a dissertation on price momentum. And by the way, I find it works very well. What was that, Cliff? It works very well. And he said, if it's in the data, write the paper. So I've drifted at least a little bit further from efficient markets, even way back then. Now, price momentum, I'll do a lot of segues. You're going to have to stop.

Tracy Alloway (7:15)

No, that's fine.

Cliff Asness (7:16)

I do parentheticals within parentheticals. Price momentum is often thought of as this index of irrationality. It can work for two different reasons. It can work because of, yes, feedback loops. Chasing line goes up, just like you said before, and people pour in, which isn't very connected to reality. It's just chasing returns. But it can also work because of what the behavioral finance people would call under reaction. News comes out, that should move the price by so and so on average. We have found, I say we. It's the royal we of academia and private researchers that the price moves the right direction but doesn't move all the way. So if you trade on that, there's still a little bit to go. That's a quant thing. You wouldn't want to bet on one stock that way. But if on average that happens, and you could do that through observing the price or the fundamentals, there's usually a little bit more to go. So it's not always irrational momentum. But here's what I observed in the very beginning of AQR. AQR launched in 1998 after we had a fabulous run at Goldman Sachs. There's survivorship bias in this. You don't get to start your own billion dollar hedge fund unless you have a fabulous run at Goldman Sachs or something equivalent. We had a good first month. You know how the story's gonna go when you say you had a good first month, right? And then that first month, by the way, was August of 1998, when the S&P was down 20% on the Russian debt crisis. And we're doing high fives. Like we say we're market neutral and we're up a little bit in a crash. And it was my first of many lessons never to high five in this business. When you fully retire and divest, you get one high five.

Tracy Alloway (8:55)

Well, no, it's the end when you do the high five.

Cliff Asness (8:57)

I'm a quant who is also superstitious, which if that's a contradiction, what the heck? But the next 18 months was the crescendo of the famous.com or tech bubble. And that was not kind to us. It was particularly not kind because we decided to start with A extremely aggressive market neutral fund. So momentum helped, as you can imagine in a bubble, but value was just destroyed. And that was most of the model back then. Things have really broadened out. We hit spreads between cheap and expensive. This is something we invented at the time and now a lot of people do. People said sort stocks on valuations, go long the cheap, short the expensive. But the very obvious question of how cheap and how expensive are they sometimes pretty tightly clustered, are they sometimes wider? Does that mean they're better or worse going forward? Was not asked before. So we invented this measure and the spread between cheap and expensive for 50 years had looked like a well behaved series. It moved around a fair amount, but. And then I'm drawing on my hand if no one can see the video.

Tracy Alloway (9:59)

Yeah, we should mention this is an audio medium.

Cliff Asness (10:02)

And then in late 99, 2000 went to just way wider than anything ever seen for 50 plus years. We also showed that historically those were better times. You never saw that before, but when it was wider or better times for value, we stuck with it. We made money round trip. Life was good. If you had asked me after the round trip, which was harrowing, you know, you know, even if we love our process, you never want to start a business with poor returns. If you asked me at the end of that, which is probably a couple years later, 2003, do you think you're ever going to see that in your career again? No one asked, thank God, because I think I would have gotten it wrong. But I think I would have said, oh, probably not, hopefully. I wouldn't say definitely. No one who does what any of us do for a living should say definitely. That's a bad word in markets, 40%.

Tracy Alloway (10:49)

Is the favored term, right? There's always a 40% chance.

Cliff Asness (10:52)

But a, it was the craziest thing numerically in 50 plus years. B, the question presupposes, it's built in that I and people of my cohort will still be around, right? And we'll probably be closer to in charge. So how's it gonna happen again? And then it happened again even before COVID By late 2019, that spread between cheap and expensive was approaching.com extremes. And then it blew past it in Covid. It went to what I in a geeky math joke that no one ever gets called 125th percentile. There is no 125th percentile. It's just the new hundredth percentile. I'm trying to convey that it went further and we survived that one too. We suffered somewhat and then we Made more than all of it back round trip good. Most of the time. We really don't look like value investors by the way. Only in extreme bubbles do we have seem to have that property. And I think it's smaller now than it used to be. The last five years have been quite strong for us and it's not been a very good value market.

Joe Weisenthal (11:50)

There's so many questions that I have that I want to ask that are sort of embedded or related to your answer. I'm going to ask actually just like a very narrow question, sort of skip ahead in something though. You make this spread between the most expensive and the cheapest stocks.

Cliff Asness (12:06)

Yes.

Joe Weisenthal (12:07)

How much easier it is to simply compile that spread is it today versus the technology that you are working with when you're starting your career.

Cliff Asness (12:17)

For that I gotta disappoint you and say not that much easier. We had the databases. A lot of technological advancement is about speed, okay. And new data sets, what, what quants will call alternative data is something we're very into. But the classic data, if you're looking for price to sales ratios. I'm old but we had telephones in Bloomberg's. But that does bring us to me observing these two episodes. And I stepped back and I asked a question what the hell happened? Why did we see something crazier than 50 years? And then why did it happen again? And that led to this paper, the less efficient market hypothesis. I do believe that markets have shown, and I think I have some good guesses as to why, that they are more susceptible to bouts of crazy than they used to be.

Joe Weisenthal (13:07)

Just before going further, one quick definitional question. Efficient markets. Because one way you could define whether a market is efficient is are these securities disconnected from what you would say the net present value of the all future free cash flows. Another way that I often think about it, but I'm no quant, is are there obvious opportunities for the active manager to make money? Just for our purposes here, how do you define market efficiency?

Cliff Asness (13:33)

Much closer to the first one.

Joe Weisenthal (13:34)

Okay.

Cliff Asness (13:35)

The practical question of can you make money from these is if there are big deviations from fair value. And this relates to some stuff I did in a less efficient market hypothesis. If there are big deviations, the classic and you'll notice a giant caveat. If you can stick with your position, not a small thing, you will make money. There are opportunities. And in fact I think a less efficient market in that sense, in your present value sense almost has to deliver bigger opportunities for people who can stick with it. It also makes it Considerably harder to stick with because the extremes you have to live through and the length of time those extremes can go on. For one thing, that I'm dying for an academic to take me up on this. I'm too old to do the math on this, but I've never seen a model that looks at pain, disutility, the negative of losing money in terms of how long you've lost money for, not just magnitude. Right. And in real life, I can tell you a drawdown that is one and a half times bigger but was six months instead of three years is ridiculously easier to live through. So the bigger disconnect from reality. Again, I haven't even told you why I think it's going on. But if I'm right that we have these bouts of it, it's not necessarily everyday things are crazy, but these bouts of it is a two edged sword. It's a bigger opportunity for people who can stick with it and it's harder to do. And I find that. Not that what I think is fair is particularly relevant, but I find that really fair. Harder to do, but more lucrative if you can do it. I don't think the efficient market idea of buying what is fundamentally mismatched against its future cash flows, adjusted for its risk, forecasting those cash flows, what risk really means, we still have some open issues there.

Joe Weisenthal (15:19)

Yeah.

Cliff Asness (15:20)

But I don't think that will ever go away unless markets are perfectly efficient. But how easy it is to identify and how easy it is to stick with. I think crazy markets make it easier to identify what to do and harder to like that.

Tracy Alloway (15:35)

I do want to ask you why you think this is happening. But before we do, this is a very basic question. But sometimes the basic questions are the most interesting. But when you say, say it's painful to try to stay rational during these bouts of irrationality. Why is that exactly? Because I get that there are funding costs, I get that there are carrying costs. But on the other hand, you're a big hedge fund with deep pockets. This is presumably what investors are paying you to do. Is it just the emotional trauma or stress of having to explain to everyone why you're taking the position that you have when it's not paying off yet?

Cliff Asness (16:09)

That is a big part of it. I have a running fight with one of my co founders who never gets upset. I'm always upset, but I'm more upset when we're losing money.

Tracy Alloway (16:17)

This is sort of our dynamic. I have to. I get more upset than Joe Jobs.

Cliff Asness (16:21)

Where he'll come in my office during a Bad period. And I'll be upset. It'll be a bad day and a bad period. He's like, why are you upset? And I'm like, well, because people are yelling at us. And I we're losing money. And he's like, but you're pretty sure we're gonna win, right? I'm like, yeah. He's like, and you have all your own money and your kids money in this, right? So you're not doing anything different. You wouldn't do that if you weren't. I'm like, yeah. He's like, so we're gonna win. It's just a question of when. Why do you care? And I look at him like he's from Mars and go, why do you not care? And we finally figured out it's kind of obvious that I talk to clients a lot more than he does. It's a lot easier to have that attitude when you're just sitting in your office. Also, we are not immune from this. Even if people love you and think you've done well for 25 years, you have a bad two years, you get redemptions, and sometimes they're of serious size. We fell by, like, half over about three years. And that's not fun. You have to shrink your firm. You have to let some people you love go. So there is some real pain that goes with it. That period did not shake my confidence in the actual investment process. I feel bad. I think I did better than our investors because I kept adding and saying, take the volume up on what I do. Not everyone can do that. And I know more than they know. Not in a weird insider sense, and just I should be more confident in my own process than anyone else is. But it is excruciating. The amount I remember someone I admire tremendously. When Stan Druckenmiller retired to run his own money, he's still very active in markets. I think he wrote a note that resonated with me because I forget the exact details. But the essence was, it's too painful and too upsetting to run client money. The man never had a down year. And I'm like, if Stan can take it, those of us, and we've had a lot more up years than down years, and life's been good or I won't be sitting here. But we've had never three, but two plus years of pain. And I'm like, if Stan can't take it, man, this is harder to do than it looks like the whole world thinks you're stupid when you're losing money. No matter what you can point to, no matter what evidence you can point to. I shouldn't say the whole world, your mom still likes you, but a lot of your world and no, a lot of investors stuck with us and I love them for it. But you do lose a lot of people. So it's not fun for a business to go through that.

Joe Weisenthal (18:37)

Intuitively, though, it's to your point. Measuring the disutility of long drawdowns versus deep drawdowns. This must be a very acute thing for anyone who's not just managing their own personal portfolio for the reason that you've just described.

Cliff Asness (18:52)

Yeah, I've talked about this with other money managers. This idea of length versus severity.

Palantir AI Narrator (18:56)

Yeah.

Cliff Asness (18:57)

And I've never had one who's not like, yes, length is much worse. Well, one of the things is when something goes on, it's often a similar story for two and a quarter years. Right. So you go back after six months where rationality is getting absolutely punished. You get a lot of sympathy from people. You show them, look, bigger bargains. We're right. We're going to be right. You go back six months later, they're like, okay, you go back six months and six months later they're like, you're just saying the same thing.

Joe Weisenthal (19:29)

And eventually they must just think, maybe you're a dinosaur. Like maybe you just don't get this new wave that's going on. Is it possible that we've really had regimes again?

Cliff Asness (19:36)

It's not everyone. We have a tremendous amount of investors stick with us. We have ones who double up, who get it that those are often opportunities. But you shrink when you lose money for a while and you grow when you make money for a while. It's the ironclad rule of this business. And sometimes it's just backwards.

Palantir AI Narrator (20:08)

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CNN Five Things Narrator (20:50)

Five times a day, CNN brings you five stories that'll get you up to speed on your day. New episodes drop Monday through Friday at 6:00am 9:00am noon, 3:00pm and 6:00pm hello.

Tracy Alloway (21:04)

From CNN, I'm Joe Beck.

Joe Weisenthal (21:06)

From CNN, I'm Fez Jamil.

Cliff Asness (21:08)

I'm Krista Bow with the five things you need to know.

CNN Five Things Narrator (21:11)

Follow CNN's Five Things podcast wherever you get your podcasts.

Tracy Alloway (21:20)

So one of the things that has been happening recently that has changed from 2015 when we started doing this is retail participation in the market. And it just feels like it is such a big thing for retail investors now to use things like options, even, you know, one or zero day options. The stuff that you might more traditionally associate with someone running a hedge fund. Now they're trading.

Cliff Asness (21:42)

No, we're not stupid enough to trade zero day options.

Tracy Alloway (21:45)

All right, some hedge funds then. Does the increased presence of retail in the market change the way that you do business at all?

Cliff Asness (21:54)

I think it contributes to what we're talking about of this dislocation. I hate this because I sound very elitist when I disown on retail. But there's a lot of academic work that shows retail on net loses net is important. They go through periods where they win. They go through periods where they win a lot. You know, if you buy a meme stock and it triples tomorrow, you don't always lose. But on average, retail transfers money to Wall street and to institutions. So if they're a bigger force in markets, you're going to have more of that going on. And it jibes perfectly because that would raise the opportunity if they're generally on the wrong side of things. But there are more of them, many more of them than they used to be. They can be right even if they're wrong on the facts. They can be right on the numbers for longer than they used to be. So I do think this is part of it. And I apologize to all the really smart retail investors. Anytime you talk about averages, you're dissing a whole lot of people who don't deserve to be. But on average retail loses zero day options. My God, they're making exactly the people they claim to despise on Wall street very rich by trading these things. And it's just fanduels.

Joe Weisenthal (23:08)

I love that before we got to get to the question of why. Right. But one last question sort of leading up to it, how do you establish that? What do you look at in the market that right now you say this is a less efficient market than once it was? What is the measure? Or is it just feel or you just feel sort of crazy like all.

Cliff Asness (23:27)

Of us observe, most of it is quantitative. I do start with thing I've talked about already, the spreads between cheap and expensive.

Joe Weisenthal (23:34)

Yeah, yeah.

Cliff Asness (23:34)

I don't just look at spreads. I wrote a piece back in 2000 during the tech bubble. It's called bubble Logic. It never got published because I tried to make a book out of it and the bubble came down too fast for me. That was good for my business but bad for my author career where I didn't just look at price multiples, I looked at it in a more holistic sense. What growth do we need to justify these multiples trying to come up? The only time I will use the word bubble is when I've tried very hard. And it doesn't mean we'll come up with the same answer, but this is the framework I use. I've tried very hard to come up with assumptions, even if I don't like them and think they're at the outer edge of possible, that could justify these prices.

Joe Weisenthal (24:15)

Yeah.

Cliff Asness (24:16)

And if they don't come close twice in my career I've been willing to go, no, I'm willing to use the B word. I should tell you right now, when it comes to within stocks, the whole market is a different issue. The market is quite expensive right now. But when it comes to this spread between cheap and expensive, I'm not using the bubble word.

Tracy Alloway (24:33)

Okay.

Cliff Asness (24:34)

That same measure, it's not the 125th percentile anymore, it's the 77th percentile. It's wider than on average, maybe making it a little more attractive if you can stick with it that big. If. But I don't use the word bubble for 77th percentile. I use it for blowing through. So five years ago I was saying it's a bubble. Now I'm just saying this is a little bit of an odd market.

Tracy Alloway (24:58)

So I want to ask you more about the bubble, but we keep touting that we're going to ask you the why question. So let's ask the why question. Okay. Why are markets becoming less efficient?

Cliff Asness (25:06)

Okay. Well, first of all, and I say this in the piece, a lot of conjecture going on. This is an op ed. As statisticians.

Joe Weisenthal (25:14)

Is there like a 50 page piece? A 50 page, a 50 page academic op ed?

Cliff Asness (25:19)

Look, this is our first time doing this. You will be convinced I could do a 50, 50 page single spaced op ed.

Joe Weisenthal (25:24)

I believe you. I know you can.

Cliff Asness (25:26)

It's just as a quant, as a statistician, you'd like to see 100 bubbles have stats on each one. This way they're all. They'd rhyme, but they wouldn't be exactly the same. You tease out what's going on if you see two in a 35 year career, you're not going to be able to prove this statistically. But I believe in my conjectures. I'm not soft selling them. I just want it clear that nobody is going to walk away saying he proved it. I list a few reasons in the piece why markets might be prone to bouts of bigger disconnects from reality. My second favorite is one I'm sure you've talked about. I know I've listened to you guys talk about it. The rise of passive investing. I am not a passive hater. There are really smart people. Mike Green's out there. The Mount. That guy hates passive. I don't know. The Middle east has never seen hate like the mountain. Mike Green hates passive. But. But again, he's a smart guy. He makes interesting arguments. I'm not that guy. I think passive has been a huge positive for an investor welfare. I actually was lucky enough to be fairly good friends with Jack Bogle and he was a hero of mine. We had a podcast briefly and he did.

Tracy Alloway (26:31)

Did you really?

Cliff Asness (26:31)

And he came on it. In fact, I'll tell you part of that story in a second. So I'm not a passive hater. But here's what we know. We know the whole world cannot be passive. And when I say passive, I mean in a Jack Bogle market cap weighted sense. Sometimes people use passive for people like us and they really mean rules based. And that's not how I use the word passive.

Joe Weisenthal (26:52)

You mean someone who owns the entire market?

Cliff Asness (26:54)

Yeah. We're long, short and leverage. How you get to passive on that, I don't know, but some people do. I own the entire market. We had Jack on the podcast and he absolutely agreed everyone can't be passive. Now of course, being Jack Bogle, he thinks at that point the marginal investor should still move to passive. But he recognizes the obvious fact that if 100% of the people are not looking at prices, nobody's looking at prices. Who's figuring out if Nvidia is worth more or less than the corner drugstore? Right. So the market gets very weird there. We don't even understand what happens there. It's a singularity. I use a physics analogy. We don't know what happens there. PhD students in finance and I used to be one of these, will stay up late at night in their cups talking about what happens. If everyone was passive, what would it even look like? We know it's very weird and I doubt all the weirdness happens between 99.999% passive and 100. So we're on a curve. We're a lot more passive than we used to be. Even that you're probably aware it's hard to measure. Exactly how much is passive. Direct passive. True market cap weight you can measure. But what about people who take, you know, 80 basis points of tracking error? They're kind of mostly passive.

Tracy Alloway (28:04)

You guys remember pegged to, like, custom indices? Now it's kind of funny.

Cliff Asness (28:08)

You guys remember the Princess Bride?

Tracy Alloway (28:10)

Yeah.

Joe Weisenthal (28:10)

Yeah.

Cliff Asness (28:10)

Remember? Mostly dead.

Tracy Alloway (28:11)

That's right.

Cliff Asness (28:12)

Fully dead. You're mostly passive. So I think fewer people thinking about prices, fewer people willing to take the other side when things get a little crazy. If one side really starts to get crazy, there are fewer people out there that has to exacerbate these swings. My actual number one reason, though. You talked about the gamification. For me, it's the overall. I'm gonna sound like a very old man yelling at the sky on my lawn right now. You know, get off my lawn. Or yelling at the sky. Some Simpsons thing.

Joe Weisenthal (28:45)

Yeah.

Cliff Asness (28:45)

I'm Abe in this case, but old.

Joe Weisenthal (28:48)

Man yells at cloud.

Cliff Asness (28:49)

Exactly. Thank you. Social media and the broader environment that you guys were talking about. I don't want to do politics except to say I don't think you find many people. There'll be some, but I don't think you find many people who don't agree with the idea that this environment has made our politics worse and more dangerous. Confirmation bias. We live in our own bubbles. We have algorithms that push us further and further towards. You start out as a moderate belief, but it keeps pushing you towards extremes. And pretty soon you're saying stupid things like, hey, that Tucker Carlson, he's a good guy. All right, I might have revealed a little Paul.

Tracy Alloway (29:26)

Yeah, you did a bit of politics there.

Cliff Asness (29:28)

So all of that adds up to making our politics worse, more prone, in particular to swings and extremes. Markets are not arbitrage mechanisms. That's sometimes misunderstood. They are voting mechanisms. The price is a weighted average vote where the weight is by dollars. I lucky enough to get more vote than the average person. And Warren Buffett gets a lot more votes than I get. If we all disagree on opinions, the reason it's not an arbitrage mechanism. And here I'll get a little geeky. Imagine you're reasonably sure this thing is mispriced in that Graham and Dodd sense, and you think it's massively mispriced. Trading enough to make it a third less mispriced is not very risky to you because it's not that Big a trade, and it's very high expected return because it's that mispriced. Now you've moved it back to a third or maybe half. The next part of the trade is much riskier to you because you already have the trade on, so you're just adding more.

Tracy Alloway (30:29)

Oh, I see. Yeah.

Cliff Asness (30:30)

And it has half the gain because you've already closed it by half. So arbitrage will not take things if on net more people believe something stupid. Stupid's gonna win and we're gonna be at least somewhat off of real prices. And I find it remarkably easy to believe that this same environment that makes our politics go a little crazy. For markets to be efficient in any degree, not even perfectly efficient, this famous idea of the wisdom of crowds has to be helping us a lot. The hypothesis that we're all geniuses is never gonna fly. Right. So the wisdom of crowds, and you know it well, says even if on average most people don't know the answer, the stupid answers cancel and the right answers don't because they're the same. My favorite example of that, and this might be Dating Myself, is Regis Philbin and who Wants to Be a Millionaire. Right. Remember the show, multiple choice show?

Tracy Alloway (31:22)

It's frightening that you're describing that as old, but I guess it is.

Joe Weisenthal (31:25)

Yeah.

Cliff Asness (31:25)

Yeah, it's been off the air. Sadly, Regis has passed away. It's old. Sorry, sorry. But you had to answer multiple choice questions and if you miss one, you're out. They start off ridiculously easy and they get harder and harder. You had multiple cheats, like three cheats. One was phone. A friend was almost useless. A, your friend usually wasn't much smarter than you, and B, people, at least to my eye. I didn't watch every episode, but seemed to choose friends who knew the same stuff they knew. Right. You really want to choose a friend who's in like a totally different field.

Tracy Alloway (31:56)

I think in some countries, the friends also had a tendency to deliberately give the wrong answer because they just didn't want to see their friend actually win money.

Cliff Asness (32:04)

The most cynical phrase ever is nothing succeeds like a friend's failure.

Tracy Alloway (32:08)

There we go.

Cliff Asness (32:09)

I don't believe. I'm not condoning that, but it is out there. The other one was eliminate two of the wrong answers. That's great.

Joe Weisenthal (32:16)

Yeah.

Cliff Asness (32:17)

Obviously, even if you have no idea, you go from one out of four to one out of two. The other one was poll the audience. And at least to my non exhaustive examination, it seemed to work pretty much every time, even if the question was hard because imagine you have 100 people in a room. Ten of them know the answer. The other ones are guessing. The 90 distribute evenly over the 4. Maybe not perfectly evenly, but roughly evenly. The 10 all land on B. So you pick B cuz it's bigger work pretty much every time. There's a crucial assumption in that the audience has to be relatively independent of each other. And they did that. They weren't talking. It was silent voting. If the audience all gets to talk, maybe the 10 convince the 90, but maybe they don't. Maybe a demagogue with a better Twitter feed convinces everyone. And if you ruin the independence and I think have we ever come up with a better vehicle for turning a wisdom of crowds into a craziness of mobs than social media? I'd be hard pressed to describe it.

Joe Weisenthal (33:26)

I find this to be very compelling. James Surwiecki at the New York Times, he came out with that book Wisdom of crowds in 2005, but that was right before social media blew up. And then, you know, there's that famous book in 1841, extraordinary popular delusion and the madness of Crowds. So we've always sort of understood that crowds can be both mad and wise. And I find this very interesting. The idea that perhaps the linkedness of the crowd is what sort of flips it from wisdom to madness.

Cliff Asness (33:54)

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

Tracy Alloway (34:24)

Foreign.

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Joe Weisenthal (34:47)

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Cliff Asness (34:49)

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Joe Weisenthal (35:02)

I want to pivot a little bit and talk about AI you've written or you've talked recently. I forget exactly the word that was used in some of the headlines. Succumbing to the surrendering ourselves, surrendering to the mission.

Cliff Asness (35:14)

I regret saying that.

Joe Weisenthal (35:15)

And so forth. We tried to talk a lot about AI. I still don't know exactly what it means in any Context, what does it mean to surrender to the machines in the AQR context?

Cliff Asness (35:26)

Sure. Well, first, not every journalist has Bloomberg's high standards.

Joe Weisenthal (35:30)

That's right.

Cliff Asness (35:31)

I am reasonably certain I said partially in that and the word partially got dropped. Even skipping all the details, if you're going to use AI in your process at all, almost by definition, you are going to lose a little intuition. And it bothered me for a couple years. I think I slowed us down on AI by a year or two just by saying, you know, we've always prided ourselves on the balance of we intuitively understand why we think this makes money and the evidence that it makes money. And when you go to AI, you're normally giving up some, not all. We actually do try very hard to figure out why we think this works or doesn't work. But if you weren't giving up some intuition, what the heck is the AI doing? If it's simple and you could have just seen it with the naked eye, it's hard to imagine it's helping. But let me give you a concrete example. We like good momentum. We like it in price. We like it in fundamentals. One way people on the quant side have tried to measure this for years is something like earnings calls. Trying to decide if earnings calls are good news or bad news. And if people underreact to good news, you want to buy when it's good news.

Tracy Alloway (36:43)

Is this just like how many times people say, great quarter, guys, or are you looking at something else?

Cliff Asness (36:47)

It's going to sound about as silly as that.

Tracy Alloway (36:49)

Okay.

Cliff Asness (36:49)

You build up tables of words and phrases with numerical values and then you say, what's the numerical score of this? And they can be much more subtle than this. I'm going to use a real simple example. The word increasing plus one.

Tracy Alloway (37:04)

Right?

Cliff Asness (37:05)

And I'm sure you see the flaw if the actual sentence was massive embezzlement is increasing. You know, our bad on that one.

Tracy Alloway (37:13)

The amount of fraud we're seeing in our private credit deals is increasing.

Cliff Asness (37:16)

Quant can survive looking stupid a lot if that's 47% of the time. If 53% of the time it's getting it right, fine. And those things had some efficacy, but they weren't great. What we do today is we train ML. It's called natural language processing. It's a subfield of ML to analyze corporate statements. But what it does, and this is going to be the geekiest thing I'll say, it represents every corporate statement. It looks across them and represents them as a set of numbers. What the geeks will call a vector of numbers. Then what we do is empirics to say, all right, we have 50 years of this across many firms. We have different vectors or numbers for every earnings call. What combination? Go long when the first number's good, short when the second number is high, blah blah, blah. What best combination forecasts that seems to be correlated to what we were doing before These word count things just considerably better. It does a better job than word counts. Language is very nonlinear. Whether something is good, whether that word increasing is good. AI is not perfect. But its chance of figuring out if increasing was good or bad, especially when it's trained on tons of these, is better than us. Here's where you lose the intuition though. I skipped a step. I like to do that. It's fine. Sneaky. If you ask myself or even some of the younger people who are much more tooled up on machine learning than I am these days, what does that vector of numbers actually mean? You often get a we. We really can't tell you that. We can say it. It's summing up in a mathematical sense the information content of that. We still get intuition because this indicator acts like a short term momentum indicator. So it's picking up what we want it to pick up. It's also done fabulously well for us for multiple years in real life. But we are giving up intuition at one stage that we used to not do. So my answer was meant to be much more subtle, that there are give ups in intuition. When you move to something like machine learning, they almost have to be or else again, what are you doing? I was uncomfortable for that for a while. So I was starting to do mea culpa saying that I slowed us down and it came out as well. I used to hate this, but now I have no job. The machine runs it, which is probably going to be true in eight years, but not yet.

Tracy Alloway (39:41)

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

Cliff Asness (39:59)

Sure. They get used interchangeably, but I think this is the difference between multi strat and multi manager. Multi strat just means I wrote a dissertation on choosing US stocks. We have applied similar things to stocks around the world, to currencies, to commodities, to directional bets through trend following. They are correlated but low. So we think of these as different strategies and we think a set of our strategies is better than a single one or you know, it's just the power of diversification. So we're big believers in particularly if you have a common philosophy. So it's not just fitting the data, it's fitting into an overall theme of what you believe in. We're big believers in multi strats. What we share with Multi Manager is that belief in diversification. Multimanager is what it sounds like it is. We are one team building these. We might of course have little separate teams at aqr, but we're one firm building these. They are farming it out to different people. To be frank. If you had told me their business model 10, 20 years ago I would have been very cynical that it worked. If you told me a what the total fees are going to be when you add up everything that's passed through and a fair amount of them and they vary in how quick they do this. If something's not working for what I would consider a very short while, yeah, they stop doing it. And I know there are a lot of low to medium Sharpe ratio risk adjusted return strategies that are really good long term but have bad periods. So if someone told me that model I would go you're going to charge a ton and you're going to throw out people who have a bad 2/4? No. And there have been people who've obviously proven me wrong and I'm humble about this. I don't fully understand why they must be very, very good at actual selecting the alpha. Right. And this is something that we don't do. We're internal quants who build our own models. We've never maybe be interesting one day. We've never tried to apply that to choosing outside people. But plenty of people by the way have tried to start Multi Manager and failed. So it's not like you just apply this charge a ton, hire a bunch of active managers and fire them if they have a bad two hours just automatically works.

Tracy Alloway (42:14)

It does seem though that like talent is the thing that's like capping multi strat expansion. Right. That's the limiting factor.

Cliff Asness (42:21)

They vary but some of the major ones are given back money. Even if you believe in this model and even from afar, I have to be a believer. I've seen the results are too good for too long in my view to have a decent chance of randomness. I don't exactly know what they're doing. I'm still waiting for Ken and Stevie and Izzy to send me their exact process. That would be Wonderful.

Tracy Alloway (42:46)

Izzy doesn't share it with you?

Cliff Asness (42:48)

No. Even better if Medallion would send me their exact process, but I respect it. But the amount of individual Alpha from teams that can be out there has to. All Alpha's finite, but has to have a decently tight cap. And the ones I've seen are fairly disciplined about this. And again, it varies. I can't speak for everyone I know we compete for talent with them much more than we used to. They have non quant parts of what they do, but they have quant parts of what they do. And for young quants, they're often considering an offer from AQR and Citadel. To pick just one example, we win our fair share, we lose our fair share. I think we probably pay a little less in the short term, but we don't fire you if you have a bad week. So some people are.

Tracy Alloway (43:34)

It does seem like a stress.

Cliff Asness (43:35)

And I'm not picking on Ken. He's not doing that. I'm just saying. No, but we know that it's a more cutthroat environment.

Joe Weisenthal (43:40)

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

Cliff Asness (43:59)

Right.

Joe Weisenthal (44:00)

Tickers that start with B tend to rise on Tuesdays.

Cliff Asness (44:04)

I usually use the CEO's middle initial.

Joe Weisenthal (44:06)

Yeah, stuff like that. We know, like. But, you know, even when you were talking about momentum and you said, well, there's two reasons theories for why momentum can work, let's just go back to the canonical, like, quant factors. Is there complete consensus about why these factors work?

Cliff Asness (44:22)

Not at all. Okay, you start out with.

Joe Weisenthal (44:25)

Because intuitively, like cheap stocks go. But my impression is that there's even still disagreement about why.

Cliff Asness (44:30)

Yeah, you start out with the Great Divide. And when they split the Nobel Prize between Gene Fama and Robert Shiller, my co founder and I wrote a piece on the COVID of Institutional Investor with that title, the Great Divide, Lars Hansen also won a share of it. I shouldn't leave him out, but Shiller and Fama were juxtaposed as the efficient market guy and the inefficient market guy, which is fairly close to. I don't think either of them are zealots, but it's fairly close to true. So you start out if something has worked historically and made a lot of money over a long period, you start out with what you led with. Is it data mining? Let's say you convince yourself it's not okay, let's just put that away. And that's really important. And I'm not, you know, pooh, poohing that step. You got, you got to do that. Then you got to ask yourself why. The two main contenders are a rational gene pharma kind of market where some stocks are riskier than others. And whatever you're using to say go long these and short these is loading on that risk. And if something is risky, you should make more for investing in it. One of the problems is identifying a there are sub fights within these fights. What do we mean by risk? Is risk beta like the capital asset pricing model?

Joe Weisenthal (45:46)

Finance academics are like every other academic because if sub fights with them, define.

Cliff Asness (45:50)

Your terms Talmudic at this point. But is risk beta like the capital asset pricing model? Well, the capital asset pricing model is a beautiful, elegant model that has failed everywhere it's ever been attempted. Is risk more multidimensional? Is risk something like what happens in a Great Depression, which is very hard to measure? The other argument is markets are not perfect. People make errors, and it's behavioral finance. So if a cheap stock beats an expensive stock because it's inherently adding some risk that you can't diversify away, that's a gene fama explanation. If it beats an expensive stock because people went too far, they went past the Graham and Dodd point, and if you can hold it, you make money when reality sets in. That's more the Bob Shiller point. I love Gene. He's my hero. I have probably drifted from 7525 gene to 7525 bob over my career. World's complicated. One of the hard parts is everyone wants to win. But both explanations can be true, and they can be true at different times. Life isn't so so simple, but those are the two biggies. But once you get into behavioral finance now, you can fight about why. What behavioral bias? Notice the you could sum up the two reasons I gave you for momentum as under reaction and overreaction. Overreaction is chasing, right? Underreaction is the information came out, didn't move far enough, and I hopped on that. Yeah, bandwagon. You know, you're in a little bit of a dodgy area when your two best explanations sound a little like antonyms. Now, the. The I'm being intentionally funny.

Joe Weisenthal (47:23)

We know it worked. We're just not sure whether it's literally opposite things or intuitively opposite things. We know it's One of them and.

Cliff Asness (47:29)

There'S some debate being a little too negative. You can actually. They're good papers. Teasing out the underreaction is easier to show. You can actually show the fundamentals do catch up. I think the overreaction probably kicks in more in bubbles where there is just more feedback trading. Again, both explanations can be true and they can vary in their intensity over time. The overreaction might have a very small part of it except plus or minus two years around a major bubble when it may be the driving force. So we have to get really comfortable. We don't have to have the be all end all single explanation. But if there are a few good explanations and no real counter ones and we have really strong 50 year empirical results, yeah, we're okay with taking some bet on this. You never want to put all your money on one thing that's not a quant thing.

Tracy Alloway (48:17)

I'm going to try to connect a bunch of the sort of of decade long mega trends that we've been discussing. But I'm thinking behavioral finance, gamification of markets, big data, AI, all those fun things combining into sports betting which seems to be getting bigger and bigger than ever. And the reason I ask is because I was talking to someone in a bar the other day and they said that AQR was doing more sports betting stuff.

Cliff Asness (48:42)

Someone in a bar was talking about AQR doing sports.

Tracy Alloway (48:45)

Yeah, I know it's. Well, it probably says more about the bars that I'm in and the people I'm talking to than anything else. But yeah, they mentioned that.

Cliff Asness (48:51)

I'm picturing the Star wars cantina right now, I gotta tell you.

Tracy Alloway (48:55)

So I'm just curious, is that a thing or are you maybe interested in the sort of prediction market aspect of things nowadays? Susquehanna is getting into it, I think.

Cliff Asness (49:05)

No one again tells you exactly what they're they're doing. I. My sense is they are into it and are good at it. This is incipient for us. We are considering and looking at it. We have a long history. Toby Moskowitz, who's a Yale professor and an AQR partner, great deal by the way. You get paid for two full time jobs that are 80% overlap.

Tracy Alloway (49:27)

I'll keep that in mind for the future.

Cliff Asness (49:28)

If Toby's listening, just, you know, you're a lucky man. He wrote a book called Scorecasting that was all about sports. But the sports analytics, the Moneyball type stuff looks a lot like what we do. It looks a lot like people are not pricing this right people. I wrote my Most downloaded paper ever. Which is really a little annoying because it's like the only one I've written outside of my field was on when to pull the goaltender in a hockey game.

Joe Weisenthal (49:58)

Oh yeah.

Cliff Asness (49:59)

And I wrote it with a colleague and friend, Aaron Brown. We built a model and we came up with, you should be pulling the goalie if you're losing by one with five or six minutes left, not with two minutes left. And I'm pretty sure we're right. We spent a lot of time in that paper saying why aren't they doing it? And making analogies to investing. You know, when people know the right thing to do but can't do it because the public opprobrium, if they get it wrong will be much worse.

Tracy Alloway (50:22)

Right.

Joe Weisenthal (50:23)

Like aren't they saying like football teams should go for it on fourth down a lot more than they do? That's like one of these things that.

Cliff Asness (50:28)

And they have started to. Yeah, there is a feedback where some of these things are slow, some of them are fast. Baseball has largely absorbed a lot of these things. You know, on base percentage was one of the major insights of, of Moneyball and nobody, nobody's missing that one anymore. But we still think particularly in the betting markets are not, probably not as rational as Toby's scorecasting book. So we do think there might be opportunities there. But I don't want to overstate it just because I am cynical about online sports betting. I gotta tell you two things. I was always a very libertarian guy. I still think I am. But a couple things have made me less libertarian. Children. The existence of my children will make you go, yeah, maybe people shouldn't be able to do anything they want when they turn 20 with some rules and sports betting and maybe walking down the New York City streets and getting a contact high. Sports betting. We're already, you know, some of these scandals we're starting to see. Yeah, how do you not have them when you know, some of these people make a ton of money but they don't all make a ton of money. And there's a lot of money in sports betting. Anything you do for entertainment is fine if you do it at entertainment size. If you go to Las Vegas and you go, the odds are on the house's side, I'm going to lose $500 over two days probably if I win, great. But it's going to be fun.

Tracy Alloway (51:45)

It's factored into the cost of a Vegas vacation.

Cliff Asness (51:47)

But like I have a whole bunch of 20 something year old, mostly guys in my extended family. A lot of Them are sports bettors and they're not even. It even pisses me off that they're not even rooting for their teams anymore. They're kind of rooting for this player to score on their prop.

Tracy Alloway (52:05)

It is a strange dynamic.

Cliff Asness (52:06)

Yeah. So do I think that gamification is highly related? I think if you go look at the Robinhood app and FanDuel, I think you will find they are far more similar on their feedback and how they treat things and in how their investors do on average, particularly sports betting. You know, investors lose on average because the sports betting companies make money. It's like saying on average insurance is a bad deal. You know how I know? Because insurance companies are profitable doesn't mean it's stupid to do because maybe if it's in a risk you can't tolerate, that could be a fair trade.

Tracy Alloway (52:40)

There's a price you pay for peace of mind, which is something I've come to appreciate over the course of 10 years.

Cliff Asness (52:45)

There is zero chance that the average online sports better is making money.

Joe Weisenthal (52:50)

Yeah, I just have one last question. You know, I think if you looked at like some particularly crazy times and markets, here's something that's changed very much since we started the podcast first 10 years ago. I think if you look at some crazy times and markets, whether it's, you know, 2019, when some of these measures were getting extreme, or just, you know, the SPAC mania of 2021, one theory that one might have offered is derp. And he's like, oh, this is now we have, you know, we haven't been observed for a long time and yet some of this sort of speculative mania crazes has not gone away. How surprised are you that the move from 0% to, say, 5% or whatever didn't have more of a sapping effect on some of the behavior and speculative froth, or just sort of animal spirits.

Cliff Asness (53:37)

In these markets, I am mildly surprised. Okay, I left this out, but I actually had three possible reasons in my paper. And the third one was super low interest rates for a long time. For why things can get crazy now, the counter argument is once you break people's brains, they don't necessarily repair themselves instantly. So I think it was probably a contributory factor. Again, very hard to prove. A lot of people in 19 and 20, when the spreads being cheap and expensive were here were saying it was a super low interest rate environment and growth stocks have more cash flows in the future. Low interest rates means they're worth more. We did the math on that. It explained like 2% of the interesting value spread. And in 99, 2000 interest rates were quite high.

Joe Weisenthal (54:21)

That's right. There was no zone 99.

Cliff Asness (54:23)

So it's not a unified field theory explanation.

Tracy Alloway (54:26)

Yeah.

Cliff Asness (54:26)

Do I think it helped kickstart some? And again, we're in the soft guesswork, but I think these are educated guesses. And I listed it as one of my three. I think it certainly kicked us off on some of these things. Certainly loosen the bounds of rationality. Absolutely. Free money will do that, I don't think. It's not human nature that they take away zirp and everything comes back. Would I have thought it was a bigger effect in going back to, you know, at one point we hit about 5% on the 10 year, almost 5%. Would I have thought that would have mattered more. Yeah. Only in 2022 did we see one ugly year over that. But it has mattered less than I thought. But it doesn't mean it will never matter.

Tracy Alloway (55:06)

All right, Cliff asness, thank you so much for coming on odd lots kicking off our 10 year anniversary celebration.

Cliff Asness (55:12)

Congratulations again.

Joe Weisenthal (55:13)

Yeah, thank you so much, Cliff. That was.

Tracy Alloway (55:15)

That was really a pleasure.

Cliff Asness (55:15)

I had fun. Thank you.

Tracy Alloway (55:29)

Jo. That was so much fun.

Joe Weisenthal (55:30)

That was great.

Tracy Alloway (55:31)

Literally the perfect guest.

Joe Weisenthal (55:32)

Literally the perfect guest.

Tracy Alloway (55:34)

1. Well, there's so many things that stuck out from that conversation, but one of the things that stuck out from the conversation is this idea about the thing that flips the wisdom of crowds into the madness of crowds. Right. And the idea that maybe the wisdom of crowds theory works as long as everyone is sort of isolated and independent and making their own choice off of the information available to them. But it starts to fall apart when everyone is tied to everyone else and sort of in the same social network. And you know, if you think about one of the dominant themes in markets in recent years, it has been people herding into the same positions. Right.

Joe Weisenthal (56:09)

I found that to be really fascinating. I think that's a. It makes it a lot of intuitive sense. It probably can explain a lot of things about the world of politics, about the world of markets, et cetera. This idea that we're all just sort of one connected global village. As Marshall mcluhan put it, we're all.

Tracy Alloway (56:27)

Just gossiping with each other all the time.

Joe Weisenthal (56:29)

Just constant talk, talk, talk. No, that's a very interesting idea. I also thought it was interesting the idea of length of drawdown versus depth of drawdown, and the former being more painful, which strikes me as something. I hadn't really heard anyone talk about that before, but especially from the Perspective of a manager of other people's money. That's a very highly intuitive. That makes a lot of sense to me that, okay, like, yeah, you had a bad quarter, whatever, eventually, like, oh, your ideas are just out of date. It's been three years since you made money. Maybe time to rethink some of your fundamental assumptions.

Tracy Alloway (57:02)

People have powerful. They must be. That's one thing I've learned over the course of 10 years.

Joe Weisenthal (57:06)

Yeah.

Tracy Alloway (57:07)

The other thing was the idea of markets not necessarily being an arbitrage mechanism, which I think is very counterintuitive to the way a lot of people will think about markets. And this idea that like, well, sometimes you can't compress the price of all the way to where it should be rationally or logically or according to EMH or whatever, because the reward just isn't necessarily there to get to that, like, final 10%.

Joe Weisenthal (57:31)

It's interesting too to think about the sort of link between patterns and interpretability, why something works. And in our conversation a couple of weeks ago with Ian Dunning of Hudson River Trading, he's like, they do not put a lot of emphasis on interpretability. There's a pattern. And they have some reason to establish that the pattern works. It makes money. The idea that they then have to also come up with an economic story about why it works is not so important to them. Maybe that's because it has to do with timeframes. Obviously, Cliff's trading timeframe is gonna be very different than a high frequency trading firm like hrt. But it is interesting. And then it's interesting to think that even in the most established quant patterns, like why do cheap stocks outperform more expensive stocks over the long term? Right. Even there, there's dispute about why this pattern holds. Even though it feels a little bit more int so much interesting stuff here.

Tracy Alloway (58:24)

It's also, I guess it sort of warms my old cynical heart that maybe the edge for humans will be spotting the regime change. Right. Which, you know, at least there's something left for us to do. If it's not just pure pattern recognition, there's that. That one very difficult thing.

Joe Weisenthal (58:41)

Good luck to us. Good luck to us.

Tracy Alloway (58:42)

Yeah. All right. Shall we leave it there?

Joe Weisenthal (58:44)

Let's leave it there.

Tracy Alloway (58:44)

This has been another episode of the Awthoughts podcast. I'm Tracy Alloway. You can follow me, Tracy Alloway.

Joe Weisenthal (58:50)

And I'm Jill Wiesenthal. You can follow me. Hestalwart. Follow our guests, Cliff Asness. He's Cliffordasness. Follow our producers, Carmen Rodriguez. At carmenarmon, Dash o' Bennett at dashbot and Cale Brooks at Kalebrooks. For more Odd Lots content go to bloomberg.com oddlots where the Daily newsletter and all of our episodes and you can chat about all of these topics 24. 7 in our Discord, Discord, GG Oddlots.

Tracy Alloway (59:14)

And if you enjoy Oddlots, if you like it when we speak to guys like Cliff Asness, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg Channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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Cliff Asness (60:22)

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