Cliff Asness

Summary

Money Stuff: The Podcast – Episode Featuring Cliff Asness
Release Date: June 20, 2025

In this insightful episode of Money Stuff: The Podcast, hosted by Matt Levine and Katie Greifeld from Bloomberg, the conversation centers around Cliff Asness, a prominent figure in the quantitative investment world and co-founder of AQR Capital Management. The discussion delves deep into factor investing, market efficiency, the role of machine learning in finance, and the evolving landscape of hedge fund fees and private equity.

---

1. Introduction to Cliff Asness and AQR

The episode begins with Matt Levine and Katie Greifeld introducing Cliff Asness, setting the stage for a comprehensive exploration of quantitative strategies in investment management.

Quote:
"What you do for the world by doing that? That can take two forms."
— Cliff Asness [03:13]

2. Understanding Factor Investing and Market Efficiency

Cliff Asness articulates the foundational principles of factor investing, emphasizing the importance of identifying and exploiting mispriced securities. He discusses how factors like value and momentum contribute to moving prices toward their "true" values, albeit in varying degrees.

Quote:
"If what you're trying to do is predict returns, you can predict returns because the price is moving towards truth. But you can also make money if you predict the price moves further away from truth."
— Cliff Asness [05:35]

Katie steers the conversation towards the efficiency of markets, prompting Asness to elaborate on the dual explanations for why momentum strategies might work—underreaction and overreaction.

Quote:
"We don't fully know, but I can tell you the framework."
— Cliff Asness [05:43]

3. The Momentum Factor and Behavioral Finance

The discussion deepens into the momentum factor, exploring its academic underpinnings and practical applications. Asness highlights the coexistence of underreaction and overreaction as reasons behind the effectiveness of momentum strategies.

Quote:
"There are two competing explanations... underreaction and the other is overreaction."
— Cliff Asness [05:51]

Katie references Brian Kelly's paper on market timing, prompting Asness to share his perspectives on trend following and its challenges.

Quote:
"It's highly diverse. Many small bets. Whatever's been happening tends to keep happening."
— Cliff Asness [25:49]

4. The GameStop Saga and Public Perception

Cliff addresses the infamous GameStop short squeeze incident, revealing AQR's cautious stance during the event and the subsequent social media backlash they faced.

Quote:
"I have no idea if we were long or short Gamestop during the whole thing."
— Cliff Asness [07:45]

He explains how public predictions about specific stocks can lead to reputational risks, even if the actual impact on the fund's performance was minimal.

5. AQR’s Relationship with Academia

Katie inquires about AQR's strong ties with academic institutions, noting that half of Yale's finance faculty are employed by AQR. Asness discusses the symbiotic relationship between theoretical research and practical investment strategies, emphasizing the importance of publishing and academic collaboration.

Quote:
"We think there's a value to employing the fancy finance PhDs to build your models."
— Cliff Asness [27:29]

He candidly shares the challenges of balancing proprietary strategies with the openness required for academic publication.

6. Embracing Machine Learning and Alternative Data

The conversation shifts to the integration of machine learning (ML) in quantitative strategies. Asness narrates his gradual shift towards embracing ML, influenced by his team’s successful implementations and the evolving nature of data analysis in finance.

Quote:
"It's still a sin to only look for patterns. You still need some economics, but machine learning is better at balancing those trade-offs."
— Cliff Asness [66:50]

He underscores the necessity of combining economic intuition with advanced data-driven techniques to enhance predictive models.

7. The Evolution of Hedge Fund Fees and Private Equity

Katie raises concerns about the escalating fees in alternative strategies compared to traditional public market investments. Asness agrees, critiquing the tendency of private equity to charge exorbitant fees for what often amounts to beta exposure rather than genuine alpha.

Quote:
"I think a tremendous amount of the private world is charging massive alpha fees for beta."
— Cliff Asness [60:38]

He contends that the re-privatization of certain investment strategies allows firms to pass on unjustified fees under the guise of offering unique alpha-generating opportunities.

8. Scaling Quantitative Strategies and Competitive Landscape

Asness reflects on the challenges of scaling sophisticated quantitative models, referencing Renaissance Technologies' Medallion Fund as a benchmark. He acknowledges their unparalleled success while noting the difficulties other firms face in replicating such performance at larger scales.

Quote:
"They have not discovered a way to scale it up many, many times bigger. They've discovered a way to take a certain amount out to provide a service."
— Cliff Asness [35:28]

9. Closing Thoughts on Market Efficiency and Behavioral Biases

In wrapping up, Asness shares his perspective on market efficiency, suggesting that markets exhibit both efficiency and susceptibility to extreme events driven by behavioral biases. He advocates for a nuanced understanding that acknowledges both theoretical frameworks and real-world anomalies.

Quote:
"One of my favorite explanations is an old man complaining about social media and 24/7 gamified trading. I don't think most people need a lot of convincing that this stuff has made... more markets are just voting mechanisms."
— Cliff Asness [46:08]

Katie and Matt conclude the episode by reflecting on the depth of the discussion and the valuable insights provided by Asness.

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Key Takeaways

• Factor Investing: Identifying and leveraging factors like value and momentum can help in predicting stock returns and moving prices towards their intrinsic values.

• Market Efficiency: Markets are not perfectly efficient; behavioral biases like underreaction and overreaction contribute to persistent anomalies.

• Machine Learning in Finance: Integrating ML with economic intuition enhances quantitative models, allowing for better pattern recognition and predictive capabilities.

• Hedge Fund Fees: There is a critique of the increasing fees in alternative investment strategies, particularly those that charge high fees for what may essentially be beta exposure.

• Scaling Challenges: Replicating the success of top-tier quantitative funds like Renaissance's Medallion Fund is challenging, especially at larger scales.

• Behavioral Biases: Extreme market events and the role of behavioral finance remain significant factors influencing market dynamics and investment strategies.

---

Notable Quotes:

• "We don't fully know, but I can tell you the framework." — Cliff Asness [05:43]

• "It's still a sin to only look for patterns. You still need some economics, but machine learning is better at balancing those trade-offs." — Cliff Asness [66:50]

• "I think a tremendous amount of the private world is charging massive alpha fees for beta." — Cliff Asness [60:38]

---

For those interested in the intricate dynamics of quantitative investing and the interplay between academic research and practical application in financial markets, this episode offers a wealth of knowledge and thoughtful analysis.

Transcript

[Matt Levine]

This is an Iheart podcast.

[Cliff Hasness]

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[Matt Levine]

Was hoping to be like one of those, like, clips on TikTok, you see, of fake headphones.

[Cliff Hasness]

Yeah, right.

[Katie Greifeld]

That's the thing. You look like you're on a podcast when you're with the headphones on.

[Matt Levine]

Yeah, no, it just looks like, you.

[Cliff Hasness]

Know, instead of we just randomly got.

[Matt Levine]

Together the chat and put these microphones.

[Katie Greifeld]

In front of us.

[Cliff Hasness]

Part of it is like with Bloomberg podcasts behind us.

[Katie Greifeld]

Yeah, right. I guess we've conveyed podcasts efficiently with all of the Bloomberg podcast signage.

[Matt Levine]

That's true. Part of the reason this has to be on video is because Matt shaved. Matt has had a beard for the past several years.

[Cliff Hasness]

I didn't know that.

[Katie Greifeld]

I've had a winter beard from, like, Christmas break through Memorial Day.

[Cliff Hasness]

I shaved over Covid for the first time in about 30 years.

[Katie Greifeld]

Okay.

[Cliff Hasness]

And my kids freaked out.

[Katie Greifeld]

Yeah.

[Cliff Hasness]

They were like, you're an alien.

[Katie Greifeld]

Yeah. My kids didn't care that much, but one of my sons said, it's a different daddy.

[Cliff Hasness]

Well, you also have hair on the top of your head when you shave the beard and you don't have any hair, suddenly you're Mr. Clean.

[Katie Greifeld]

You just. Does Mr. Clean not have a beard?

[Cliff Hasness]

Yes. He doesn't No, I don't think so.

[Matt Levine]

Mr. Clean. You know, Clean. Come on.

[Cliff Hasness]

I think that's a fair point.

[Katie Greifeld]

Wouldn't fit the Mr. Clean Shaven. All right, all right. It's the Money Stuff podcast. We have a guest, Cliff Hasness.

[Cliff Hasness]

Hello.

[Katie Greifeld]

Who runs aqr. Thanks for coming in.

[Cliff Hasness]

Thanks for having me.

[Katie Greifeld]

I always like to ask hedge fund managers, like, what do you do for a living? Like, what is economic function of the business that you run?

[Cliff Hasness]

Okay, those are, to me, slightly different questions.

[Katie Greifeld]

Right. One sounds like it's about you, one sounds like it's about aqr. I'm more interested in aqr. Let me just.

[Cliff Hasness]

Even if it's about aqr, what you do for a living is. And people might not like this phraseology, but you're trying to predict what happens to securities. You're trying to buy ones that go up either in the absolute or more than some benchmark, and sell ones that do the opposite. The broader question which I think is behind what you're saying is what do you do for the world by doing that? And they overlap, but they're not exactly the same thing. Something can have a net positive effect on the world, even if you're not waking up. And I'll admit this, I don't think most people in their jobs wake up and always think that way. And certainly not active managers just go, I am just making the world a better place. They're thinking, is Nvidia undervalued? Is it overvalued? The things that I think you do for the world is first, take the other side of positions other people disagree with you on or don't want to bear. That can take two forms. That can mean one of you is biased and wrong. You hope it's not you. But in that case, what you do for the world is you move prices back towards, not necessarily all the way to, in the abstract, the correct price. That's hard to define, actually. But something is mispriced, and you take the other side of that, it could be a risk premium. Other people don't want to bear. In a lot of strategies, it's not necessarily that they're making an error. If mergers are announced and three quarters of the pop you should get, if it closes happens, a lot of people might not want to stick around for that last quarter. And if you're willing to take the other side of that, maybe you get paid a little for doing that. And that is a service to the market. People want to get out, and you're helping that's kind of the positive side. But again, it's not what you're thinking about when you do the trade. There are positions active managers will take that are not about that. Let me put it this way. If what you're trying to do is predict returns, you can predict returns because the price is moving towards truth. But you can also make money if you predict the price moves further away from truth. If you're a momentum meme stock investor, and it doesn't mean you can't get that right. I think of that a little more as trading than investing. But they all come together. It even gets complicated within some famous quantitative factors. One famous quant factor is the momentum factor.

[Katie Greifeld]

I asked the finance professor what should I ask? And he said, you should ask him if momentum trading makes markets more or less efficient.

[Cliff Hasness]

We don't fully know, but I can tell you the framework. There are two competing explanations in academia and the general world that cares about these things for why momentum on average works. There's always a third that it'll never work again. And it was just random and it was just luck. But if it's true, why does it work? I actually think these can both coexist. So it's not truly embarrassing. But it sounds embarrassing that the two major explanations, one is under reaction and the other is overreaction. When you've narrowed it down to two things that at least feel like the opposites, you should feel a little shame for a second.

[Katie Greifeld]

Is the explanation just that there's a correct price and momentum is trading from below to above the correct price?

[Cliff Hasness]

Well, I'll give you two scenarios. Information comes out and people have a behavioral bias that behavioral psychologists would call anchoring an adjustment. They move towards, but not all the way to the new information. And I think that fits a lot of intuition in the short run that can make momentum work. If you're following fundamentals or prices, good news comes out or the price moves. If good news comes out, on average, the price goes up, but not enough. If the price moves, it may on average be responding to good news. And simply by observing the price move, you can say, okay, sometimes it's wrong, but on average, it doesn't quite move enough. If that's the reason. And I think the weight of the opinion in academia, I believe, is towards this underreaction explanation. Then even though you're trading on momentum, you're still moving the price towards kind of truth or equilibrium or something. But the flip side is overreaction. You think of that more as just your classic positive feedback loop. Someone's buying something just for, you know, fomo. It's been going up in there and well, that could be a negative reason or they're just predicting more people buy it because of fomo. In that sense, if you buy some weird meme coin, you could do that for a rational reason. Not that you are a long term holder, but you just believe it's gonna keep going.

[Katie Greifeld]

So wait, did you make money on GameStop?

[Cliff Hasness]

This is the God's honest truth. You won't believe me. I have no idea if we were long or short gamestop during the whole thing.

[Matt Levine]

You never went back and checked?

[Cliff Hasness]

No, I never did. I did have an episode where I mentioned on perhaps a different TV network that we were short amc.

[Matt Levine]

What did your Twitter look like after that?

[Cliff Hasness]

It was very ugly. Yeah, I certainly knew of that world. Even though we're quants, I watch the markets all day even if I don't do anything about it. It's like the old joke about the weather. Everyone talks about it but nobody does anything about it.

[Matt Levine]

It's the best thing to talk about.

[Cliff Hasness]

So it's not like everything that went on with GameStop. Melvin Capital, you know, I'm watching it every day, but we take relatively tiny positions in every stock. There was nothing weird in our P and L. And yes, I was not even curious. It probably wasn't even in our universe at that point of things we trade. But then I'm going on this other network. It's allowed. I was going on.

[Matt Levine]

Yeah.

[Cliff Hasness]

And in kind of a pre call of what are we going to talk about? You guys know you don't want to get on there and have absolutely nothing to talk about. You want to have some. Not necessarily the answers worked out, but agreed upon topics. They're like, everyone in this segment gives us some longs and shorts. But I'm saying as a quant, that's kind of silly. They're not indicative. And we kind of made a deal where they'd let me briefly explain that doesn't make a whole lot of sense for Quantum, but it might be fun. And my way of saying it was, if I give you a few names long and a few names short, you could look in six months later and think we had a fantastic year or a terrible year and be terribly wrong in either direction because they're tiny. So I went through and AMC was, I think I'm accidentally doing it again.

[Matt Levine]

We'll keep going.

[Cliff Hasness]

Hopefully they don't listen to you guys.

[Matt Levine]

Well, we'll find out, won't we?

[Katie Greifeld]

This will be Turn off.

[Cliff Hasness]

But it was bad on every single thing in our model, practically, which is hard to do. It was expensive, unprofitable, high beta. They were issuing shares, not buying back shares. There are more examples. And so I said that. But then I added that we're only short 12 basis points, so the crazy people could be right. And it doesn't really matter. I discovered two things. They're not gonna like a short period. And crazy people don't always like being called crazy. Yeah, I had to discover that for myself. So my Twitter got ugly for a while. You may have noticed I've gotten, I think, a fair amount better at this, but I used to be pretty bad about responding to ugly, which you learn.

[Matt Levine]

Your lesson on that you always feed the trolls.

[Cliff Hasness]

Less so than I used to.

[Matt Levine]

Yeah.

[Cliff Hasness]

At least I think I'm better. Maybe I'm wrong, but I became public enemy number three to the meme stock crowd for a while. I did not.

[Katie Greifeld]

Three.

[Cliff Hasness]

Really? Yeah.

[Katie Greifeld]

Three.

[Matt Levine]

Second to Ken Griffin.

[Cliff Hasness]

Ken Griffin was number one. I don't believe Ken did this, but it's the whole pull the buy button thing. And oddly enough, Gary Gensler was clearly number two. Oh, yeah, because they thought he was covering for the manipulators and the naked shorts and whatnot. I've met both. I know Ken a little better than Gary. I don't think there are any two people on Earth less likely to be in cahoots than those two. I think they're on the opposite side of most issues, but that was the theory. But both Ken and Gary are too smart to respond to them on Twitter, so I certainly became the most actively engaged. And then I never did that again before today when I've accidentally done it.

[Matt Levine]

Before we get back to stuff that matters. Can I just say, on AMC, I tweeted, when did Dune 2 come out? It was like, last summer.

[Cliff Hasness]

Yeah. A year ago.

[Matt Levine]

I tweeted that I fell asleep during Dune 2.

[Cliff Hasness]

And.

[Matt Levine]

And it reawakened that crowd. At least on my Twitter, because I the same crowd. Or just like the AMC crowd.

[Cliff Hasness]

She's dissing a movie. Yeah.

[Katie Greifeld]

I fail to appreciate it.

[Cliff Hasness]

Don't you understand? You have to like every movie or else you're anti America.

[Matt Levine]

And then there were a lot of conspiracy theories about Bloomberg reporter lies about falling asleep in Dune 2 because she hates AMC. It would be, but it wasn't.

[Cliff Hasness]

We got a little list, too.

[Matt Levine]

Oh, yeah?

[Cliff Hasness]

Yeah. You do innocuous things. I was obnoxious, so I kind of deserved it. You didn't deserve.

[Matt Levine]

Thank you for saying that you didn't deserve it.

[Katie Greifeld]

I haven't seen Dune 2. I saw Dune 1. It was good.

[Matt Levine]

I saw 2. It was pretty good.

[Cliff Hasness]

No, you didn't.

[Matt Levine]

Actually, I didn't.

[Cliff Hasness]

You were asleep. Well, no, not the whole time.

[Katie Greifeld]

Not the whole time, but also like you went on and they were like, give me some longs and shorts. And you gave them some longs and shorts. Would you have known that? Or did you have to be like, ah, I gotta.

[Cliff Hasness]

I would not have known that.

[Katie Greifeld]

Right. So you don't know what you're.

[Cliff Hasness]

Long shorts. It would've been better for me if we didn't have the call because then I could have just said, I don't know. I rarely know individual stocks and if I do know, I'm probably not happy. I know. And even then it's like we lost 20 bips on that today, which is a giant number for us to lose on one stock. And even then I probably don't notice.

[Katie Greifeld]

So you lose 20bps, someone comes to you and says, that stock went down.

[Cliff Hasness]

I might be told about it. For us, it's whether these 750 stocks beat these 750 stocks. Yeah, I don't memorize 1500 stocks. Now, we take a fair amount of risk. Some funds more, some funds designed to be less. And that's about the size of these two positions. So when I say small, I'm not saying we're not both taking risk and trying to generate pretty decent returns. But if you just think about it, a quant is playing the odds. They're saying, affirm a company with these characteristics. And this can be old school factor quants from the 1990s, these can be modern machine learning, but with these characteristics tend to beat these characteristics. If that's all you know, and it is all we know, why on earth would you take a lot of risk in any one company? AMC really could have done well. It could have been bad on every single thing that on average doesn't work. And it could be a special situation that we don't understand. Something could be good on everything and the CEO can embezzle all the money. We don't want to take a lot of risk on any one thing because we have no insight in that. It's risk for no return.

[Katie Greifeld]

One thing you've written is that like over time the quant, like factor model has moved closer to being what Graham and dad investors do. Are you like an abstract meta Graham and dot investor? Is that like the way to think of what you do?

[Cliff Hasness]

I think it's moved Closer There are still differences and maybe some of the like momentum. If it's overreaction, if you're riding momentum. I don't think a Graham and Dodd manager does that. So I don't want to push the analogy, but this came out very, very early in my career. This is like 30 years ago. This is like my Goldman Sachs days. Started hearing a lot of active stock pickers. Some I'm still friends with one guy in particular, I was laughing at them and I was telling my friend, you all say the same thing. You all say you're looking for valuation plus a catalyst. I don't know if everyone says it, but I've heard it many times and I'm making fun of him. And at some point he looks at me and goes, you do value and momentum. And it was a gotcha. He won that round. Because I'm like, okay, I see your point. So even back then you can think of those two together. We literally add them up. But if you think of them as this holistic system, we're looking for cheap things that are starting to get better in price or fundamentals over time. And now I'm not talking the really modern stuff, alternative data, machine learning, I'm talking just classic quant stuff that's been academia and then imports over to applied. Profitability is a factor. Robert Novy Marks wrote a great paper that we all incorporated where all else equal given valuation and momentum, if a company is more profitable on some famous scales, gross profitability, roa Roe, that's some degree a positive low beta investing. Two of my colleagues, Andrea Frazini and Lasse Peterson resurrecting stuff Fisher Black did, and they're very good about saying Fisher did it first that lower beta stocks, if you're famously a capital asset pricing model person, they're supposed to on average underperform higher beta stocks. That's the main output of that model. It doesn't work. I mean it's one of the largest empirical failures ever. It doesn't work in any kind even outside of stocks. People test it in other places. Therefore it kind of makes low beta stocks little bit of a free lunch because they are lower risk and they keep up. If you actually go read Graham and Dodd, they're not just buying low multiples. They're much more holistic than that. High quality companies that have a moat, that have some kind of margin of safety I think was the they use margin of safety in looking for low risk. Doesn't sound so different. So over time I've thought at least a Core amount of what quants and academics, if you take them as a whole are finding is the full panoply of stuff that a Graham and Dodd investor. We do it very differently. Again, we're betting on the concept working. On average, they are using it in a soft or a hard sense as a screen to look for candidates. And then they're trying to learn a lot about that situation. Their upside is if they learn a lot about that situation, they could be more reliable than my, you know, hey, we could be wrong about amc. And their downside is they better be right. The concept can work and they can still lose if they're wrong about the specifics. So over time I've gotten a little less hubris about this. I think quants caused the problem, by the way. I think when Gene Fama and Ken French started looking at like price to book in the late 80s and early 90s and there were other people who did it too. I'm just a Chicago guy, so I'm going to just go with Fama and French.

[Matt Levine]

Fair enough.

[Cliff Hasness]

They did it best in my very biased opinion. I don't think I'd have to go back and check, but I don't think the first few papers used the word value investing. Over time, low multiple investing in the quant world came to be called value investing. And in the Graham and Dodd world, they get kind of mad at that. And they'd be like, it's not value investing. There are plenty of low multiple companies that deserve to be low multiple and there are plenty of high multiple companies that deserve it. And I think over time the quantitative process agreed with them more and more. So I still think it's a communication problem because they'll still talk about the value factor. In the quant world, that's just low multiples. And if a Graham and Dodd manager gets mad or any old school active stock picker gets mad at that, I'll just say you're right because value implies a more holistic thing.

[Katie Greifeld]

But isn't like modern quant and what you're doing now kind of just that more holistic thing, like you're just ingesting more data points and you have a less linear model and it's like moving towards that anyway.

[Cliff Hasness]

If you look at machine learning where either to construct factors, one of the best uses of ML we found is a subset of ML called natural language processing where you take textual data and you try to say, is this good news or bad news? Quants have kind of done this forever. You get transcript of an earnings call and the old school way to do this for a long time was you count up good words and bad words, good phrases and bad phrases. So increasing plus one and you parse the whole thing. And I'm sure you guys immediately see the problem. If the actual sentence was massive embezzlement is increasing, then you were off on that +1. Quantitative stuff can survive doing some horrifically stupid things in isolation. If 53% of the time increasing is a good word, then 47% of the time you were stupid. Turns out Natural Language processing, or nlp, if we want to sound like the cool kids that is taking that same data and training a ML model to say what predicts and what doesn't predict. It, of course, never gets near perfect. At the end of the day though, we believe it does a lot better than the word count methods and is additive to a model. But importantly, and I think this was your point, Matt, it's not qualitatively different. We've looked at both price and fundamental momentum forever. Fundamental momentum, the classic measures are things like, are earnings being revised? And you want the revision, you want the new news up faster or slower? Are earning surprises coming in positive or negative? And this is this anchoring and adjustment idea that if that's good or bad news, you can make money trading on it because it's not fully incorporated in the stock price. Parsing an earnings call poorly, as in the past, or better now. We think of it as just another form of are good things happening in the near term? And if so, they're probably under appreciated. So, yes, I don't think it's changed dramatically. It's much more of an evolution rather than a spirit change. But we got bigger tools in the tool chest now.

[Katie Greifeld]

But it is moving. I mean, like, you know, you asked like what a sort of like traditional old school fundamental manager would do. One thing they'd do is listen to the earnings call and like talk to management. Right. So it's like your machines are moving in the direction of being an old school analyst.

[Cliff Hasness]

Yeah. As someone with four kids in or around college age, my wife and I, and she's done a lot more of this, have spent a lot of time trying to figure out what careers will not be utterly destroyed by ML. And it's not an easy question.

[Katie Greifeld]

No podcaster, is it a podcaster?

[Cliff Hasness]

A podcaster is probably a good one for a while. They have those fake ones, so we'll all be podcasters. But yeah, it's another example. I always say these useless statements, like, on a sufficiently long horizon we're all replaced by machine learning. It's a question of what I look.

[Matt Levine]

Forward to that day.

[Cliff Hasness]

We're getting way too philosophical, but the transition to that can be very painful and weird. But a world of abundance and leisure? Maybe our horrible fate in the long term.

[Katie Greifeld]

I'll take it.

[Matt Levine]

Yeah.

[Cliff Hasness]

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[Katie Greifeld]

The price of just about everything going.

[Cliff Hasness]

Up, we thought we'd bring our prices down. So to help us, we brought in a reverse auctioneer, which is apparently a thing Mint Mobile Unlimited Premium wireless everybody get 30, 30 better get 30 better get 20, 2020 better get 20, 20 everybody get 15, 151515 just 15 bucks a month sold. Give it a [e-mail] Switch upfront payment of $45 for a three month plan equivalent to $15 per month required new customer offer for first three months only speed slow after 35 gigabytes of network's busy taxes and fees extra see mintmobile.com Trust isn't just earned, it's demanded. Whether you're a startup founder navigating your first audit or a seasoned security professional scaling your GRC program, proving your commitment to security has never been more critical or more complex. That's where Vanta comes in. Businesses use Vanta to establish trust by automating compliance needs. Over 35. Frameworks like SOC2 and ISO 27001 centralize security workflows, complete questionnaires up to five times faster, and proactively manage vendor risk. VANTA can help you start or scale your security program by connecting you with auditors and experts to conduct your audit and set up your security program quickly. Plus, with automation and AI throughout the platform, Vanta gives you time back so you can focus on building your company. Join over 9,000 global companies like Atlassian, Quora and Factory who use Vanta to manage risk and prove security in real time. For a limited time, our audience gets $1,000 off [e-mail] special that's V A N T A dot com special for $1,000 off.

[Katie Greifeld]

I want to talk about market timing because I was reading the Virtue of Complexity paper from Brian Kelly et al. Which is sort of.

[Cliff Hasness]

I will say he may be one of the only hosts of a podcast to read that paper. This is not a simple paper, though he writes very clearly.

[Katie Greifeld]

It's the sort of like it's the notion of taking a sort of simple factor model and blowing it up into a nonlinear AI model. And one almost throwaway sentence in the piece is this simplified AI model that he built for illustrative purposes lowered its risk before 14 of the last 15 recessions. I've always thought the naive best way to invest would be just market timing. Just have all your money in the stock market before the market goes up and not before it goes down, if.

[Cliff Hasness]

You can do it.

[Katie Greifeld]

My impression is that respectable hedge fund managers, respectable quant managers, respectable academics say the hardest thing in the world is market timing, and no one claims to get alpha from it and it's not a thing. Is that changed and has that changed due to machine learning?

[Cliff Hasness]

It has changed a bit. We do trend following on macro assets, old school CTA stuff. We think we've made it new school by incorporating fundamental momentum, by doing a lot of more esoteric markets. So we think even that's had a march of progress to it. But all else equal, I wrote my dissertation on momentum in individual stocks for some reason that I cannot explain. If you're using past returns to predict the future, just what's going up will keep going up and vice versa. To pick individual stocks. The whole industry calls it momentum. And if you think markets tend to keep going the same direction, everyone calls it trend following. Same thing starting in, I think, 2008. We always had it in our macro models, but we started formally offering separate trend following products. It doesn't take hero bets on any one market. It's not Gasarelli selling all the stocks a minute before October 19th of 87. I'm dating myself. My wife's birthday is October 19th, which always, always gets a little amusement. Crash 87 meant to be.

[Katie Greifeld]

Did you ever like crash themed gifts?

[Cliff Hasness]

There have been some jokes over. Over time. So trend following. It's essentially market timing, but it's highly diverse. Many small bets. Whatever's been happening tends to keep happening. That's the main way we'll do market timing.

[Katie Greifeld]

You won't do like your main equity fund is and that long.

[Cliff Hasness]

That was kind of a toy model to illustrate a point.

[Katie Greifeld]

Right?

[Cliff Hasness]

I know we're not taking a lot of risks on that model. Market timing I still think is quite hard. Might there be advances in it in the future? Absolutely. But are we taking significant risk in it now aside from trend following? Not really.

[Katie Greifeld]

You probably submit more papers to financial journals than the average, like asset manager by like 100%. I want to ask two questions. One is like, I want to learn about your relationship with academia because I think it is fascinating that you employ half of the Yale faculty and the Finance PhD pipeline runs mainly at AQR. Then two, because I think of what a hedge fund manager does as sort of finding anomalies, finding market inefficiencies, finding factors that are predictive of returns. And I think of what a finance academic does is mainly also that when do you publish and when do you just trade on it?

[Cliff Hasness]

There's so much to talk about here. First. There are a lot of reasons we do it. One is just personal consumption. We grew up in this world. We like being part of it. We were interested in this stuff in a purely academic sense before we got seduced into making money.

[Katie Greifeld]

I would posit that you think there is a value to employing the fancy finance PhDs to build your models. And the way to attract fancy finance PhDs is to offer them the most academia like possible work environment.

[Cliff Hasness]

Absolutely true.

[Katie Greifeld]

Publishing papers.

[Cliff Hasness]

Absolutely true. And to letting them publish. There are people including, including our two Yale professors. I don't know if we'd have come to AQR if we said, you can never write about this. We do a crass calculation though. If we think there is something that we are relatively unique on, entirely unique, or a very small handful of people know this, we won't publish it.

[Katie Greifeld]

The optimal time that would get you tenure.

[Cliff Hasness]

The optimal time to publish a paper is after you've made money from something for 11 years and an hour and a half before someone else is going to publish the paper. And we cannot get that right. I can think of one example of momentum and factors. The fact that factors themselves, like value profitability, also exhibit momentum as something that we've traded on for many years that we've always refrained from writing a paper on. Because no one else had. And again, we knew we couldn't be the only people who do this, but we didn't think the cat was out of the bag. And then someone else wrote the paper. And I'm sure we've done this to other people too, because you never know what they're doing internally. So life, you know, you do it long enough, life works out kind of fair, but that is the goal.

[Katie Greifeld]

But you were mad there because like as academics, you wanted credit for that paper and someone else published it.

[Cliff Hasness]

You could call it childish, but it's human. It's fun to discover something.

[Katie Greifeld]

Were you also mad because publishing that reduces the value of the signal?

[Cliff Hasness]

Maybe a little bit. Maybe a little bit. So far it still worked wonderfully. The trend is still your friend when it comes to factors. But it would be a fireable offense from AQR to publish something that we thought was truly proprietary. Let me give you my favorite example of this because it came up recently. One of the things that we've gotten into in a fairly big way, as have some other quants, is what's called alternative data. Those are new data sets that put people put together with sweat equity. The classic example and the only one, and this is the point that I'm allowed to talk about, are credit card receivable data. Where are credit cards? Because that's been discussed a million times.

[Katie Greifeld]

That's like barely alternative.

[Cliff Hasness]

Yeah, yeah, well, that's the point is this stuff is arbitrageable. I mean, it can go away value, be it the narrow quant sense of value or the more broad Graham and Dodd sense of value is trying to take advantage of, I think basic human nature. I think spreads between cheap and expensive are wider still than the historical average, not tighter. I don't think there's a lot of evidence that so much capital is in that that it's making it go away. If you get a short term information advantage because you have put the time in or paid someone who's put the time in to create a new data set that other people don't have, they're going to have it eventually. So I am talking to the Australian Financial Review. I don't even know if we had discussed alternative data beforehand, but he asked me about it and I said to him that our head of stock selection, a fellow named Andrea Frazini, has asked me, I might have said, told me, but has asked me not to talk about these things. There are things I'll talk about, but there are things we think are, you know, overused word, but are true alpha that the world doesn't know and I think it's reasonable. So I really can't talk about them. He writes the article and what it says is mostly that. But instead of saying he's asked me not to talk about it, it says Andrea Frazzini won't tell me what we're doing in alternative data. Which has a slightly different connotation. It has a connotation of addled old man, don't worry about it. We're doing some stuff here. So again it's an example where there certainly are things we won't talk about.

[Katie Greifeld]

Is that ever true? Are you like most cutting edge machine learning people like, ah, don't worry about it. Or do you read all the papers?

[Cliff Hasness]

I read most of the papers. I don't read all of the papers. I at least skim all the papers. I know the gist. We do a lot of different things at this point. Once you're a quant you want more factors and you want to trade it in more places. And I will admit that a week doesn't go by where I don't ask someone for a review of what we're doing. Somewhere at some point I approved it. So Pascliff had some understanding. Yeah, but also just, you know, some of this is pretty decent math and the old mathematicians do their best work in their 20s. I can tell you I'm probably still decent in math, but I'm not what I was when I was 22. Wisdom has hopefully replaced some of mathematical ability.

[Katie Greifeld]

Like is the personnel at AQR different from at Like I don't know who you think of as like quantity competitors, but like my impression is that you have many more finance PhDs than other places that might have more pure math people. Pure math undergrads or something.

[Cliff Hasness]

Yeah, I think there's some truth to that.

[Katie Greifeld]

What makes them better or worse?

[Cliff Hasness]

First, I think there's going to be a correlation that the closer you get to the high frequency world, the more you're going to be more in the pure math realm. My very tortured analogy is quantum mechanics versus Newtonian physics. When you get into the high frequency world first you have a ton of data by nature you just have a lot more instances and a lot less theory. So turning yourself over to the data more so than having an economic rationale is far more rational and that becomes a more mathematical exercise. We tend to think in our long 700 short 700 stock portfolios, average holding period is maybe nine months, maybe closer to a year at times. That's nowhere near High frequency. High frequency. When I wrote my dissertation and I did have this in, there was a monthly contrarian strategy. Now we're talking sub seconds kind of thing. I think when you're gonna have a medium holding period strategy, as I think of us, we're not Warren Buffett, but we're not hft. Even the machine learning stuff needs some economics too. You simply don't have enough data and there's too much of a dimensionality. Even with Brian's virtue of complexity, you need to give it some structure or else it's going to overfit and go mad. So I think that's the reason not saying we'll never do something in a higher frequency world, in which case we'd probably have to shift more, but in our world, being a mathematician, being an excellent programmer, but also having the economics behind it, it's kind of what we're looking for, right?

[Katie Greifeld]

I think of like Renaissance as famously employing exclusively people who have never thought about markets or finance or economics. And they come to it pure. And I feel like you are very much like people who have math chops but have economic intuition and think about it as an economic.

[Cliff Hasness]

I think that's accurate. I do have a couple of Renaissance observations. One of my favorite questions people ask me was how'd they do it? And I love that question because I get to respond. So your hypothesis is I know how they took a few billion dollars and still take a few billion dollars out of the market to share among a relatively tiny group of people every year with apparently very low risk, and I choose not to. I have some inklings of the general what they've worked on. My biggest guess is that they were 10, 15 years ahead of everyone else on most of this stuff and are just have developed more sophisticated systems over time. I think natural language processing. They were very early on.

[Katie Greifeld]

They all came from like Mercer came.

[Cliff Hasness]

From a few other things. John Lew and I, one of my founding partners wrote when Fama and Shiller shared the Nobel Prize, we wrote a whole overview of market efficiency in the debate about it. And I brought them up as an example. The medallion fund has almost nothing to say about market efficiency. It says these guys can extract a toll on the market with reliable consistency. But in terms of market size, it's a giant number for a few people to make each year. It's a tiny number in of whether the markets are efficient. And apparently the rest of us can't do it quite as well as them. They're the goat when it comes to this.

[Katie Greifeld]

Yeah, I think of like, you know, like Jane street can reliably extract some money from the market. And I think of that as like, a fee for service. They're like a market maker rather than being like a predictor of asset prices.

[Cliff Hasness]

I'm going to guess, and again, it's a purely guess that not all of it, but a fair amount of Renaissance had a similar. Similar flavor.

[Katie Greifeld]

I think when you talk to people who run POD shops, like, some of what they do, they conceive of as it's not literally market making, but it has that sort of flavor.

[Cliff Hasness]

And the constraint on those things is typically capacity. Now, it's a ton of money for those firms. They're amazing firms. But I'll give you an example of Renaissance. I've complimented them like crazy, so hopefully they won't be mad at me for this. One last comment. Everyone talks about the medallion fund, but no one's allowed to invest in that. They keep that for themselves. Another favorite question I get is this doesn't happen much anymore, but every once in a while I would get an investor saying, is a medallion fund better than you guys? And I'd be like, oh, hell, yes. But they won't take your money or my money. And I think we're pretty darn good and we will, so why is that relevant? But Renaissance also runs a fair amount of money in more open institutional products where they look very good. I'm not gonna deride them at all, but they don't look better than us. They look like really good, solid, regular quants. So what they cannot do, I think it's kind of obvious, is take the medallion process and scale it up many, many times bigger. They've discovered a way, as you put it, to just take a certain amount out to provide a service. I called it taking a toll, and that's amazing. But they've not discovered a way to do that at institutional scale, which, thank God, because that leaves something for the rest of us to do.

[Katie Greifeld]

I want to get back to something we talked about at the very beginning, because you mentioned that paper you wrote with John Liu, which you talk about the two possible explanations for making money, for anomalies, for whatever, which are sort of behavioral irrationality or whatever, and you're bearing a risk for someone. Everything you write and what you said here, you seem sort of agnostic about which one or what combination there is. Do you prefer one? Is it more reliable to get paid for taking a risk?

[Cliff Hasness]

Well, forgetting which one you think is really going on, they do have different characteristics. The positive behind a risk Premium is you may be able to get it forever because it's rational that you get it. The negative behind it is it's a risk premium. You have to figure out why. If the risk premium is most of the time this is fine, but it has depression risk. It's gonna do particularly bad in a depression. Well, that's not a very pleasant risk to have. You may get paid for doing that. The positive of behavioral is it's essentially over the long term, over the short term it can be a very, very painful lunch. Cause it doesn't always work. But if on average it works long term, it's a free lunch lunch and that you're not taking additional systematic risk. And the negative is it can go away, it can be arbitraged away if that error stops being made. If too much capital chases it, it can be arbitraged away. It's hard to arbitrage some of them away. Like basic valuation, it's very easy to arbitrage some others like alternative data where the point is to be quicker to getting a data set and to trading the data set. I will say this, and I hope Gene Fama isn't listening. I was probably 75, 25, a risk guy 30 years ago. I'm probably 75, 25, a behavioral.

[Katie Greifeld]

Is that GameStop or something?

[Cliff Hasness]

Well, GameStop may be the unfairly extreme example. It's not fair to pick the most extreme crazinesses to make your point. But yeah, it's real life experience. It's watching the spread between cheap and expensive stocks in 99, 2000. We started our firm in late 98. So right before the real crazy part of what's called now the dot com bubble, the spread between how we define cheap and expensive, either just using quant multiples or adjusting for forms of growth set records, we had 50, 75 years of data and we lived through it, blowing past those. Then after that I'm like, all right, that was a once in 50 year event. We survived it. We ended up being right. We ended up making money round trip. That was excruciating on the first leg. If you asked me back then, am I ever going to see that in my career again? I hope I'd be smart enough not to say never. None of us in our field, yours or mine, should say never. Markets are pretty hard things to say never about, but I think I would have said it's highly unlikely. For one, it was literally the most extreme event in at least 50 years. Second, the question presupposes I and people like me will still be around and presumably if we're still around, we're in more supervisory and authoritative positions. So how's it gonna happen again? And then it happened again almost exactly 20 years later, from 2018 through 2020. You can point to Covid. It went absolutely mad during COVID You guys remember for the six month period after Covid when the only two stocks you're supposed to own were Peloton and Tesla. One of them has still worked out mostly for you. One of them did not. But even before COVID though you cannot pin it on Covid. By late 19, early 20 approaching tech bubble levels, which again had not been seen the prior 50 years before the tech bubble. Again painful period for us. Again made money round trip. I'm going to brag about that. We survived it. I think we're stronger than we were beforehand. But if the first one didn't make you start to go, this behavioral stuff may be real and maybe bigger than it used to be. And I wrote a piece called the Less Efficient Market Hypothesis about markets getting less efficient. That may be true, but I think it's more accurate to say they are. What my evidence is they are prone to some extreme bouts of craziness on occasion. It's not quite the same as in steady state always being less efficient, but probably some of both. But I do think that has happened. I probably was not giving behavioral enough credit early on, and I think it's gotten to be a bigger part. A lot of times big economic forces.

[Matt Levine]

Show up in our lives in small ways.

[Cliff Hasness]

Four days a week I would buy.

[Katie Greifeld]

Two cups of banana pudding, but the.

[Matt Levine]

Price has gone up.

[Cliff Hasness]

So now I only buy one small but important ways from tech billionaires to the bond market to yeah, banana pudding. If it's happening in business, our new podcast is on it. I'm Max Chavkin. And I'm Stacey Vanek Smith. So listen to everybody's business on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

[Katie Greifeld]

I want to read something from the John liu paper from 2014. Suppose you imagine some investors get joy from owning particular stocks, for example, being able to brag at a cocktail party about the growth stocks that they own that have done well. One way to describe this Some investors have a taste for growth stocks beyond simply their effect on their portfolios. It can be rational for them to accept somewhat lower returns for this pleasure. But even if rational to the individuals who have this taste, if some investors are willing to give up returns to others because they care about cocktail Party bragging. Can we really call that a rational market and feel this statement is useful? I feel like that was a little prescient about how I have experienced some of the last five years, which is that it seems to me that it is hard to explain some of the stuff I write about, which is maybe not the most important thing in the world. It's often.

[Cliff Hasness]

I think it's the most important thing in the world.

[Katie Greifeld]

I do too. It's maybe not the biggest dollar value, although Tesla's pretty big. It does seem like a thing that is regularly happening is that people have a taste for stocks beyond any rational or irrational calculation about how it will affect their portfolio.

[Cliff Hasness]

Well, particularly the whole meme world. Meme coins are clearly a political statement.

[Katie Greifeld]

Clearly there is a non economic motivation for some of that and you can call it rational, but it's.

[Cliff Hasness]

Well, a few things. That paragraph, even writing it, I think we went a little too far because I don't think these people, we kind of write it like they're consciously doing it. Like, I just love owning these. I know I'm gonna lose money. I think they kind of end up. I don't think that they resolve cognitive dissonance by saying I'm gonna make money, even if that's what's really going on.

[Katie Greifeld]

Right. I think this was maybe a little harsh on growth investors in 2014, but on meme investors in 2014, no, it's.

[Cliff Hasness]

Dead on meme investors. We were also given a little mild shot to some academics who kind of try to save market efficiency by saying it's more meta efficient. If you count people just have tastes for this. That argument's been made. I think that's kind of a cop out if we dumb market efficiency down to that. What do we actually even mean by market efficiency? If you go, I know I'm going to do terrible on this, but it's fun. To me, that's functionally a fairly inefficient market. So we proposed in that piece, which did not catch on, that classically market efficiency and the testing of it has a joint hypothesis problem. You're testing whether markets are efficient, but you also need a model for how prices are set. And if that model for how prices are set includes this is fun to own, then you've pushed it too far. It has to be a reasonable hypothesis.

[Katie Greifeld]

This is what I write about. I want someone, and it's probably not me, to write a finance textbook that incorporates the factor of this is fun to own and gives some guidance on how to price that.

[Cliff Hasness]

And how to predict what's going to be fun to own and how long it's going to continue.

[Katie Greifeld]

And people, someone is making money on this.

[Cliff Hasness]

Oh yeah.

[Katie Greifeld]

And not just fading it. Someone is like, I have a model for which meme coins will go up.

[Matt Levine]

Well, people have tried to do that. You think about all the different buzzy strategies. I'm thinking about ETFs.

[Katie Greifeld]

That would just be great.

[Matt Levine]

Yeah. But there have been attempts. If you scrape social media, et cetera, and find out what people are buzzing.

[Cliff Hasness]

About, it's the old value managers lament. But it's also, I think, true if ultimately it's a bad investment in return sense that will happen eventually. I do think that time horizon and the extremes have lengthened a lot of the point of this.

[Katie Greifeld]

Why will it happen eventually?

[Cliff Hasness]

Why will it happen eventually? Well, if you buy a company that continues to perform poorly and you paid a ton for it, I think there's only so long that can go on. I think it gets more and more obvious. For one thing, I think a lesson.

[Katie Greifeld]

Of the meme stock and frankly of crypto is that fundamentals set a floor.

[Cliff Hasness]

On valuation, but not a ceiling.

[Katie Greifeld]

You know, he's like doing lbo, right? But then I don't want to just be mean to bitcoin for no reason. But like, you could certainly have a model in which you say the fundamentals of bitcoin are nothing and you could then say and in 50 years it'll go to zero. But like, that's a weird thing to say now. I don't know.

[Cliff Hasness]

Well, let me be clear. I wouldn't short bitcoin with my worst enemy's portfolio. And I 100% sure. I'll never say money is a little bit of magic. What becomes money is a little bit of magic. But I think most of the probability is eventually zero. But it may be a very long time horizon. I think what we've learned watching this stuff is that time horizon is lengthened.

[Katie Greifeld]

Okay, time horizon is lengthened.

[Cliff Hasness]

I'll take in my piece on this. I try to hypothesize, and I admit it's real opinionated hypothesizing. You cannot prove, if I'm right that markets are somewhat less efficient. Why? Gets even harder. But one of my favorite explanations is an old man complaining about social media and 24,7 gamified trading. I don't think most people need a lot of convincing that this stuff has made, say our politics worse, made us more in bubbles, hate each other more when it was supposed to make us love each other. More markets are just voting mechanisms. I don't think it's any different than politics. So this notion that things get crazier and can go on for longer and I have a very cynical view of your statement that this stuff might take 50 years. I think the more absolutely unsubstantiated by anything something is, the longer the craziness can go on.

[Katie Greifeld]

Right. I think that if you compare the longevity of bitcoin to the GameStop premium, I think Right.

[Cliff Hasness]

But go back to the tech bubble. Cisco Systems, great company, but was selling at a very stupid price, selling about 100 pe when the E was gigantic. You can have a small startup company, it's growing super rapidly, sell at 100 pen one of the largest companies in the world with some of the largest earnings in the world selling at 100p. You needed some very heroic and I would argue near, I'll never say impossible, but near impossible assumptions. Even if the tech bubble didn't break in March of 2000 when it did, and by the way, I still don't know why it broke in March of 2000 and not a year earlier or a year later. Once you're well past what I would consider rational saying you know exactly where but the time horizon, you can imagine that going on for when the growth is good, maybe even great, but not nearly enough to justify that price. It gets more and more obvious if something is based completely on air. One of the weirder down Matt's point that there's no arb to the upside, there's no LBO mechanism and shorting it is just frankly too dangerous. It's a weird way to say sometimes people say markets are efficient because you can't make money from these things. And I'm like, it's another weird way to defend efficient markets to go. They can be so frigging stupid that they're terrifying to make efficient. That may be totally true, but it also is a weird way to argue that markets are efficient.

[Matt Levine]

What does it mean for AQR if markets are less efficient?

[Cliff Hasness]

Well, any active management is an inherently arrogant act. You cannot tell the average person we should all be active managers, we should all have podcasts.

[Matt Levine]

But I agree.

[Cliff Hasness]

So it's an arrogant alpha.

[Matt Levine]

No, it's rise to the top perform.

[Cliff Hasness]

To believe you should be an active manager and to believe you're doing something good for your clients. You have to believe two things. That you have alpha and that you're not charging the full extent of that alpha through your fees. And we do believe that. And a lot of active management managers believe it, but it's an inherently arrogant act. It is consistent to say most people shouldn't do this, but we should, though the arrogance is obvious.

[Katie Greifeld]

What percent of your alpha should you charge in your fees?

[Cliff Hasness]

That is a super hard question. I've thought about writing about this at one point. It kind of depends on how unique your alpha is.

[Katie Greifeld]

Okay? Because I would think that if you got a pod shop manager really drunk, they would say 110%.

[Cliff Hasness]

No, that's what they can charge for their fees, not what they should charge. I don't think they'd ever admit, no matter how junk.

[Katie Greifeld]

No, right. No.

[Matt Levine]

Right.

[Katie Greifeld]

They wouldn't admit it. Like Will versus Shel in their soul, they want 110%.

[Cliff Hasness]

It's a great question, but it's a hard question if your alpha is doing Fama and French price to book. By the way, I still think Fama and French are going to be right in the next 30 years. They haven't been right in a while, but spreads have gotten wider and wider and that's been a wind in their face. I wrote a piece on this saying the long run is lying to you, saying that X the spread, widening values, the simple fama French value has delivered alpha. It's just lost on the repricing. But what you can actually charge for doing price to book should be very low, a very small fraction of the expected return.

[Katie Greifeld]

I mean, there are those who would say that's not alpha beta, right?

[Cliff Hasness]

But that's kind of what we're saying. Everything exists on a spectrum from one to the other. If you have discovered and built the database yourself, an alternative data source that you have built. This is extreme and I can't think of an example at AQR that fits this. But you can charge nearly 100% of that alpha because what they're getting is still absolutely unrelated to everything else what they're doing. There's some de minimis notion that if you charge 99% maybe no one would bother to do it, but you can charge a large fraction of the alpha because what you're delivering is still ultimately net returns that you cannot get elsewhere that aren't correlated to the rest of the portfolio are worth it.

[Katie Greifeld]

We can talk abstractly about what percentage of your alpha you should charge, but no one gets sent a bill for 90% of the alpha. Is pricing sort of like set by just like anchored norms?

[Cliff Hasness]

Anchored norms is another way of saying what do other people charge are in the ballpark of what you're doing. So of Course that matters, right? If you are way off the anchor, way off on the high side, no one should invest with you if you're way off on the low side.

[Katie Greifeld]

If I have people who charge really high fees and are pretty good at demonstrating they have alpha then oh, some.

[Cliff Hasness]

Of the POD shops charge insane fees and have been very good and that goes to that. Are they doing something unique And I think to some extent they are. I think their problem becomes kind of in the direction of Medallion without going all the way. I don't think they've rebuilt Medallion. Nothing is that. But they should charge a higher percent if they're doing something very unique. And they do. I have a very. You can, you know. Violin playing I think rosy view of how we think about fees. We're building a long term business. We think we have business value. We do not think we're just a hedge fund. We run a lot of traditional assets too, even the hedge funds. We were a big pioneer in doing the more obvious strategies at considerably lower fees. Starting in merger arb and trend following. The way we broke into some of those as standalone products, not as part of our multi strats was charging less and saying this is real and it's good, but you can do one of every merger and make a fair amount of money. But that's not magic and we shouldn't charge magic fees for it. But in that kind of kumbaya big picture sense, charging fees such that clients are happy with the long term results is probably how you build business value.

[Matt Levine]

If you step back from just aqr though, I'm curious to hear your thoughts on your industry overall and whether alternative strategies in general are too expensive.

[Cliff Hasness]

Yes, they are.

[Matt Levine]

I have some numbers to back up that statement. There's a new study out there. My understanding of it is that you Basically take a 6040 since 2008, you add alts exposure to it in various proportions and that blended portfolio basically trails that benchmark basically in close proportion to the fees that are charged by some of the ALTS managers. And I don't know, I read that and I was just kind of wondering, I mean you could make the case that why are we charging so much?

[Cliff Hasness]

Well, this is almost a mathematical certainty. Yeah, forget about ALTS for a second. You cannot tell me the average active portfolio beats the market after fees and costs. The average active adds up to the market because for every deviation one way there's deviation the other way. When I said it's inherently an arrogant act to be an active manager, it means you think you Got it. Even though if you buy one of each, you can't have it. I mean, a subset of the market, like alts could. But I think a lot of that still applies. It's an inherently arrogant act. But we've been saying this for at least 24 years. We wrote a paper, and yes, I start a lot of sentences with we wrote a paper in 2001 called Do Hedge funds hedge? Where we took the known indices of hedge funds and we tried to take out just the market beta. You could argue for a more sophisticated risk model and that could, as usual, go down that rabbit hole. But we found their betas were. First of all, this is more mundane but statistically underestimated, partly because a lot of hedge funds do some stuff that does not have perfect liquidity. And this was a small early version of what I got into at the private world later on. But if something doesn't trade all the time and you try to estimate its correlation with the market, you will underestimate it, because one great way to look uncorrelated is to have a three day lag in when you trade, your returns will be off. So the betas were underestimated by earlier studies. Given the correct betas, there was pretty much no alpha to the hedge fund world. First of all, I was a lot younger then, a lot less well known, so I cared. Nowadays I quite obviously court controversy, but back then I probably had 10 famous managers call me and yell at me about that paper. Yeah, the first time I was of course obnoxious. They called and said, why did you write this? And I gave the obnoxious answer because we think it's true. And that was apparently not an acceptable answer. I will only call out one person on the positive side, Richard Perry, famous hedge fund manager. He called me up and I already been yelled at by a whole bunch of people. And so when I heard Richard Perry on the phone. People like Richard Perry didn't call people like me back then. He was big, I was small. So I'm like, I know what this is. He's just gonna yell at me. He gets me on the phone, he goes, that paper you wrote, that's just correct. Good job. All right, And I'm only telling the. I'm not giving the names. And I do remember them. Of course, you have the ones written.

[Matt Levine]

In a book somewhere.

[Cliff Hasness]

All I have a list of vedettas in my brain.

[Katie Greifeld]

So wait, so you've been making fun of private equity managers on similar lines recently. Do you get calls from them?

[Cliff Hasness]

No, they're so fat and happy, they don't even care about me. Making fun. No. I get yelled at by some usually friends. I live in Greenwich, Connecticut.

[Katie Greifeld]

Sure, sure, sure.

[Cliff Hasness]

So you all hang out country club.

[Katie Greifeld]

They're like, I am actually not a.

[Cliff Hasness]

Member of a country club. But the old often used in a very bad way. Some of my best friends are. So I'm off the hook. I have some good friends who are private equity managers. Most of them can accept the. You're right about the industry, but not our firm. Which is essentially what I'm saying. So I'm not being mean about this.

[Katie Greifeld]

Your criticism is volatility lender. Your criticism is that private equity seems to have a higher sharpe because it has a lower volatility because it doesn't report.

[Cliff Hasness]

Yes, that is my main criticism, which I think is quite obviously true. That's just me. Some people do disagree. How the best disagreement I've heard, and I have some sympathy for this because I do not think markets are perfect, is we are right about the valuations. You are right that we move at a highly damp version of the market, but market moves too much. We are right. My response to that is why don't we get to do that Right?

[Katie Greifeld]

That's fair.

[Cliff Hasness]

You know when we've had a tough year because market's gone crazy and we were on the wrong. Wrong side of that, I have to tell my clients we're down 12%. I don't get to tell my clients we're up based on where I'd mark the portfolio. So why one group? And by the way, they could market just like we market. They're brilliant at valuing companies and they can tell you where they could sell it for today. So it's like an institutional legal quirk that they get to do it one way and we have to do it another. Everyone can mark their portfolio at what they could sell it for today or what they think it's worth. And one side gets to do it one, one gets to do the other. So that's a reasonable argument, but I still think it leads to an unreasonable conclusion. I will say 90% of my critique is about the volatility or the beta is about saying these things are low risk. 10% is about perspective, not trailing future returns. Because if I'm right about the volatility laundering, it has implications for returns going forward. If when David Swensen was pioneering, which is what he called his book, private equity is part of an institutional endowment portfolio, he's quite clear a lot of it's an illiquidity premium that no one wants illiquidity, everyone's scared of it. So if you're willing to do that, you get paid extra. If I'm right that people love the fact that they don't have to look at the volatility, that means illiquidity is no longer a bug. It is now a feature and very simple model for how expected returns are set. You get paid a higher expected return on something if you have to bear a bug nobody wants and you pay through a lower expected return if you have a feature everyone wants and you have to pay up for it. So the chance that that is going on going forward I think is quite high. Whether it means there's no edge to private equity or a negative edge or a smaller positive edge, I can't tell you that. But I do think if you think of risk adjusted return as numerator of return denominator of risk, I think my statements are mostly about the denominator, but they're 10% now about the numerator going forward. A lot of times big economic forces show up in our lives in small ways. Four days a week I would buy.

[Katie Greifeld]

Two cups of banana pudding but the.

[Matt Levine]

Price has gone up.

[Katie Greifeld]

So now I only buy one small but important ways.

[Cliff Hasness]

From tech billionaires to the bond market to yes, banana pudding. If it's happening in business, our new podcast is on it. I'm Max Chavkin. And I'm Stacey Vanek Smith. So listen to everybody's business on the iHeartRadio app, Apple Podcasts or wherever you.

[Katie Greifeld]

Get your podcasts on. My concern about private equity private markets, which is that it seems to me that like there is a ton of fee pressure in public markets. Everyone has kind of learned the gospel of buy low cost index funds. And even charging for fama French factors is not a 2 in 20. Business and private markets because they're not indexable, because it's harder to extract factors because they're not liquid, don't have those problems. And you can charge 2 and 20 for a lot of private stuff. And so it seems to me that there is a move to put a lot of stuff that would have previously been public into private markets and to say we can put privates into 401 s and there's good economic rationales for putting private assets into 401 s because you don't need liquidity. But there's also this really overwhelming if.

[Cliff Hasness]

There'S a positive illiquidity premium, that too.

[Katie Greifeld]

Right.

[Cliff Hasness]

Sorry, go on.

[Katie Greifeld]

But no, I just like you have written for years about criticizing people for charging alpha fees for beta. And it seems to me that the re privatization of the market is a way to sneak some alpha fees onto beta.

[Cliff Hasness]

I think a tremendous amount of the private world is charging massive alpha fees for beta. I won't mince words about that. If they outperform or underperform on net after all these massive fees and if performance going forward is tougher. And by the way, your point about the fee compression in the public world only makes my hypothesis that they won't beat the public world by the same amount going forward stronger. I think privates have a big function in the world. I don't think they're going away. What you started out with me. What do you guys do for the world? There are things that are in between what should be public and what's mom and pop. But I think where we are now, I think a lot of institutions are giving up some amount of expected return for the ease of limit of reducing their agency problem of sticking with something. Now if they're going to be terrible and not stick with things, it might be rational to give up some return. But you can't double count and say we're making ourselves better investors by giving up some expected return and oh yeah, our expected returns are going to be higher. If that's the rationale, then you've accepted my argument and I think you have to say this is what we get paid for by making your life easier.

[Matt Levine]

I am curious what you make of the push to put privates into more retail accessible wrappers. I'm talking about ETFs, but I guess I'm also talking about interval funds a little bit. It just seems like that's where the world is headed.

[Cliff Hasness]

I'm going to hedge this and say it very carefully. I think it's a terrible idea.

[Matt Levine]

Okay, go on.

[Cliff Hasness]

There are things that end up up in retail in a very good way eventually. We've done some of this when we introduced mutual funds. I can't say always going to retail is a bad thing. You can price it reasonably. You can say these are strategies you've never had before. This feels a little bit more like we've exhausted the institutions. I think I saw a number saying endowments are like 43% for a number I can't verify. That's wildly specific, but that's the number I remember. So it has a feel of who else are we going to get to own this stuff?

[Katie Greifeld]

It seems so explicitly that it's really wild.

[Cliff Hasness]

I think it's Explicitly that it's one of these things that for the cynics, I don't think there'll ever be a satisfying moment where we get to say we were right. It'll just be somewhat worse over the next decade. It's not one of these things like a tech bubble in 99, 2000 that at least I don't think there are scenarios where things get worse rapidly. And secondary sales, we got close to some of that in the gfc. I was on some investment committees where we were talking. We didn't do it, but we were talking about whether we would have to do that. So I'm not saying there's no chance of ugliness, but I think the meat of the probability is it's just somewhat worse. But it does cause me some worry, sadness to say, yeah, what we really need is to stick a liquids in 401s.

[Matt Levine]

Yeah.

[Cliff Hasness]

So yeah, bluntly, I think it's a bad idea.

[Matt Levine]

Well, I just wanted to talk about, and I don't know if this goes too far, but it feels like you had a change of heart when it comes to machine learning and AI in general. This is something we've spoken about before.

[Cliff Hasness]

I did.

[Matt Levine]

Well, was that like a light bulb moment or was that just, you know, maybe people on your team wearing you down over time?

[Cliff Hasness]

It was more the latter. Yeah, the latter is the second one. Right. I gotta think that through every time I tell people this. I've said it in a lot of public arenas, I sell it to clients. I think I probably slowed us down by a couple years in machine learning. I think it probably cost us some money. Cause the stuff has worked pretty well. We've always described ourselves, and we were getting into this a little bit early earlier as a blend of data. Backtests are nice out of sample, long periods are even nicer. But also theory. Theory can be a formal economic theory, but it can also just mean a common sense story where you think you understand why you're making money. And there's no way to say exactly what percentage of both, but I've often described it as an attempt to be 50 50. We want something that makes sense to us as economists and have strong data. When you move into the machine learning world, I don't think you have to abandon theory. And this is something I think we do a little different than most. I think there are some in the machine learning world who kind of throw theory out the window. We're still using common sense and theory to kind of limit the scope. But you are leaning more on the data. And again, I'm making up these numbers. But if regular stuff is 50, 50 machine learning, 75, 25 data. Even for us, that was uncomfortable for me. When you've been telling a story for 25 years and it's worked for you and your clients, it's not easy to move. I also think it's not improper for the role for the old man of the firm to go, let's just slow down. You guys come in here. I picture doing this with a southern drawl and you newfangled machine learning, curling your mustache. Yeah, no, that's the villainous role.

[Matt Levine]

Oh, no. I think it's kind of cool.

[Cliff Hasness]

You think? Okay, so I actually can defend it as entirely appropriate. But yeah, I had to be convinced it wasn't a light bulb moment. It was people like Brian Kelly, Andrea Frazini, Laura Serbin, all partners of mine presenting great results that made increasing. And I did a lot more reading. I was probably. I programmed in a programming language called LISP in the 1980s that was an early machine learning or AI language, not even machine learning. And then I didn't do anything about that for the next 30 years. So I think part of it was just me getting up to speed.

[Katie Greifeld]

Frankly, the thing I find compelling in the Kelly paper is you feed a sort of toy set of factors into a 10,000 neuron model and what he argues is basically you might get a better view of the actual function that generates the results than if you just try to use your economic intuition on a linear regression. There's theory behind it and the theory is things are not as linear as traditional methods would be.

[Cliff Hasness]

It's basically saying it's still a sin to only look for patterns. You still need some economics, but machine learning is better at balancing those trade offs. So the idea of throwing more at it when you have a better technique for that, you lean in that direction. I did have a better title, I think, than him. I wanted him. It's not better. His title's great. No, but I wanted to call it simply Occam was wrong.

[Katie Greifeld]

Okay.

[Cliff Hasness]

All right. Maybe not better, but I still want him to use that for maybe a follow up paper.

[Katie Greifeld]

All right, Cliff, thanks for coming.

[Cliff Hasness]

Oh, this was fun.

[Matt Levine]

Thanks for being on video with us.

[Katie Greifeld]

And that was the Money Stuff podcast. I'm Matt Levine.

[Matt Levine]

And I'm Katie Greifeld.

[Katie Greifeld]

You can find my work by subscribing to the Money stuff newsletter on Bloomberg.com.

[Matt Levine]

And you can find me on Bloomberg TV every day on Open Interest between 9 to 11am Eastern.

[Katie Greifeld]

We'd to love. You can send an email to moneypodloomberg.net Ask us a question and we might answer it on air.

[Matt Levine]

You can also subscribe to our show wherever you're listening right now and leave us a review. It helps more people find the show.

[Katie Greifeld]

The Money Stuff podcast is produced by Anna Mazarakis and Moses Andam. Special thanks this week to Stacey Wong.

[Matt Levine]

Our theme music was composed by Blake.

[Katie Greifeld]

Maples, Brendan Frances Newnham is our executive.

[Matt Levine]

Producer and Sage Bauman is Bloomberg's Head of Podcasts.

[Katie Greifeld]

Thanks for listening to the Money Stuff Podcast. We'll be back next week with more stuff.

[Cliff Hasness]

I'm Shonali Basak and I have a new show. It's called Bullish and it's about the future of Wall Street. Join me and Ken Griffin, Boaz Weinstein, Melody Hobson, Jane Fraser and other as I explore Wall street south, the rise of finfluencers. Oh, and I learned how to count cards. Another black die. Oh my God.

[Katie Greifeld]

This is amazing TV.

[Cliff Hasness]

Watch Bullish Tuesdays on bloomberg.com or tune in live at 6:00pm Eastern on Bloomberg TV and 8:00pm eastern on Bloomberg Originals.

[Matt Levine]

This is an iHeart podcast.