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Network Bloomberg Audio Studios Podcasts Radio News.
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C
Happy Thanksgiving.
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D
Hope you're having a great holiday. We are obviously not recording on this.
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C
Thursday, so we're gonna rerun a favorite old episode. It's gonna be great.
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D
You're gonna enjoy it even more the second time. Bye.
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C
Wait. Capitol Paleologo or Paleologo? Paleologo.
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D
I'm so excited for you to tackle that and not me.
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B
Good luck.
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D
Thought I had it down, but then I heard you say it and I.
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C
Feel like when I first met you, I think I asked you if you go by Gappy because of your famous track record of taking gardening leave and like having gaps in your career.
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B
Oh, okay, I didn't remember that. But yeah, that's a great excuse for a nickname.
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C
Is it just. I mean.
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B
No, but the reason is when I came to the States for grad school and this was a long time ago in 95. So the first thing that you did was set up an email account. You still had the freedom to choose an email account. Now they just give you your initials with a number. And so my initials are G, A P Gap. Giuseppe Andrea Palaiologo. And of course it was taken. So I said okay, well Gappi. And then Everybody in grad school and then my wife who's Italian, everybody started to call me Gappy and that stuck. And now at work they just have dispensed with my real name. Like on all systems I'm just Gappy Paleologo. So I expect that that will be prosecuted for tax evasion because on my tax forms there is Gapi Paleologo or something like that.
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C
Well, hello and welcome to the Woody Stuff podcast. I'm Matt Levine.
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D
And I'm Katie Greifeld.
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C
And we have a guest today, Giuseppe Gappi Paleologo, who is now at Paliasney and has been at most of the other big hedge funds and Hudson River Trading. I do want to start by talking about gardening leave.
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D
Okay, Natural Place.
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C
I think that we counted from your link. Your LinkedIn is like famous for discussing your gardening leave in some detail. And I think we counted three years of gardening leave.
- [03:48]
B
No, I think it's a bit.
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C
Okay, it's not precise.
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B
Fifteen months from Citadel, one year Hudson River Trading and four months from Millennium.
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C
Okay, so pretty close.
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B
Not terrible though.
- [03:59]
C
Yeah, a bit less than two years from my perspective. It seems very fun. Did you enjoy your three years of gardening?
- [04:07]
B
I do. So I try to keep myself busy. So I teach typically at some university. So the first time during my Citadel 2 Millennium Guardian leave I was teaching at Cornell and in the HRT to Bam Garden leave I was at nyu and I love teaching. And then what I do is it helps me focus on stuff. Usually what I do in, you know, whenever I read a book or read a paper that I like, I take notes. I take notes in latex and then I re derive or think about things. And so that typically is the basis for my course material and then it becomes the basis for my books. I've written a couple of books during my non competes.
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D
Interesting because thinking about gardening leave, Matt and I talk about it all the time because it's very alluring to me. Gardening leave doesn't really exist in journalism. I love to imagine what I would do. But one of the questions I had for you was, you know, do you have ever have anxiety about losing your edge or falling behind? But it sounds teaching is one of the ways that you keep reading.
- [05:18]
B
Yeah, I'm not particularly worried with that. I think that there is only a very specific subset of quantitative researchers who are afraid of losing their edges. And yeah, that's not been my case. I keep reading, I try to stay.
- [05:31]
C
Up to date, do the books, feedback into the work. Do you get ideas or deepen your understanding of techniques by teaching and writing the books, or are they just sort of like extracurricular?
- [05:44]
B
No, no, no. It's definitely. I learn a lot from writing the books. A lot.
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C
And then do you, like, go to your next job and generate more profits by.
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B
Of course, plenty more profits. Sell that to my employers. No, but I definitely. I learn a lot from writing, from the first drafts, and then I rewrite and rewrite. And I learn a lot from discarding material too. It's very useful to discard material. It makes you really focus on what matters and what doesn't. So I try to give a narra like a logical connection between various topics. And that is something that is possible only when you write a book. I really do not like writing. Nobody, I think, likes writing, maybe except for you. I do like writing.
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C
I understand that it's weird. Even among writers.
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B
I find it very painful. I find painful letting go of material.
- [06:38]
C
Yes.
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B
But I also like it, you know, it's some kind of strange delayed gratification, I guess.
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C
One theory that I have written is that hedge fund and quantitative research gardening weave is like a source of human flourishing because you have all these highly trained people who have an enforced year off. And I've written that all the hedge fund researchers should go work at LLM companies or like analytics departments of sports teams. And I'm partially kidding and partially not. How true is it for you? How much of your quantitative skills at this point are really just for investing and how much of it is if you spent three months consulting for a soccer team, you'd be able to tell them how to find better players?
- [07:32]
B
Hmm, I'm not sure. So I'll say this right. I was thinking a few days ago if there was a kind of a common thread in my professional life, because it seems kind of random and actually I think that there is, because I think that I was about 14 when I realized that I had an aptitude for applied math. I discovered physics and I liked math. And I also liked literature very much. So I loved reading, I read a lot. I was not a very social animal. And then basically since then, I've been doing the same thing in various forms, right? I did physics, I did applied math. I didn't do applied math in finance. I did applied math in weird things like optimization and logistics. So I have been doing kind of the same thing over and over, which has been writing and applying math to something. So I think that I could do it. I would like to do it. But I also think that it's not that simple to Go to a new field and say, oh, after three months, I know soccer. No, there is a lot of specificity. And the beauty of, I think, being a good applied mathematician is that they start with the problems and with the domain first and that they're sufficiently mature from a mathematical standpoint that they are not making too much, much of an effort in using math. So I think the good art of being an applied mathematician is to study persistently the application. So, no, I don't think that after three months it would be good enough. But after a year, you know, about a year of being fully immersed in an application, then you start getting a little bit better, and then the math is not the problem. And then you start doing some good work.
- [09:26]
C
You have a famous essay on, like, advice for quant careers, and you say that, like, the things that matter the most are creativity and genuine interest in the problems more than, you know, math. Horsepower. Yeah. This is a dumb question, but how does one develop, how does one identify, you know, creativity and interest in financial topics? And is the obvious answer, those are where the money is? Or like, why. Why did you fall in love with finance as a topic? And is the answer, because that's where the money is.
- [09:59]
B
So first of all, I think that creativity is a personality trait. Doesn't belong to. You're not creative in finance, you know, you're creative in cooking, you're creative in whatever. And it's a mix, I guess, of extroversion, openness to experience, and I don't know what else. I'm not a psychologist, but I do believe that people are genuinely creative. And in fact, you see it sometimes, you ask someone and you find out that, yes, they like writing, they play some instrument badly, and they paint and they do whatever. And so I would say if you go to finance, because that's where the money is, there's nothing wrong with that. And in a way, that's my story. I was a researcher and it wanted to have more money and whatnot. But eventually you stay in finance, or at least in my little domain, because you're genuinely curious about finding out stuff, right?
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C
Why are the problems. Why do they arouse curiosity? Why do the problems of finance intrigue you after years of doing it? What's interesting about those problems as opposed to other domains?
- [11:11]
B
It's really hard for me to say. I think that I read once that a young songwriter asked Bob Dylan how to become a good songwriter. And Bob Dylan just answered, well, what's going on? I say, what do you mean, what's going on? Yeah, what's going on? What's going on in your life? Just look around. So sometimes I get these questions from investors. But how do you keep yourself interested? How do you find problems? It's not a problem. The problems jump at you. There are too many problems. There are too many interesting problems. So if anything, the skill is in sorting the problems in the right order. Right. That is where maybe having some maturity in doing research kicks in. But there are lots of problems, infinite problems, weird problems.
- [11:58]
C
What's your favorite problem right now?
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B
I don't know. Like right now, what are we working on? I mean, we are trying to understand how earnings are monetized, Right. How do you make money in earnings? It's such a basic thing in fundamental equities.
- [12:11]
C
And you mean if you're like, correct about predicting earnings.
- [12:15]
B
Yeah. What are. I mean, without getting too much into details, but, you know, are the relevant variables. Imagine that you had an oracle who told you what the variables are. What would you do with that? What would you do if you had all the information in the world? Right. And everything in your world, in your existence would be like an approximation problem.
- [12:35]
C
There's an incredible stylized story of like the guys hacked into, I think like one of the newswire services and got earnings releases early, like for hundreds of companies. And they traded on this and they had like a 70% success rate, which is great, but also like, means they had a 30%. Like they traded the wrong way, knowing earnings perfectly in advance. It's like a good.
- [12:58]
B
Yeah, yes.
- [12:59]
C
So they had the oracle and you know, it's still hard.
- [13:01]
B
Yes, it's still very hard, actually. Shout out to Victor Hagani, who wrote the paper about 10 years ago on this. He made a organized a simple controlled experiment where it gave basically a biased coin where you, I think, had a success rate of 60%, 40% failure. And you had some capital and you could invest it over time on these informed predictions. And a lot of subjects went bankrupt. Okay, now I think we are better than that. But still there are lots of problems related to trading around an event.
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D
For example, before we get too far away, you mentioned Bob Dylan. It actually reminded me of another Bob Dylan quote, which I'm going to paraphrase poorly, but he basically said when asked about writing songs, do you think that you could write whatever the work that was being referenced now? And he said, I don't think so. It's like the words were in the air and I just plucked them out. They were just sort of hanging in the air and they came to me and it Kind of also rang true with what you were saying about you didn't go looking for problems, they're just there necessarily. I actually want to go back to applied math if it doesn't interrupt the course of conversation too much. You tweeted on June 24 that there's no child prodigies when it comes to poetry, when it comes to applied mathematics. And I'm not saying that you said that you were a prodigy, but you were a child at 14. I mean, how. How at 14 do you realize that you have inaptitude for something like applied mathematics?
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B
Okay, I don't want to flex about this stuff.
- [14:31]
D
No, you should.
- [14:33]
B
I think I'm honestly a little weird. I'm just a little weird, I think, honestly.
- [14:37]
D
But I like prodigy weird.
- [14:39]
B
Or I did have my share of. Yeah. Adults telling me that I was good at this or that or, you know, but, yeah, I mean, what can I say? I'm just a little bit atyp. Also, when I talk to investors, I think investors enjoy my presence because I think I'm incredibly unfiltered for somebody who's talking to them. So it's, like, fun for them. And I was very unfiltered when I talked to my professors in. In school. Sometimes I corrected them. Stuff like this. Yeah, I don't know. Honestly, I don't know.
- [15:13]
C
When you talk to, like, fundamental equity portfolio managers, like, how much matrix algebra is there in your conversations, how quantity are the fundamental PMs or whatever?
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B
I don't think they're quantity, but I think that they're very analytical. So I don't think that they would make great mathematicians, but I think they would make very, very decent applied mathematicians. Actually. They tend to be very analytical, they tend to be very process oriented. And they have also additional qualities that actually I mentioned in that essay. Like they have very little disposition effect. So that's part of being analytical. They have no sunk cost fallacy in them. So even though they don't do a lot of math, but they do some math. Okay. So first of all, they're fluent, in a sense, in basic literacy, but I think it's more their process that is closer to, if not a mathematical one, but more of a scientific one.
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D
And when it comes to being a quant, does it basically boil down to being good at math and being interested in math or things such as statistics and physics? I mean, do you need to have any finance or economics background at all?
- [16:28]
B
So I think that having an economics background is not necessarily a benefit, might even be a disadvantage, actually. But just Based on very few samples that I have a lot of very good, outstanding quantities. Quantitative researchers actually come from physics and specifically from astrophysics. That's the experience that I've had in.
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D
A couple of places in broad brushstrokes. Could you talk about why economics in the small sample size you have, how could that possibly be a detriment?
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C
And why is astrophysics good?
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B
So I can answer the second question more easily. I think that astrophysicists deal with large amounts of data and they deal with observational data. So they don't get to do a lot of experiments. And that's good for finance, right? You deal with a lot of data. You need to know how to have good hygiene for observational data, and you need to have very good theory, like you need to have very good instruments without being falling in love with those instruments. Whereas I think economists. Okay, first of all, my statement is purely empirical. Okay, so I'm just really guessing on economists and I'm going to be hated by all economists or economists in finance, but I do have my issues with their methods, right? So first of all, I think that there is an original scene in economics which is, I think a lot of economics is informed by a desire to be as rigorous as mathematics, right? And so a lot of theoreticians in economics are very deductive in their approach. If you think of, of the unrealistic assumptions behind the welfare theorems or arrow's impossibility theorem or whatnot, or just pick up Samuelson textbooks. And I think this is sort of axiomatic rather than very axiomatic, very deductive. Whereas physicists are very happy to think in terms of small idealized models that apply to a specific domain. And if the model doesn't work out, they will discard and make another one. The grand theory behind physical theories exists. Like there are people who do this for a living. But many, many good theoretical economists, physicists start in the small and then they expand the domain of their models. So economists tend to maybe in a sense fall in love with methods too much, with techniques too much.
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- [21:11]
C
We had Cliff asked this on the podcast a little while ago and my father, not a finance person, listened to the episode and said I still don't know what a quant is. I just read Skimmed your new book which is called the Elements of Quantitative Investing and lays out the elements. What is a quant like? What are the elements? What's the thing that makes someone a quant investor? Or that someone reading a slim book about the elements of quantum investing needs to learn?
- [21:40]
B
Well, if I am being consistent with my book, investing is really about problems and not about specific techniques or anything like this. So it's basically a way to go through the whole investment process from let's say preparing the ingredients to cooking to eating. That is very process driven. Ultimately you would imagine that one thing that quant investing has in common across multiple domains, if you do futures, stocks, event based and whatnot, is I think the number of bets tends to be high in systematic investing. So you can be a very successful macroeconomic investor portfolio manager. And according to even several statements by Buffett, he made 10, 12 very good bets. Okay, so that's great. And that's not quantum vesting. You know, you could put enough PMs making, you know, 20 bets in their lives, and you, you will get a few that have, let's say, 12, 13, right. And. And they will be rich. We do not have that luxury. Like we have to make millions of bets. You know, we trade a portfolio with 3,000 stocks, sometimes in waves of half an hour. You can't make a judgment on all of these bets. So you need a method that reduces the dimens of your problem to something that can be treated in a systematic manner. I don't know if that answers for that, but basically the idea is, think about, if you make a lot of bets, you cannot bet individually. You have to have some kind of heuristic or some kind of method around that.
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C
Right? And to me, the book, sort of the standard method, I guess, in quant investing is you build a factor model of what drives your universe of investments. You're shaking your head.
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B
Yeah. Yes and no. I think yes, because the book has maybe 150 pages on factor models. But also no, because maybe in a hundred years from now, I suspect there will be still something left, but we might have better techniques and not necessary factor models any longer. I don't know.
- [23:49]
C
Wait, I want to go two directions of that. One is, are the better techniques something more neural netty unstructured?
- [23:56]
B
Who knows? Yeah, something like that. I mean, there is a. There is a revolution every five years.
- [24:01]
C
So my other question is like, I've never fully understood like a factor model is like, here are some factors that drive the returns of stocks, and then there's like some residual idiosyncratic returns. There are clearly people whose business is to identify factors and then invest in factors. My impression is that at the places that you work the businesses, the opposite of that is to hedge out your factor risk as much as possible and to get as much idiosyncratic risk as possible. Is that right? And how do you discriminate between a factor return and an idiosyncratic return? What makes a thing a factor as opposed to a.
- [24:47]
B
Okay, so that's a good question. So first, a lot of systematic investing is still about factors, just not the factors that get published in the literature. You know, not the factors that Cliff maybe was talking about. And yet a lot of successful systematic investing is really factor driven in the.
- [25:05]
C
Sense that you have a model that has like 20 factors and like 10 are like value, and you neutralize those and you trade the other 10 kind.
- [25:13]
B
Of you do and you do the rest. You have other terms that matter. So that's one thing. But there are two other things. There are sometimes sources of returns that are factor like, but not quite like factors. So you may have a theme. For example, you may identify a theme in the market that is not pervasive enough or is alive only for a few months, but is there, and it's not only affecting, let's say, two stocks. Right? So these broad themes can be invested on, but cannot really model in the traditional way as traditional factor model. Also, there is a lot of good modeling in factors as opposed to bad modeling. So it seems easy, but it's not that easy. So there is a little bit of craftsmanship in making these models. Okay. And then the third thing is that there are also returns that have nothing to do with factors or almost nothing to do with factors. So if you really know how a company works and you have a little bit of an edge in predicting its future performance, you can bet on it and you make enough bets and again, you will make some money if you repeat and recycle. So even discretionary investing in this sense has inherited a little bit of the spirit of systematic investing.
- [26:32]
C
I think of that as at a pod shop at Bally Osny, you have discretionary investors who know a lot about a company, make bets on the company, and then someone like you tells them these are your factor exposures. You have to get those down to zero so that you're making pure bets on your idiosyncratic knowledge of the company. Is that like, kind of right?
- [26:53]
B
Kind of right? Yeah. I think that at this point it is very interesting how the mind of professional portfolio managers has been remolded in a factor based world so that a modern portfolio manager, discretionary portfolio manager, thinks in factors. So I don't even need to tell them, hey, this is your exposure. They see their exposure. They have the tools to see it and they control it in real time with minimal intervention from me. So what we do is we have a good team that models factors in a way that is suitable for the investment universe and style in which they operate. That's again, very, very sophisticated and difficult. And portfolio managers use that and they neutralize. It's become second nature.
- [27:42]
C
And they've internalized that. Their goal is to create idiosyncratic alpha rather than factoring space.
- [27:46]
B
That's right.
- [27:46]
C
I feel like a criticism that people sometimes have of the pod shop model is that there's some universe of factors that exist in commercial models and that are known in the literature. And then portfolio managers have a set of exposures to factors that are sort of inchoate or unknown. But ultimately, when you become really, really smart, you will know that actually the bet they were making was some knowing the company really well means they had exposure to some personality factor in the CEO or something that eventually someone will be able to write that down and it'll come out of being idiosyncratic and become a factor. And then I don't know what happens.
- [28:27]
B
I think that there is some truth to that. There is definitely some truth to that in the sense that sometimes portfolio managers, especially in specific sectors, will use some heuristics that you could call characteristics in a factor model, but they are not in a factor model. And then they trade that. However, it's also true that the decision that enters a particular investment is usually not that simple as taking a ratio in a spreadsheet. So it's a bit more complicated than that. You could still argue that there is a factor, right. And what's the factor is ultimately the set of theses that are highly correlated or relatively highly correlated across portfolio managers, across firms. Because if there is an expected return, and if you have skill and you have sufficient skill to be close to the best possible portfolio, you have to be also relatively close to other people approximating that best possible portfolio. Right. So then, then it becomes a truism, right? There is a factor, and that's the factor of informed investors. So it's true, Right.
- [29:35]
C
I think of it as like, there's a scientific process that everyone is pursuing. They hire the best people and they do the best work to pursue that scientific process. And so they'll eventually converge on something that is like truth. But that means buying all the same stocks.
- [29:51]
B
Yes, it's very difficult to get to that truth.
- [29:53]
C
Sure. Sort of abstract.
- [29:58]
B
It's not. Okay, let's hire up the truth.
- [30:00]
C
It would be weird if there weren't hurting among like the best.
- [30:02]
B
Yes, yes, there is. There is. And by the way, and this brings to one of the limitations of factor models, right? Which is effectively a factor model is a form of glorified regression over time. Right. And behind a regression, there is a bit of an assumption, to some extent, of independent observations over time. And the market and hedge funds are not independent random variables. They are super dependent random variables. And they are in a sort of continuous indirect conversation through their portfolios. And sometimes the conversation gets really nasty when one hedge fund is in state of distress and all of a sudden, or not even a hedge fund, it could be Also an institutional investor and they decide to liquidate part of their portfolio and then it becomes a process where you have a lot of reflexivity and positive feedback and everybody suffers. And in this case, factor models don't really. You can still identify like if the system is running at temperature with some characteristics, but they're not factors in the traditional sense.
- [31:08]
D
I do want to talk about, before we move too far away, I do want to talk a little bit about how and if factors can die because, you know, we've talked a bit about identifying factors. But when do you decide that, that this doesn't work anymore necessarily, that the market has fundamentally changed and this worked maybe 10 years ago, maybe 15 years ago, but maybe now it's devolved.
- [31:35]
B
Well, there is the good old reason, which is people make mistakes in the sense that we think that there is a factor and then we look back and there is no factor. Right. So there are so many factors that some of them have got to be a little bit redundant. So that's one reason. Right. So just pure, in a sense, research revisions. And then there is also the fact that there are two other things that can happen. One is the moment that you tell people that there is a factor, the factor comes into being to some extent. Right. So it's never black and white, that the factor did not exist. Maybe the factor did exist and then the moment you identify it, it becomes more existent.
- [32:21]
D
Like as you know, you speak it into existence.
- [32:23]
B
Yeah, yeah. So ESG is one case where the focal point that it became makes into an investable theme.
- [32:32]
D
I thought that was just blackrock pumping esg.
- [32:34]
B
It's possible, but everybody had to incorporate it in some sense. Right. So it became a major source of revenue for the vendors. Right, right. So that's one thing. And then there is the adaptive nature of the market. So things that before generated a priced return. So you run some risk, you made some money and then it becomes table stakes, it becomes incorporated into factor models, it becomes.
- [33:02]
C
Becomes a smart beta etf.
- [33:04]
B
It becomes a smart beta and then it becomes. So I think you could say definitely that medium term momentum worked much better. You could say that even, you know, short term reversal worked better. There were years when short interest was great and there are factors or data sources that work well now and then maybe in five years will become known and become part of the, I mean, credit card data. Right. For consumer. That was like there were people who were making a lot of money in 2011 through, I don't know, 16, 17. And then it's become that it's very hard to make money in that.
- [33:41]
C
You said the market is a conversation among hedge funds. One thing that I think might be true, that I'm not entirely sure of, is to what extent the market is a conversation among four hedge funds. Now to what extent is the marginal pricer of every stock a portfolio manager at one of the places you've worked?
- [34:01]
B
It's a very good question. I don't really have the answer to this. I'm not sure.
- [34:06]
C
What is the intuition at places like that the market price is determined by the collective thought of the top people at the top hedge funds? Or is it like we are a little bump on the market and we're trading against the whole random universe?
- [34:21]
B
I mean you'd like to think that the prices are determined by the marginal informed investor. Right. So by people like us at the time horizon where we predict. Right. Which is not the same as at the time horizon of half a day. Right. That's a different player.
- [34:36]
C
What is your time horizon like? I think of it as well.
- [34:39]
B
It depends. Well yes, it depends. Within a hedge fund you have a variety of. Even within longshore equities you have portfolio managers who are very tactical and so they think in terms of. They have strong daily or intraday alpha even though they're fully discretionary up to PMs that think easily in terms of months. Also depends on the sector. So financials typically probably monetizes a little bit less on earnings and. And tends to have a longer horizon. Banks are basically modeling giant balance sheets. Right. And then in a hedge fund you also have systematic. But even in systematic there are all sorts of timescales and this cacophony makes the prices. I really don't know. I said another question is basically how inefficient is the market? How incorrect are the prices are within a factor of two, like Black used to say. Or I don't know, I don't think that the market is becoming so super efficient, but it seems to be more efficient.
- [35:38]
C
I do feel like one of the big stories is the rise of these big multi strategy hedge funds. You would hope, maybe you wouldn't hope because it's sort of governing the economic interest, but one might hope that the rise of these big multi strategy hedge funds and a lot of capital being allocated to them would observably make the market more efficient.
- [35:59]
B
Yeah, I don't know if observably holds. It's really hard to like can you, can you tell when a bubble is forming?
- [36:10]
D
A lot of people would say that they can Yeah.
- [36:14]
B
I can point you to a few papers that, you know, made all the wrong calls. I don't want to shame academics in public.
- [36:24]
D
I do like the idea that the market is a conversation between four hedge funds because I live in the ETF world and, you know, the big thing is passive is just distorting the market and there's no price discovery anymore. And it sounds like that's on the opposite end of that spectrum.
- [36:41]
B
I didn't say. I think exactly that It's a conversation between. It's a beautiful thing to say, though. It sounds really cool.
- [36:49]
D
It does sound good.
- [36:50]
B
Sounds good.
- [36:50]
D
Great podcasts.
- [36:52]
B
Yeah. Yeah, that's great.
- [36:54]
C
Yeah.
- [36:54]
B
But I think your question is whether the rise of passive has made markets less efficient.
- [36:59]
D
It's more of a statement. I don't think I was a bad podcaster and didn't actually ask a question, but.
- [37:05]
B
Okay, how do you know?
- [37:07]
D
How do I know that passive is destroying the market? People on Twitter tell me so.
- [37:11]
B
Oh, okay. Don't trust people on Twitter.
- [37:16]
D
That's rule number one.
- [37:17]
B
Rule number one. No, I don't know. I mean, the rise of passive has made index rebalancing a weirder strategy. Right. So where the margins have compressed, but the size has become so big that you can still make money in it. And periodically, it's a very cyclical strategy.
- [37:35]
C
So I don't know if you're an index rebalancing pm. Do you take, like eight months of vacation a year and just not do it all day when there's not a rebound?
- [37:48]
B
Not the ones I know who probably listen to this podcast. They work very hard.
- [37:53]
C
Sure.
- [37:54]
D
You want to name their names, too?
- [37:57]
C
Indexes aren't being rebalanced all the time. They're planning on.
- [38:00]
D
They rebalance more than you would think.
- [38:02]
B
Index rebalancing is another poster child for a strategy that seems so simple that everybody can talk about it. And then it's full of nuances and it requires a lot of skill to trade effectively.
- [38:16]
C
I believe that just because I thought a little bit about just the sort of accounting of. You basically know how many index funds there are. You, let's say, can predict what will come in and out of the index and what the. So there's some mechanics around figuring out the market caps that'll come in and whatever, but then it feels like the unknown is like, who else is doing the rebalancing strategy? Is that right?
- [38:40]
B
I think you're mostly right, but I don't want to say because out of respect for the PMs that I know, fair enough.
- [38:51]
C
So we had Cliff Asness on a few weeks ago and to me Cliff Asness is a quantitative investor, a systematic investor. But what he's doing is sort of recognizably what a sort of traditional asset manager would do. He's like trying to find companies that are undervalued. He talked about it's like being a Graham and Dodd investor. You want valuation plus a catalyst. And he's like, well, we're trading value and momentum. And you look at what ADR2 is, maybe a little different, but there's the high frequency trading firms. You can model those as those are quantitative versions of a voice market maker 50 years ago where they're trying to keep inventory flat and trying to make the bid ask spread. So those are very traditional economic functions that have been turned into systematic. What's the intuition for what a Bally Osny or a Citadel or a Millennium does? What business are you in, do you think? As a philosophical matter? One thing I think, I think about.
- [39:57]
B
Index, you're asking from a social standpoint.
- [40:00]
C
Or so the index rebalancing to me feels like the sort of trade, and I think to some extent was the sort of trade that an investment bank would have done 20 years ago, 30 years ago. And like some of that function I think has moved to the big multi manager hedge funds. But I wonder from where you sit how you see the role in the financial markets of those firms.
- [40:21]
B
So at a very high level we don't do anything different than everybody else in the sense that what we provide is always this, right? We provide shifting time preferences, which means we provide liquidity, we house risk for people who don't want to hold it right now. And that's what you do when you do index rebalancing, right? That's what you do when you do merger ARP and when you do the various subtypes of basis trades, right? So we do provide liquidity, which is very important. And then the second thing we again, very high level, we provide price discovery, right? So we study the firms and we think, okay, this is at the margin mispriced and we are going to short it or we're going to invest in it. And that's a beautiful thing. So we do it at a different timescale, right? So you always want to do things at the margin where you don't have a lot of other participants and at the margin of the, let's say month to three month investment horizon, there are not that many participants. So in the words of another hedge Fund manager I cannot name. But he said once, you know, we don't invest in securities. We dated them and so we are in the dating service. Not that many people are doing it and so we do it. But I would say also this, right, not at the social level. I just want to answer at my personal level what we do, we are a massive filter of talent and the talent that we hire is a massive filter of information. So it's like information squared.
- [41:51]
C
Maybe this is like a bad question, but do you think that long only asset managers are worse than they were 30 years ago because that filter has been so successful? In other words, there are lots of jobs you could have gotten in Finance in 1990, but there's a clear hierarchy now.
- [42:10]
B
I think that the market and the set of investors has learned. I think the distinction between beta and alpha has been useful for investors. Active investors who are mostly long only I think have suffered from this distinction because the vast majority of them underperforms their benchmarks and so there is no reason for them to exist. And then what we do is we provide really uncorrelated returns to the benchmarks, to most factors. And investors want that. Right. So there is a future where active investors, long only investors, asset managers will become even less influential, smaller. And also I think of that as.
- [42:59]
C
Like a customer demand side, but also like a talent filter side. Right?
- [43:03]
B
Yeah. And then the interesting thing is, and then there is also a process where the multi manager platforms are able to make the business model of a single portfolio manager that is not sustainable in isolation working in this kind of federated system. So why would you or how could you survive as a single portfolio manager hedge fund nowadays it's really, really diffic. But you can do it in a multi manager platform provided that you have sufficient talent, sufficient edge.
- [43:35]
D
That's also where you can blame the passive influence on Twitter if you're a long manager, that it's impossible to beat the market now because you just have this money constantly pouring in.
- [43:44]
B
Yeah, I don't disagree. Yeah.
- [43:47]
C
I have one more question on social roles which is like you've worked at most of the big paw jobs but you also worked at hrt. What's the difference in roles and in like what they do all day? Because HRT I think of as a classic like high frequency trading firm where I don't know that they're exactly a market maker but they're certainly on the higher frequency side. And then like the pod shops have a lower frequency and a, you know, they're not prop. They're running hedge funds. Like what's the cultural and role and differences?
- [44:16]
B
Yeah. Okay. So I briefly mentioned HRT in an interview with the Financial Times and my manager told me that people at HRT were both annoyed and delighted by what I had said about hrt. I think HRT is a really special place, even in the context of prop trading firms. So I'm a little bit hesitant to just bin them as a representative. Right.
- [44:46]
C
Tell me why they're not representative.
- [44:48]
B
They're not representative because there is something in the culture of HRT that is special. Okay. It's collaborative, it's truly kind. Yeah. So I think it's a great place to work and it is fundamentally monolithic. So you have, you know, sharing of ideas and you can work at the intersection of these ideas. It's also a place that is very tech oriented. So it's a bit of a technology firm operating in the financial space. And because of that, it also attracts, I think, the best technical talent that I've ever worked with. It's just a pleasure to work with great technologists, people who are very competent in that respect. So nothing against the hedge funds. I love hedge funds for different reasons. I love bam, which is also very collaborative and it's an investment company. But HRT has a technical side to it and also again, a cultural side to it. It's great.
- [45:54]
A
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C
Exclusions apply.
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- [48:24]
D
We didn't talk about AI. We don't have to talk about AI.
- [48:27]
B
AI, of course you have to talk about AI.
- [48:30]
C
So I have three models of how investing works systematic. One is like you have some economic intuition and you build a model of the stock market that predicts prices. Another way is a sort of neural netty AI way where you throw a lot of data at a neural net and it builds its own model of how to predict stock prices. And then the third model is you get really good at prompt engineering and you go to ChatGPT and you say what stocks will go up? But you ask it in the right way and then ChatGPT tells you what stocks will go up. How good are those ones? Okay, I assume the third model no 1 uses, but someone uses.
- [49:08]
D
I think a lot of people use that.
- [49:09]
B
All right, so first thing like, okay, nobody knows anything and anybody saying the opposite should be heavily discounted. Okay, so we agree on this. And so let's forget for a second all the technical details of AI just from a pure industrial organization standpoint, right? So what's going to happen? And consider AI just like another technology like Internet and whatnot, right? So first of all, we are going to observe economies of scale. So there's going to be concentration and there was going to be some kind of monopolistic competition. I was thinking about Bloomberg specifically, which could be, I hope for you people to be among the winners because you have a good starting Point, right? You have lots of data, you have a customer base. And maybe in the future we'll finally not see the good old Bloomberg terminal, which has been kind of unchanged since I remember it. And instead people will just prompt Bloomberg to conduct very complex actions where it will act on a sequence of keywords and connect them and give you, like, a much more valuable product for which Bloomberg will charge twice as much as they do already. So this is going to happen in one form or another. If it's not Bloomer, somebody else will do it. Okay. But the same thing applies to other areas of finance. So maybe once upon a time, you know, a big, sufficiently big fund could build their own client for email. Right. Of course, nobody builds a client for email anymore. Right. So a lot of this stuff gets outsourced. We will outsource at some point some of the functions that we conduct internally using AI to other AI agents. It's perfectly fine. So this will become a utility to some extent. Those functions include, like, well, not stock picking. Not stock picking. I think that the functions that we will see available are essentially like another self, like another Matt Levine who can be a good baseline for you. You could feed a post train an AI system with all your gazillions of words, and that agent will reproduce your sense of humor, your investigative style and everything. Okay. It's a good approximation. It's not going to be perfect, but why not, Right? So I would be very happy to have a replica of myself that can answer most simple questions. Now, I think that the decision to invest in a particular stock is a very demanding cognitive function, and I don't see that really being replicated very well. But I think that this will be baselined to some extent.
- [52:00]
C
Is it a demanding cognitive function because it exists in a competitive market? So this sort of whatever the cognitive function is going to get, the baseline is always going to get higher because someone else will have the same information as you do or the same.
- [52:15]
B
Well, this is getting really in the highly speculative side of things. I think that in order for an AI agent to be good at this, they have to be able to experience the world the same way that an investor experiences it. And our inputs are much more complex than just a string of text or YouTube videos. Right. We have a model of the world which comes from visually experiencing the world, talking to humans, consuming the goods. Right. Anything. It's vastly more complex than the way an AI system right now experiences the world and also influences the world. So an investor has a fundamentally different experience of a company than an LLM that has an experience that is mediated by multiple layers of processing. They learn about a company through text that is written by somebody. So I don't think that's in danger for the time being. But maybe again in five years, maybe we will have our glasses fitting our experiences to AI agents. Who knows, right? But I don't think that it's that close and I don't think AI is that smart also. So I think that having a baseline system would be already pretty good.
- [53:31]
D
That's somewhat comforting that our experiences count for something. Our physical experience of the world.
- [53:37]
C
It's interesting because I always think of the comparison as like, like investing in self driving cars where investors do a lot of things. One thing they do a lot is sit at a desk and read computers and look at numbers and those things seem like things that a computer can do well, whereas drivers have physical reflexes and have a complicated field of vision. I always thought investing should be easier than self driving cars for a computer and a master. But you, I don't think you're alone in this. Think of investing as the great liberal art where it's like you incorporate all of human experience. And so the AI can't really, let's.
- [54:15]
B
Take the metaphor to extreme consequences. Imagine that you had a system that is the equivalent of a perfect self driving car in investing. So now I'm giving you a machine, a box that is telling you the long term value, if not the returns, because the moment that the value is known, you immediately equilibrate to that level. So imagine that you know true value of everything because a box tells you so and it's infallible. It's an oracle. Okay. Would you think that finance stops existing? I wouldn't say so, right? So I think that a lot of arbitrage trades, you know, would maybe change significantly. But every risk, right, every return would be correctly priced by the risk of the agents trading it. So there still would be trading because we still have different preferences, but basically every risk would be priced. There would be in a sense less alpha, but finance will still exist.
- [55:11]
C
There's a lot of like service provision.
- [55:12]
B
Like liquidity provision, service provision, liquidity provision and yeah, and so the liquidity provision would still exist. The informational services maybe will stop existing in the current form, but that's okay. I think that we'll all still be employed.
- [55:27]
C
It's an interesting way to think about it because I do think, like we talked about, like one thing that the big hedge funds do is things that have the flavor of liquidity provisions. Basis Trades and, and merger arb and whatever things that I think of as something that a bank would have done 30 years ago and that now a big hedge fund does. And then another thing they do has the flavor of information provision where it's getting prices right. To me those things seem quite intellectually separate, but I guess they feed each other in the sense that the better you are at prices, the better you can be at liquidity provision. Is that sort of right? You wouldn't want to do merger arb trades if you didn't know the value of the stock.
- [56:07]
B
At short horizon, liquidity provision and, and information tend to be very closely related. Like, you know, a limit. If you are good at, if you're good at crossing, if you're good at crossing, you should be pretty good at adding. Okay, adding liquidity. So you know, by this I mean, like you could make, you know, a profit by posting a lot of limit orders and providing liquidity to the market or crossing the spread and making money with predicting the future prices. If you're good at one, you're good at the other, most likely right at that timescale. I think that this though might. I'm not sure because I haven't thought about this very carefully, but I think this might decouple at a longer timescale. So one year out, I'm not sure. And in any case, at that timescale, it's really difficult for an AI or for a human being, anyone. There are not that many hard data. Even the unstructured data are not that many. So it's a very difficult problem. It's decoupled, it's complicated. But I tend to believe at longer time scales you have more or less liquidity provisioning and violations of law of one price on one side and predicting on the other side.
- [57:22]
C
But you combine both.
- [57:24]
B
But you can combine both and it's a very potent mix. Right.
- [57:27]
C
Is it normally different people? It is, right?
- [57:29]
B
Very different people, for sure.
- [57:31]
C
The different pods are different.
- [57:32]
B
Very different people, Very different cultures. Yeah.
- [57:36]
C
Can you summarize the difference in cultures between, like I, I have a guess, but.
- [57:40]
B
Well, as you said, people who typically trade in arb trades, if not historically, but also historically come from banks.
- [57:48]
A
Yeah.
- [57:49]
B
Right. Whereas you still can see long only portfolio managers being recycled and reformatted into long short portfolio managers. You can have an excellent short short specialist becoming a long short portfolio manager. Like it happened.
- [58:06]
C
I mean, my sense is that the people on the information provision long short side are more academic and research oriented and the people on the arb side are more trading sell side traders.
- [58:19]
B
Yeah, I think you can actually have very good long, short portfolio managers who were journalists in their past lives.
- [58:26]
C
I've heard of some of them. Thought about this?
- [58:29]
B
I think I've thought about it.
- [58:30]
C
No, just like idly.
- [58:31]
B
Big reveal.
- [58:32]
D
No, not breaking news on your podcast.
- [58:37]
C
I've noticed how much money they make that's better than podcasting. Not thought about it in the sense that I'd be good at it. Just in the sense that the money is good.
- [58:47]
D
You could be bad at it and paid really well for a short amount of time.
- [58:51]
C
I don't know that that's true. Actually. They're an excellent talent filter or so I hear.
- [58:58]
B
Yes, I think that you could interest a few edge funds.
- [59:02]
D
They might be listening.
- [59:04]
B
They might be listening.
- [59:07]
C
I would end on that.
- [59:09]
B
I'm closing on a high note.
- [59:13]
C
Kathy, thanks for coming on the it was a pleasure.
- [59:16]
B
Thanks for having me.
- [59:23]
C
And that was the Money Stuff Podcast. I'm Matt Levine.
- [59:25]
D
And I'm Katie Greifeld.
- [59:26]
C
You can find my work by subscribing to the Money stuff newsletter on Bloomberg.com.
- [59:31]
D
And you can find me on Bloomberg TV every day on Open Interest between 9 to 11am Eastern.
- [59:37]
C
We'd love to hear from you. You can send an email to moneypodloomburg.net Ask us a question and we might answer it on air.
- [59:43]
D
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.
- [59:49]
C
The Money Stuff Podcast is produced by Anna Mazarakis and Moses Andam.
- [59:53]
D
Our theme music was composed by Blake.
- [59:55]
C
Maples, Brendan Frances Newnham is our executive.
- [59:57]
D
Producer and Sage Bauman is Bloomberg's Head of Podcasts.
- [60:00]
C
Thanks for listening to the Money Stuff Podcast. We'll be back next week with more stuff.
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B
Ah, greetings from my bath festive friends.
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A
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B
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A
Perfect gift this holiday on Amazon. Janice Torres here and I'm Austin Hankwitz. We host the podcast Mind the Business Small Business Success Stories produced by Ruby Studio in partnership with Intuit QuickBooks.
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D
We're back for season four to talk to some incredible small business owners.
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A
The big thing about working at tech is that it's ever evolving, ever changing. Everyone's a rookie.
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B
That's how fast the industry is changing.
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A
So what I'm really excited about is to be part of that change. So listen on the iHeartRadio app, Apple Podcasts or wherever you get your podcast.