Giuseppe Paleologo on Quant Investing at Multi-Strat Hedge Funds
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Hello and welcome to another episode of the Odd Lodge podcast. I'm Jill Wiesenthal, normally joined by my co-host Tracy Alloway, but she's on vacation today, so it's just me in this intro. But in today's episode, you will hear a conversation taped live at Bloomberg's Reimagining Information Forum on June 12th. We spoke with Gapi Paliolago, Global Head of Quantitative Research at Baliasni Asset Management. He has a new book out. It's called The Elements of Quantitative Investing.

Neither of us have read it because it would go way over our heads because we're not quant, so we don't know how to read that stuff. But Gappy is great at explaining all of this stuff in clear English. So we had a great conversation and we hope you enjoy listening to it. So just to begin, I'm going to start with a really, really dumb question, possibly, but isn't all investing quant investing nowadays? I mean, every investor has access to some form of quantitative tool. They're all using numbers. Yeah. I guess yes. End of answer. Yeah.

I think so. I mean, pretty much everybody uses some kind of quantitative overlay, right? But to different degrees. So I have a friend who worked for one of the tiger cubs and they refused to use a sharp

They refused to use logs in a spreadsheet because they said that they were dangerous. Probably they took the log of a negative number. And so, yeah, no, to different degrees. But yes, there is some quantitative culture seeping through. Okay, so what defines quantitative investing? How would you differentiate that from, I don't know, value investing, discretionary investing? Okay, I think that there are several possible answers.

So I'm going to go with one answer that I read in my life as a quant, I think. It's a Wiley book. It's a very good book, by the way. And I think Cliff Asness defined quantitative investing as...

Basically investing in a large cross-section of assets having a relatively low edge, low expected return in all of them. And so that's his definition. But it's not quite...

I think, complete enough at this point, because you can also be a quantitative investor trading a relatively narrow cross-section of assets, but with high frequency, right?

What matters really is the number of bets, in a sense, that you are going to take. So I think that probably is-- if you have a large number of independent bets or quasi-independent bets, this means that you need to be able to scale your method to a large number of independent bets. And this means that you are in some way a quantitative investor.

Speaking of roles and jobs, global head of quantitative research at Belyazny. What's your job? You've been there about six months. What is the job at a fund, at a firm like Belyazny? Okay, global head of quantitative research. Okay, so basically I am the head of quantitative research for equities. And maybe one day in the future I will do some commodities research.

or fixed income, but I'm perfectly happy to serve equities, you know, both discretionary and systematic. What we do is, I mean, my group mostly, I mean, I am in meetings, so I don't do any work. So we, in a sense, provide centralized quantitative services for the firm. So the first backbone thing that we do is you develop factor models

wherever you can, right? So for equities at different horizons, ideally, you would like to develop them for other asset classes. But, you know, factor models are the backbone of a lot of quantitative investing nowadays.

And then hedging at the firm level and at the individual PM levels, which is apparently very simple, but actually it's very deep as a problem. And then we do portfolio advisory services, which is basically you go to PMs, you help them construct better portfolios, you help them understand their performance, which is extremely important, manage their risk, manage their drawdown, on occasion be their therapist,

But this is what we do. I know you're in meetings all day, but if you were someone on your team, how would you be coming up with actual ideas for factors? I hear people who sometimes come up with ideas from all thoughts episodes. Some of them have even turned out reasonably okay. But how does idea generation work? You sit down, you're like, I need to come up with a new factor today. What are you doing? What are you looking at?

Okay, I want to specify a little bit more what's a factor because otherwise it gets a little bit too vague. So there are factors and factors. So there are some factors that are real factors. And what are those? Those are essentially attributes of some kind that you can assign to your investable universe. And there are...

sources of returns that affect the individual securities through this characteristic. And they are pervasive. So every asset is in some form affected by the systematic source of return, number one. So they've got to be pervasive.

The second thing is they got to be persistent, right? So it's not the case that I have a lot of factor returns for two months and then nothing for 10 months, right? So that's not really a factor. And then possibly the third characteristic is that they have to be interesting. So they have to be in some way vaguely interpretable. So when you match these requirements, it's a factor. Now...

Now, imagine that you have the Trump factor. Let's say if Trump wins, a few stocks will definitely benefit. A few stocks will definitely not benefit from the election of Trump versus Kamala Harris. Another possibility

source could be, well, tariffs, right? Another source could be AI. Okay, AI definitely doesn't fit the characteristic of being pervasive because there is a relatively small universe that's affected by the AI theme. It's likely not going to be persistent. So it wasn't here like a few years ago and it will probably not be here in five years because everything will be to some extent AI.

It's interesting, but that's a theme. It's not a factor. That's what I would call a theme. And there are also some mathematical characteristics of a factor versus a theme. Like what? So basically you can create a portfolio that tracks a factor and this portfolio will have

relatively small idiosyncratic risk. So it will be truly a reproduction of the systematic source of return that you were observing through the assets. So imagine that this systematic source exists, but you do not observe it directly. It's latent. It's out there. But you can actually reconstruct it with a portfolio. A theme is, let's say, 10 assets.

you cannot really reconstruct it the same way because 10 assets are just too few to diversify away the idiosyncratic source of returns of the individual assets. So when you're thinking about factor identification, how much of the money that you make, the actual returns,

come from essentially factor identification or being able to identify a factor before other measure, identify a factor that exists before other competitors out there in the market.

Okay, that's a great question because I think I know the answer. Okay, great. But the reality is this. I think somebody else's factor is my alpha and vice versa, right? Say more. There are well-known factors, let's say some variety of value and momentum or reversion, and you can bet on those and you diversify away everything else.

And what you get is basically you get some returns that are priced, priced in the sense that, as you know, you pay basically some risk for that, right? So this is priced return, and that's great. But once upon a time, like these were not public knowledge. If you were lucky enough to be a hedge fund in the 80s, and I've met a few, you know, and you were maybe also investing in Europe, right?

These factors were really working very well and they were alpha. They were not called factors. You know, the first, I think, published paper is probably 89 for momentum, right? Now, there is alpha and alpha is basically, ideally would be a return that has no associated risk to it. It hardly ever exists. So what you really have are factors that,

that exist at some frequency or in some universe or with some characteristic that nobody else has found yet. And so they can be exploited more. How do you make sure that when you're isolating a particular factor, you're not

accidentally taking into account some other dynamics. So, you know, maybe you want to invest in a bunch of companies with like pricing power during the tariffs, but actually your cohort of companies ends up just looking like a bunch of big tech companies or something like that.

I mean, the short answer without explanation is that you can, but the long answer is a little bit more involved. If you have true characteristics, like, I don't know, a tariff and a tech classification that are 100% correlated, well, then you really have only one. You don't need both, right? So, okay. But if I have in my...

let's say, arsenal of factors, if I have multiple factors, they're somewhat overlapping, but not completely overlapping, then you can build a portfolio that separates the impact of one from the other. So you try to isolate them. You can isolate them. You can kind of purify them. Now, there is also the scenario where

There are factors that are not in the model and they should be. And basically, they complicate the picture a little bit. But otherwise, if you have a reasonable model, you're going to be able to separate them to understand what's the relationship. You can create a portfolio that exploits the first one and then create a second portfolio that is uncorrelated to the first one that exploits the second one.

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Just zooming out for a second again, and this sort of relates to Tracy's first question, but also I guess relates to my first question. If you have a fund and it has various PMs and analysts in there, is there a difference between quant at your level, which is at the fund level, versus say a pod or a PM whose specialty is quant trading? And are there different definitions or different senses in which that term can apply? Yeah, I mean,

The fact is that quant is a very generic label nowadays. So there are many quants and they do all sorts of very interesting jobs. Some of them are just differentiated because they live in different constructs. So nowadays in a platform, especially in a quantitative one, it's not impossible to see pods and center groups.

Okay, so that's one distinction. So what's the difference? In a pod, you typically have a siloed group. I'm probably stating the obvious, but you have a siloed group. They don't communicate with other pods. You want, at the firm level, to have independent sources of alphas. And their payout typically is a percentage of their P&L after costs. Okay, and then you're a quant in a pod.

In a center group, typically you are part of a larger group and the group will hopefully have large capacity. So these are, you know, a larger program, like a larger research program. Their compensation tends to be more discretionary and that's a center group.

Then you have all sorts of other quants. So you have people like me who serve the firm at the center level. I also serve the leadership of the firm. And then you have people doing, for example, execution research, which is extremely complex and interesting, right? So

It's not black and white. You can do execution research and be responsible for some P&L. It's very, very rich nowadays and very specialized. I was actually going to ask about execution because when we're talking about quant investing, I think a lot of questions are around factors and idea generation, but you have all the

I would assume boring stuff like liquidity, trading costs that you also have to think about. How do you actually incorporate those into your strategies?

So you can do it in a variety of ways. It depends, first of all, what position the firm occupies in the ecosystem. So if you are a high-frequency trading company, most likely you are using your own capital because you are capacity constrained, so you don't need a lot of capital. So those firms exploit market microstructure-level information. Okay, so...

in a sense a high frequency trading firm does not have a market impact model in the traditional sense they don't see parent orders right they execute at the microscopic level if you are a hedge fund typically you trade a lot you have your own data set of orders um

These data sets differ a lot. So you could have a market impact model for a quantitative trading group or a strategy, and you could have a different market impact model for hedging and a different market impact model for fundamental investing. And then what you get is basically a term called

a function that you place in your optimization problem that hopefully helps you size the portfolio or trade the portfolio optimally. And this is extremely important. You know, market impact is a very, very sizable fraction of the lost P&L of a firm.

What, as of today, what value is there in your world of specifically generative AI, LLMs, et cetera? How do you currently or not currently get actual value out of them? Okay, so on this, I have really relatively little to say that's original. Tell us everything your employer is doing with AI. Yes, and I'll send you the resume. Thank you.

But I think, okay, just let's recap the basics, right? So the basics are, at least for the time being, everybody is trying to be more productive with AI, right? So...

You want to have all your documents. You want to have now, you know, Web Perplexity has a finance module. I think one day soon, maybe Bloomberg will not have the keywords any longer. You just give, you know, Bloomberg a task and it will grab all the pieces of information and hand it over to you. And maybe you can schedule it. All of this is relatively table stakes.

I mean, the agentic aspect is not yet, but it will become pretty soon. I think it's going to be very hard to compute with the likes of maybe Bloomberg, but for sure, let's say, you know, the big hyperscalers. So that's one. At the investment level, it's much more complicated. So

In strategies where there is a natural richness in data, you can definitely use, if not deep learning or AI, but you can definitely use very advanced machine learning algorithms. And you do not have a data snooping problem. You do not have a backtesting problem. And so you are in a data rich environment and you can do that. And

And it's not a secret that, for example, XTX has a very large on-prem, you know, number of NVIDIA cards. I don't remember, H100 or something like that. So that's one thing, right? The question is really what's going to happen to the slower investment styles. And my...

My view is that hopefully large firms like mine will have an advantage, but we'll see, right? Why? Because we do have the scale. We have a large number of PMs. We have a lot of historical data. We have a lot of proprietary data that nobody else has. So maybe that will work out.

But how to make it happen, I don't know, because things are changing so fast. And also, I'm relatively a tourist in the area, so I'm trying to learn a little bit more about it. ♪

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I mean, is that true? If I get something really cool and unique, I can automatically become, I don't know, a billionaire trader if I can figure out how to execute on it? Is that all there is? Maybe yes. I have very weak beliefs on this. I don't know. Maybe yes. We'll find out. Well, so where are people getting interesting data sets from? I mean, you get interesting data from observing human beings actually investing. And you don't get to see a great PM investing, but I do.

That's the benefit. So from your central position,

you just get to see a lot of activity and you get to see novel data that other people don't get to see simply by being in the center of all of these different trades and everything. And that gives you a sort of higher abstraction layer or whatever it is that someone else in the market doesn't have. Yeah. And it's possible that not in the distant future, good PMs will become good because they can improve

on themselves by basically playing or training or having a baseline of an agent that reproduces their behavior. So, you know, there is an alter gap, well, I'm not a PM, but an alter whatever who says, what would you do, right? And you get a baseline behavior. And then you can think about it and you could say, well, I would do something different. And then that becomes an example in a reinforcement learning process where you

the AI keeps learning from you and you keep improving because the baseline is changing.

So before we came out here, I asked Perplexity to come up with a new factor. And it came up with something called the policy agility factor, which is supposed to be that countries that display policy flexibility have better outperformance over the longer term. Countries that are able to more quickly adapt to changing situations are outperformers over the long run. Can you grade that factor? I didn't do a backtest.

But like if someone brought you an idea like that, not me, perplexity, I don't want you to insult me over the next five minutes. What would you say to them? What are the problems with this? I mean, no major problems. There are questions. So the first thing that you want to make sure is that

if AI, whatever it means, brings to you a definition, right? That definition should be at a point in time and should not be trained on all the past data, right? So number one, you want to do that. Because if you backtest that feature and in a way perplexity has already tested it, it's not a fair play. The backtest will look great.

So unfortunately, we live in a world where some factors will never be backtestable. So you don't know whether they work or they don't work, right? You just know that you cannot test them in advance. Like policy agility. This seems to be a very low turnover factor, right? And it seems to be probably a very low sharp factor. And a low universe. And a small universe. So how do you know...

Well, probably you want to make sure that it makes sense and maybe you can start putting a small volatility allocation to it. And then you would build it up as you watch it perform? Yes, out of sample. Okay, so speaking of backtests, I have one more question. But it seems like quant investing, part of the issue with this is you are looking back at historical data. That's all you have. You don't have data about the future, unfortunately.

It strikes me as hard to deal with regime changes. So when you have a big break in how something works in finance or markets or the global economy, how does quant investing actually take into account those sorts of risks? Like say, you know, a lot of investing is based on the idea that bonds and stocks are going to move inversely to each other. And then in 2022, they started moving together.

I think that most people with a quantitative background in finance will tell you that regime change is very difficult to detect and to act on in an effective manner. So I think that's been my experience at least, right? So in every possible application I've tried and it really never works for me. Maybe it works for somebody else.

What I think it's a bit easier to do is to detect regime change in a human being. So instead of trying to use, you know, there are many, many algorithms for regime change. You know, there are Markov-based, QSAM, completely non-parametric. Instead of trying to act

on regime changes in the environment. Try to detect changes in the behavior of a portfolio manager and act on that because that works, I think.

And it usually jives with experience. So that is something that can be exploited. I want to go back to an answer you gave early on, which is sort of like the old school factor investing and like the original versions. And maybe there was sort of a international factor or a liquidity factor, the small cap factor, the value factor. And it feels like a lot of these things

haven't worked in ages. And there's this debate that seems like, okay, is this the long cycle and eventually it's going to come back? Or is it that everybody not only knows about these factors and have discussed them to death, they're also extremely commodified in the sense that you could just buy an ETF of them, right? You could just buy a small cap ETF. It's trivial to execute. You could just buy a momentum ETF. It's trivial to execute, a value ETF, et cetera. My intuition would be

Since everyone knows about them and they're completely commodified technologically, they're just gone. But there is still debate. Some people think it's only a matter of time before these come back in vogue and that it's the long cycle, etc. I'm curious how you think about some of the original factors that people discuss and their prospects going forward. Well, so some factors...

were identified, but then somehow they got demoted. So famously size, right? So conditional on having other characteristics of a stock, size doesn't really explain much of your returns. And so it's a combination of other factors. Okay, well, that's one case. Then there are cases where

It seems that some factors have been exploited. You know, their capacity has been exhausted and so you can't make an attractive return of them. There are some factors that still have a low sharp, but they still have a positive sharp. And so, you know, every positive sharp deserves reward.

however small, an allocation. What's an example of that? Medium-term momentum, I would say, right? Medium-term momentum is tradable and it's relatively high capacity. Then you have the whole-term structure of momentum. So, you know, there is a shorter horizon reversal and whatnot. Short interest worked great until it didn't really work so consistently any longer. And then they also assume different characteristics, right? So,

you start having more crashes and the like. Is there a meme factor? No, I don't think so. But has that changed anything? It's a theme or something like that. Sorry? It's a theme. It's a theme, okay. Yeah. I don't know that ESG is a factor either. I don't think so. Okay. Oh, why do you say that? Because I don't think it's really that persistent. You mean it doesn't affect human behavior? I think that just...

I mean, there is also this feature, right? The moment they say that a factor exists, it comes into view. It's reflexive, right? Yeah, there is reflexivity in this, right? But I don't know that it really explains much of the returns in recent times. I'm going to ask one more question because I started with a dumb one and so I will finish with another dumb one. Is there good and bad alpha or is bad alpha just beta? No, every alpha signal is, you know,

God's little child. There is no bad alpha. All right, Gappy, thank you so much for coming back on Odd Lots.

We're going to leave it there. That was our conversation with Gappy Paliolago. I'm Jill Wiesenthal. You can follow me at The Stalwart. Follow Tracy at Tracy Allaway. Follow our guest, Gappy. He's at underscore underscore Paliolago. Follow our producers, Kermen Rodriguez at Kermen Armin, Dashiell Bennett at Dashbot, and Cale Brooks at Cale Brooks. For more Odd Lots content, go to Bloomberg.com slash Odd Lots. We have a daily newsletter and all of our episodes.

And if you enjoy the show, please leave us a positive review on your favorite podcast platform. Remember, Bloomberg subscribers get to listen to Odd Lots ad-free on Apple Podcasts. Just go to the Apple Podcasts app and follow the instructions there. Thanks for listening. ♪

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