Giuseppe Paleologo on Quant Investing at Multi-Strat Hedge Funds

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2:00Hello, and welcome to another episode of the Odd Lots podcast. I'm Jill Weisenthal, normally joined by my co-host Tracy Elloway, 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 Gappy Palioligo, Global Head of Quantitative Research at Ballyasny 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 quants. We don't know how to read that stuff.

2:36But 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 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 sharp.

3:15They 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, two 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 Asnes defined quantitative investing as basically investing in a large cross-section of assets having a relatively low edge low expect a return in all of them.

4:11And 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? So what matters really is the number of bets, in a sense, that you are going to take, right? So I think that probably is, 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 Bellyazny, what's your job?

4:59You've been there about six months. What does the job until at a fund, at a firm like Belias now? 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 or fixed income. But I'm perfectly happy to serve equities, 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?

5:49So 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.

6:28But 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.

7:07And 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've got to be persistent. So it's not the case that I have a lot of factor returns for two months and then nothing for 10 months. So that's not really a factor. And then possibly the third characteristic is that they have to be interesting.

8:00So they have to be in some way vaguely interpretable. So when you match these requirements, it's a factor. 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 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.

8:56It'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 a 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.

9:42You 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 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. Say more. There are well-known factors.

10:28Let'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, these factors were really working very well. And they were alpha. They were not called factors.

11:14You 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 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.

12:06I mean, the short answer without explanation is that you can. but the long answer is a little bit more involved if you have a true characteristics like i don't know um 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 uh let's say arsenal of 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.

13:03But 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, for example.

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15:36Crypto trading provided by Backed Crypto Solutions, LLC. Complete disclosures available at public.com. 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. The fact is that quant is a very generic label nowadays. Yeah. So there are many, many quants and they do all sorts of very interesting jobs.

16:22Some 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. And then you're a quant in a pod. In a center group, typically you are part of a larger group.

17:11and the group will hopefully have large capacity. So these are 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. Like you can do execution research and be responsible for some P &L. It's very, very, very rich nowadays and very specialized.

18:04I 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.

18:46So those firms exploit market microstructure level information. 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. 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. 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, a function that you place in your optimization problem that hopefully helps you size the portfolio or trade the portfolio optimally.

19:47And 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, etc.? How do you currently or not currently get actual value out of them?

20:22Okay, 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. So you want to have all your documents. You want to have now, you know, what perplexity has a finance module. I think one day soon, maybe Bloomberg will not have the keywords any longer. where you just give Bloomberg a task, and it will grab all the pieces of information and hand it over to you, and maybe you can schedule it.

21:11All 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, 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.

22:05And it's not a secret that, for example, XTX has a very large on-prem 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 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.

22:52and 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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24:34Paid for by Public Investing. All investing involves the risk of loss, including loss of principal. Brokerage services for U.S.-listed registered securities, options and bonds, and a self-directed account are offered by Public Investing, Inc., member FINRA and SIPC. Crypto trading provided by Backed Crypto Solutions, LLC. Complete disclosures available at public.com slash disclosure. Introducing the all-new Adobe Acrobat Studio, now with AI-powered PDF spaces. Do more with PDFs than you ever thought possible. Need AI to turn 100 pages of market research into five insights with a click? Do that with Acrobat.

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25:36Maybe 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 on the market doesn't have.

26:16Yeah, 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 know, the AI keeps learning from you and you keep improving because the baseline is changing.

26:59So 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 back test, but 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?

27:38I 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. You know, 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. You just know that you cannot test them in advance.

28:32Like policy agility. This seems to be a very low turnover factor. 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 back tests, 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.