Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of The Odd Laws podcast.
Speaker 2: I'm Jill Wisenthal, normally joined by my co host Tracy Alloway,
Speaker 2: but she's on vacation today, so it's just me in
Speaker 2: this intro. But in today's episode you will hear a
Speaker 2: conversation taped live at Bloomberg's Reimagining Information form. On June twelfth,
Speaker 2: we spoke with Gappi Pallioligo, global head of quantitative Research
Speaker 2: at Ballysny Asset Management. He has a new book out,
Speaker 2: it's called The Elements of Quantitative Investing. Neither of us
Speaker 2: have read it because it would go way over our
Speaker 2: heads because we're not quant so we don't know how
Speaker 2: to read that stuff. But Gappy is great at explaining
Speaker 2: all of this stuff in clear English. So we had
Speaker 2: a great conversation and we hope you enjoy listening to it.
Speaker 3: So just to begin, I'm going to start with a
Speaker 3: really really dumb question, possibly, but isn't all investing quant
Speaker 3: investing nowadays? I mean every investor has access to some
Speaker 3: form of quantitative or using numbers.
Speaker 4: Yeah, I guess yes. End of answer. Yeah, I think so,
Speaker 4: I think so I mean pretty much everybody uses some
Speaker 4: kind of quantitative overlay, right, but two different degrees. So
Speaker 4: I have a friend who worked for one of the
Speaker 4: Tiger cubs, and they they refused to use sharp They
Speaker 4: refused to use logs in a spreadsheet because they said
Speaker 4: that they were dangerous. Probably they took the log of
Speaker 4: a negative number. And so yeah, no, two different degrees.
Speaker 4: But yes, there is some quantitative culture seeping through.
Speaker 3: Okay, so what defines quantitative investing? How would you differentiate
Speaker 3: that from I don't know, value investing, discretionary investing.
Speaker 4: Okay, I think that there are several possible answers. So
Speaker 4: I'm going to go with the one answer that I
Speaker 4: read in my life as a quant I think it's
Speaker 4: a wily book. It's a very good book, by the way,
Speaker 4: And I think Cliff Asnes defined quantitative investing as basically
Speaker 4: investing in a large cross section of assets, having a
Speaker 4: relatively low edge low expected return in all of them.
Speaker 4: And so that's its definition, but it's not quite I
Speaker 4: think complete enough at this point, because you can also
Speaker 4: be a quantitative investor trading a relatively narrow cross section
Speaker 4: of assets but with high high frequency. Right, So What
Speaker 4: matters really is the number of bets in a sense
Speaker 4: that you are going to take right. So I think
Speaker 4: that probably is if you have a large number of
Speaker 4: independent bets or quasi independent bets, this means that you
Speaker 4: need to be able to scale your method to a
Speaker 4: large number of independent bets, and this means that you
Speaker 4: are in some way a quantitative investor.
Speaker 2: Speaking of roles and jobs, what do you Global head
Speaker 2: of quantitative research at Pelisney. What's your job? You've been
Speaker 2: there about six months. What does the job intel at
Speaker 2: a at a fund, at a firm like PALSNT.
Speaker 4: Okay, global head of quantitative research. Okay, So basically I
Speaker 4: am the head of quantitative research for equities, and maybe
Speaker 4: one day in the future I will do you know,
Speaker 4: some commodities or fixed income, but I'm perfectly happy to
Speaker 4: serve equities, you know, both discretionary and systematic. What we
Speaker 4: do is I mean my group mostly, I mean I
Speaker 4: am in meetings, so I don't do any work. So
Speaker 4: we in a sense provide centralized quantitative services for the firm.
Speaker 4: So the first backbone thing that we do is you
Speaker 4: develop factor models wherever you can, right, so for equities
Speaker 4: at different horizons. Ideally you would like to develop them
Speaker 4: for other asset classes. But you know, factor models are
Speaker 4: the backbone of a lot of quantity investing nowadays. And
Speaker 4: then hedging at the firm level and at the individual
Speaker 4: PM levels, which is apparently very simple, but actually it's
Speaker 4: very deep as a problem. And then we do portfolio
Speaker 4: advisory services, which is basically you go two pms. You
Speaker 4: help them construct better portfolios, You help them understand their performance,
Speaker 4: which is extremely important, manage their risk, manage their drawdown
Speaker 4: on occasion, be their therapists. But this is what we do.
Speaker 3: I know you're in meetings all day, but you know,
Speaker 3: if you were someone on your team, how would you
Speaker 3: be coming up with actual ideas for factors. I hear
Speaker 3: people who sometimes come up with ideas for all thoughts episodes.
Speaker 3: Some of them have even turned out reasonably okay, But
Speaker 3: how does idea generation work? You sit down, You're like,
Speaker 3: I need to come up with a new factor today.
Speaker 3: What are you doing? What are you looking at?
Speaker 4: Okay? I want to specify a little bit more what's
Speaker 4: a factor because otherwise gets a little bit too vague.
Speaker 4: So like, there are factors and factors, So there are
Speaker 4: some factors that are real factors and what are those?
Speaker 4: Those are essentially attributes of some kind that you can
Speaker 4: assign to your investable universe. And there are sources of
Speaker 4: returns that affect the individual securities through this characteristic, and
Speaker 4: they are pervasive. So every asset is in some form
Speaker 4: affected by the systematic source of return number one, So
Speaker 4: they've got to be pervasive. The second thing is they
Speaker 4: got to be persistent, right, So it's not the case
Speaker 4: that I have a lot of factor returns for two
Speaker 4: months and then nothing for ten ten months, right, So
Speaker 4: that's not really a factor. And then possibly the third
Speaker 4: characteristic is that they have to be interesting, so they
Speaker 4: have to be in some way vaguely interpretable. So when
Speaker 4: you you know you match these requirements, it's a factor. Now,
Speaker 4: now imagine that you have the Trump factor. Let's see
Speaker 4: if Trump wins, a few stocks will definitely benefit, a
Speaker 4: few stocks will definitely not benefit from the election of
Speaker 4: Trump versus Kamala Harris. Another source could be well tariffs, right,
Speaker 4: Another source could be AI. Okay, AI definitely right. Doesn't
Speaker 4: fit the characteristic of being pervasive because there is a
Speaker 4: relatively small universe that's affected by the AI theme is
Speaker 4: likely not to go not going to be persistent. So
Speaker 4: it wasn't here like a few years ago, and it
Speaker 4: will probably not be here in five years because everything
Speaker 4: will be to some extent. AI it's interesting, but that's
Speaker 4: a theme, it's not a factor. That's what I would
Speaker 4: call a theme. And there are also some mathematical characteristic
Speaker 4: of a factor versus a theme, which so basically you
Speaker 4: can create a portfolio that tracks a factor, and this
Speaker 4: portfolio will have a relatively small idiosyncratic risk, so it
Speaker 4: will be truly a reproduction of the systematic source of
Speaker 4: return that you were observing through the assets. So imagine
Speaker 4: that this systematic source exists, but you do not observe
Speaker 4: it directly. It's latent, it's out there, but you can
Speaker 4: actually reconstruct it with a portfolio. A theme is let's
Speaker 4: say ten assets, you cannot really reconstruct it the same
Speaker 4: way because ten assets are just too few to diversify
Speaker 4: away the idiosyncratic source of returns of the individual assets.
Speaker 2: So when you're like thinking about factor identification, how much
Speaker 2: of the money that you make the actual returns come
Speaker 2: from essentially factor identification or being able to identify a
Speaker 2: factor before other measure identify a factor that exists before
Speaker 2: other competitors out there in the market.
Speaker 4: Okay, that's a great question because I I think I
Speaker 4: know the answer. Okay, great, But the reality is this,
Speaker 4: I think you know somebody else's factor is my alpha,
Speaker 4: and vice versa. Right, So say more, there are well
Speaker 4: known factors, let's say, some variety of value and momentum
Speaker 4: or reversion, and you can bet on those and you
Speaker 4: diversify away everything else, and what you get is, basically
Speaker 4: you get some returns that are priced priced in the
Speaker 4: sense that, as you know, you pay basically some risk
Speaker 4: for that. Right, So this is priced return and that's great.
Speaker 4: But once upon a time like these were non not
Speaker 4: public knowledge. If you were lucky enough to be a
Speaker 4: hedge fund in the eighties, and I've met a few,
Speaker 4: you know, and you were maybe also investing in Europe,
Speaker 4: these factors were really working very well, and they were alpha.
Speaker 4: They were not called factors. You know. The first I
Speaker 4: think published paper is probably eighty nine for momentum. Right now,
Speaker 4: there is alpha, and alpha is basically ideally would be
Speaker 4: a return that has no asocidate risk to it. It
Speaker 4: hardly ever exists. So what you really have are factors
Speaker 4: that exist at some frequency or in some universe, or
Speaker 4: with some characteristic that nobody else has found yet, and
Speaker 4: so they can be exploited.
Speaker 3: More, how do you make sure that when you're isolating
Speaker 3: a particular factor, you're not accidentally taking into accounts some
Speaker 3: other dynamics. So, you know, maybe you want to invest
Speaker 3: in a bunch of companies with like pricing power during
Speaker 3: the tariffs, but actually your cohort of companies ends up
Speaker 3: just looking like a bunch of big tech companies or
Speaker 3: something like that.
Speaker 4: The short answer without explanation, is that you can. But
Speaker 4: the long answer is a little bit more involved. If
Speaker 4: you have true characteristics, like I don't know, a tariff
Speaker 4: and a tech classification that are one hundred percent correlated,
Speaker 4: well then you really have only one. You don't need both, right,
Speaker 4: So okay, But if I have in my let's say,
Speaker 4: arsenal of factors, if I have multiple factors they're somewhat
Speaker 4: overlapping but not completely overlapping, then you can build a
Speaker 4: portfolio that separates the impact of one from the other.
Speaker 3: So you try to isolate you can you.
Speaker 4: Can isolate them, you can kind of purify them. Now
Speaker 4: there is also the scenario where there are factors that
Speaker 4: are not in the model and they should be and
Speaker 4: and basically those they complicate the picture a little bit.
Speaker 4: But otherwise, if you have a reasonable model, you are
Speaker 4: you're going to be able to separate them to understand
Speaker 4: what's the relationship. You can create a portfolio that exploits
Speaker 4: the first one, and then create a second portfolio that
Speaker 4: is uncorredly to the first one that exploits the second one.
Speaker 2: Just zooming out for a second again. And this sort
Speaker 2: of relates to Tracy's first question, but also, I guess
Speaker 2: relates to my first question. If you have a fund
Speaker 2: and it has various pms and analysts in there, is
Speaker 2: there a difference between quant at your level, which is
Speaker 2: at the fund level, versus say a POD or a
Speaker 2: PM whose specialty is quant trading. And there are different
Speaker 2: definitions or different senses in which that term can apply.
Speaker 4: Yeah, the fact is that you know, quant is is
Speaker 4: a very very generic label nowadays. Yeah, so there are many,
Speaker 4: many quants and they do all sorts of very interesting jobs.
Speaker 4: Some of them are are just differentiated because they live
Speaker 4: in different constructs. So nowadays, in a platform, especially in
Speaker 4: a quantitative one, it's not impossible to see pods and
Speaker 4: center groups. Okay, so that's one distinction. So what's the
Speaker 4: what's the difference. In a pod, you typically have a
Speaker 4: siloed group. I'm probably not stating the obvious, but you
Speaker 4: know you have a silot group. They don't communicate with
Speaker 4: other pods. You want at the firm level to have
Speaker 4: independent sources of alphas, and their payout typically is a
Speaker 4: percentage of their P and L after costs. Okay, and
Speaker 4: then you're a quant in a pod. In a center group,
Speaker 4: typically you are part of a larger group and the
Speaker 4: group will hopefully have large capacity. So these have you know,
Speaker 4: a larger program, like a larger research program. Their compensation
Speaker 4: tends to be more discretionary. And that's a center group.
Speaker 4: Then you have all sorts of other quants. So you
Speaker 4: have people like me who serve the firm at the
Speaker 4: center level. I also serve the leadership of the firm.
Speaker 4: And then you have people doing who are doing, for example,
Speaker 4: execution research. She's extremely extremely complex and interesting, right, So
Speaker 4: it's not black and white like you can do execution
Speaker 4: research and be responsible for some P and L. It's
Speaker 4: very very very rich nowadays and very specialized.
Speaker 3: I was actually going to ask about execution because when
Speaker 3: we're talking about quant investing, I think a lot of
Speaker 3: questions are around factors and idea generation. But you have
Speaker 3: all the I would assume boring stuff like liquidity trading
Speaker 3: costs that you also have to think about how do
Speaker 3: you actually incorporate those into your strategies.
Speaker 4: So you can do it in a variety of ways.
Speaker 4: It depends first of all, what position the firm occupies
Speaker 4: in the ecosystem. So if you are a high frequency
Speaker 4: trading company, most likely you are using your own capital
Speaker 4: because you are capacity constraint, so you know you don't
Speaker 4: need a lot of capital. So those firms exploit market
Speaker 4: microstructure level information. Okay, so in a sense, a high
Speaker 4: frequency trading firm does not have a market impact model
Speaker 4: in the traditional sense. They don't see parent orders, right,
Speaker 4: they execute at the microscopic level. If you are a
Speaker 4: hedge fund, typically you trade a lot, you have your
Speaker 4: own data set of orders. These data sets differ a lot,
Speaker 4: so you could have a market impact model for a
Speaker 4: quantitative trading group or a strategy, and you could have
Speaker 4: a different market impact model for hedging and a different
Speaker 4: market impact model for fundamental investing. And then what you
Speaker 4: get is basically a term function that you place in
Speaker 4: your optimization problem that hopefully help to size the portfolio
Speaker 4: or trade the portfolio optimally. And this is extremely important.
Speaker 4: Uh you know, market impact is it is a very
Speaker 4: very sizeable uh fraction of the lost P and L
Speaker 4: of of a firm.
Speaker 2: What as of today, what value is there in your
Speaker 2: world of specifically generative AI, l O, MS, et cetera.
Speaker 2: What how do you how do you currently or not
Speaker 2: currently get actual value out of them?
Speaker 4: Okay, so on this I have really relatively little to say.
Speaker 4: That's that's original.
Speaker 3: But tell us everything your employer is doing with AI.
Speaker 4: Yes, that'll send you the resum, thank you, But I think, okay,
Speaker 4: just let's recap the basics. Right, So the basics are,
Speaker 4: at least for the time being, everybody is trying to
Speaker 4: be more productive with AI. Right, So you want to
Speaker 4: have all your documents you want to have now you
Speaker 4: know what? Perplexity has a finance module. I think one
Speaker 4: day soon maybe Bloomberg will not have the keywords any longer.
Speaker 4: You just give you know, Bloomberg a task and it
Speaker 4: will grab all the pieces of information and hand it
Speaker 4: over to you and maybe you can schedule it. All
Speaker 4: of this is relatively table stakes. I mean, the the
Speaker 4: agentic aspect is not yet, but it will become pretty soon.
Speaker 4: I think it's going to be very hard to compute
Speaker 4: with the likes of maybe Bloomberg, but for sure, let's
Speaker 4: say you know the big hyperscalers. So that's one. At
Speaker 4: the investment level, it's it's much more complicated. So in
Speaker 4: strategies where there is a natural richness in data, you
Speaker 4: can definitely use if not deep learning, but you or AI,
Speaker 4: but you can definitely use very advanced machine learning algorithms
Speaker 4: and you do not have a data snoopin problem, you
Speaker 4: do not have a back testing problem, and so you
Speaker 4: are in a data rich environment and you can do that.
Speaker 4: And it's not a secret that, for example, XTX has
Speaker 4: a very large on premu you know number of in
Speaker 4: Nvidia cards I don't remember h one hundreds or something
Speaker 4: like that. So that's one thing, right. The question is
Speaker 4: really what's going to happen to the slower investment styles.
Speaker 4: And my view is that hopefully large firms like mine
Speaker 4: will have an advantage. But will see right why because
Speaker 4: we do have we do have the scale. We have
Speaker 4: a large number of pms, We have a lot of
Speaker 4: historical data, we have a lot of propriety every data
Speaker 4: that nobody else has. So maybe that that will work out.
Speaker 4: But how to make it happen, I don't know because
Speaker 4: things are changing so fast. And also I'm, you know,
Speaker 4: relatively a tourist in the areas I'm trying to learn
Speaker 4: a little bit more about.
Speaker 3: You mentioned proprietary data, and this comes up a lot
Speaker 3: where people talk about, well, the competitive advantage nowadays really
Speaker 3: is that data set? I mean, is is that true?
Speaker 3: If I get something really cool and unique, I can
Speaker 3: automatically become I don't know, a billionaire trader, if I
Speaker 3: can figure out how to execute on it. Is that
Speaker 3: all there is?
Speaker 4: Maybe yes, I have very weak beliefs on this. I
Speaker 4: don't know. Maybe yes, we'll find out.
Speaker 3: Well, so where are people getting interesting data sets from?
Speaker 4: I mean, you get interesting data from observing human beings
Speaker 4: actually investing, and you don't get to see a great
Speaker 4: PM investing, but I do. That's that's the benefit.
Speaker 2: So from your central position, you just get to see
Speaker 2: a lot of activity and you get to see novel
Speaker 2: data that other people don't get to see simply by
Speaker 2: being in the center of all of these different trades
Speaker 2: and everything, and that gives you a sort of higher
Speaker 2: abstraction layer or whatever it is that someone else in
Speaker 2: the market doesn't have.
Speaker 4: Yeah, and it's possible that not in the distant future,
Speaker 4: good pms will become good because they can improve on
Speaker 4: themselves by basically playing or training or having a baseline
Speaker 4: of an agent that reproduces their behavior. So you know
Speaker 4: there is an alter gapy, well ano a PM, but
Speaker 4: an alter whatever who says what would you do right?
Speaker 4: And you get a baseline behavior and then you can
Speaker 4: think about it and you could say, well, I would
Speaker 4: do something different, and then that becomes an example in
Speaker 4: a reinforcement learning process where the AI keeps learning from
Speaker 4: you and you keep improving because the baseline is changing.
Speaker 3: So before we came out here, I asked perplexity to
Speaker 3: come up with a new factor, and it came up
Speaker 3: with something called the policy agility factor, which is supposed
Speaker 3: to be that countries that display policy flexibility have better
Speaker 3: outperformance over the longer term. Countries that are able to
Speaker 3: more quickly adapt to changing situations are outperformers over the
Speaker 3: long run. Can you grade that factor. I didn't do
Speaker 3: a back tust. But like if someone brought you an
Speaker 3: idea like that, not me, perplexity, I don't want you
Speaker 3: to insult me over the next five minutes. What would
Speaker 3: you say to them? What are the problems with this?
Speaker 4: I mean no major problems, there are questions. So the
Speaker 4: first thing that you want to make sure is that
Speaker 4: if AI whatever it means, brings to you definition, right,
Speaker 4: that definition should be at a point in time and
Speaker 4: should not be trained on all the on all the
Speaker 4: past data, right. So number one, you want to do
Speaker 4: that because if you back test that feature and in
Speaker 4: a way perplexity has already tested it, it's not a
Speaker 4: fair play. You know, the performance will the back test
Speaker 4: will look great. So, unfortunately, we live in a world
Speaker 4: where some factors will never be back testable. So you
Speaker 4: don't know whether they work or they don't work, right,
Speaker 4: You just know that you cannot test them in advance,
Speaker 4: like a policy agility. This seems to be a very
Speaker 4: low turnover factor, right, and it seems to be probably
Speaker 4: a very low.
Speaker 3: Sharp factor in a low universe and.
Speaker 4: A small universe. So how do you how do you know? Well,
Speaker 4: probably you want to make sure that it makes sense,
Speaker 4: and maybe you can start putting a small volativity allocation
Speaker 4: to it and.
Speaker 3: Then you would build it up as you.
Speaker 4: Watched it out for yes, out of sample.
Speaker 3: Okay, so speaking of back to us, I have one
Speaker 3: more question. But it seems like quant investing. Part of
Speaker 3: the issue with this is you are looking back at
Speaker 3: historical data. That's all you have. You don't have data
Speaker 3: about the future. Unfortunately, it strikes me as hard to
Speaker 3: deal with regime changes. So when you have a big
Speaker 3: break in how something works in finance or markets or
Speaker 3: the global economy, how does quant investing actually take into
Speaker 3: account those sorts of risks, Like say, you know, a
Speaker 3: lot of investing is based on the idea that bonds
Speaker 3: and stocks are going to move inversely to each other,
Speaker 3: and then in twenty twenty two they started moving together.
Speaker 4: I think that most people with a quantitative background in
Speaker 4: finance will tell you that regime change is very difficult
Speaker 4: to detect and to act on in an effective manner.
Speaker 4: So I think that's been my experience at least, right
Speaker 4: so in every possible application I've tried, and you know,
Speaker 4: it never, it really never works for me. Maybe it
Speaker 4: works for somebody else. What I think it's a bit
Speaker 4: easier to do is to detect regime change in a
Speaker 4: human being. So instead of trying to use you know,
Speaker 4: there are many many algorithms for regime change. You know,
Speaker 4: there are MARKT based q sum, completely non parametric. Instead
Speaker 4: of trying to act on regime changes in the environment,
Speaker 4: try to detect changes in the behavior of a portfolio
Speaker 4: manager and act on that because that works, I think,
Speaker 4: and usually you know, jives with experience with so that
Speaker 4: that is something that can be exploited.
Speaker 2: I want to go back to an answer you gave
Speaker 2: early on, which is sort of like the old school
Speaker 2: factor investing and like the original versions and maybe they
Speaker 2: sort of an international factor or a liquidity factor, or
Speaker 2: the small cap factor, the value factor. And it feels
Speaker 2: like a lot of these things haven't worked in ages,
Speaker 2: and there's this debate that seems like, Okay, is this
Speaker 2: the long cycle and eventually it's gonna come back, or
Speaker 2: is it that everybody not only knows about these factors
Speaker 2: that have discussed them to death, they're also extremely commodified
Speaker 2: in the sense that you could just buy an ETF
Speaker 2: of them, right, You could just buy a small cap ETF.
Speaker 2: It's trivial to execute. You could just buy a momentum ETF.
Speaker 2: It's trivial to execute a value ETF, et cetera. Like
Speaker 2: my intuition would be, since everyone knows about them and
Speaker 2: they're completely commodified technologically, they're just gone. But there is
Speaker 2: still debates. Some people think it's totally a matter of
Speaker 2: time before these come back in vogue, and that it's
Speaker 2: the long cycle, et cetera. I'm curious how you think
Speaker 2: about some of the original factors that people discussed in
Speaker 2: their prospects going forward.
Speaker 4: Well, so some factors were identified, but then somehow they
Speaker 4: got demoted so famously. Size, right, so conditional on having
Speaker 4: other characteristics of a stock. Size doesn't really explain much
Speaker 4: of your returns, and so it's a combination of other factors. Okay,
Speaker 4: well that's one case. Then there are cases where it
Speaker 4: seems that some factors have been exploited. You know, their
Speaker 4: capacity has been exhausted, and so you can't make an
Speaker 4: attractive return of them. There are some factors that still
Speaker 4: have a low sharp, but they still have a positive sharp,
Speaker 4: and so you know every positive sharp deserves, however small
Speaker 4: and allocation.
Speaker 2: What's an example of.
Speaker 4: That medium term momentum. Right, medium ton momentum is treadable
Speaker 4: and it's relatively high capacity. Then you have the whole
Speaker 4: term structure of momentum, so you know there is a
Speaker 4: shorter horizon reversal and whatnot. Short interest worked great until
Speaker 4: it didn't really work so consistently any longer. And then
Speaker 4: they also assume different characteristics, right, so you start having
Speaker 4: more crashes and the like.
Speaker 2: Is there a mean factor?
Speaker 4: I don't think so. But is there change any theme
Speaker 4: or something like that it's a theme or a theme? Yeah,
Speaker 4: I don't know that ESG is a factor either. I
Speaker 4: don't think so.
Speaker 3: Oh why do you say that?
Speaker 4: Because I don't think it's really that persistent.
Speaker 3: I mean, it doesn't affect human behavior.
Speaker 4: I think that just there is also this feature, right
Speaker 4: the moment that you say that a factor exists, it's reflexive, right,
Speaker 4: there is reflexibity in this, right, But I don't know
Speaker 4: that it really explains much of the returns in recent times.
Speaker 3: So I'm going to ask one more question because I
Speaker 3: started with a dumb one, and so I will finish
Speaker 3: with another dumb one. Is there good and bad alpha
Speaker 3: or is bad alpha just beta? No?
Speaker 4: Every alpha signal is you know, God's little child. There
Speaker 4: is no bad alpha.
Speaker 3: All right, Gappy, thank you so much for coming back
Speaker 3: on odd Lots.
Speaker 2: We're gonna leave it there. That was our conversation with
Speaker 2: Gappi Pallioligo. I'm Jill Wisenthal. You can follow me at
Speaker 2: the Stalwart, Follow Tracy at Tracy Alloway. Follow our guest Gappy,
Speaker 2: He's at Underscore Underscore Polioligo. Follow our producers Kerman Rodriguez
Speaker 2: at Kerman armand dash O Bennett at Dashbuck and kel
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