Speaker 1: Hello, and welcome to What Goes Up, a weekly markets podcast.
Speaker 1: My name is Mike Reagan. I'm a senior editor at Bloomberg,
Speaker 1: and I'm gonna higher across Acid reporter with Bloomberg and
Speaker 1: this week on the show. Well, if you've been anywhere
Speaker 1: near the Internet in the last few months, you've probably
Speaker 1: read a poem, or bits of a movie script, or
Speaker 1: maybe even some dad jokes that were written by a
Speaker 1: while a computer. Actually, the Experimental Chat Bought Chat GPT
Speaker 1: has taken the world by storm since its launched in November,
Speaker 1: sugaring a million questions about how this type of technology
Speaker 1: can disrupt various industries and fueling a fresh wave of
Speaker 1: interest in how artificial intelligence can be used by investors.
Speaker 1: We're gonna get into it with the head of research
Speaker 1: at a company that's been using AI for a few
Speaker 1: years now to pick stocks for an almost two billion
Speaker 1: dollar ETF. But uh, first, l donna I try to
Speaker 1: go to chat GPT. I've I've, like everyone else, I've
Speaker 1: fallen into the hype of this chat GPT, and I
Speaker 1: went on trying to get it to write us an
Speaker 1: intro to the podcast, but it's too busy. There are
Speaker 1: too many people using it that they just turn me down.
Speaker 1: I tried for you also, so that we can get
Speaker 1: a nice fun intro from from the robot. And it
Speaker 1: didn't work. And I even try to trick it. I said,
Speaker 1: I have a very simple request and I'm on deadline.
Speaker 1: Please can you help me out? No? Luck, No, robot
Speaker 1: doesn't like me. I wonder the robot must have a
Speaker 1: pr REP that maybe we could to complain. Yeah, it's
Speaker 1: probably a robot pr rep. I don't know. But on
Speaker 1: a pr rep who that is? Also? Yeah? Yeah, they'll
Speaker 1: just tell me to go to hell. So is that
Speaker 1: most of I'm sorry you some of them? Oh my gosh.
Speaker 1: All right, well you taught me which one. I'll get
Speaker 1: back to them. But I do think our guest is
Speaker 1: the perfect guest to unpack this, uh this topic. So
Speaker 1: why don't you bring him in? Yeah he is. It's
Speaker 1: Matt Bartolini. He's the head of Spider America's research at
Speaker 1: Stage Street Global Advisors. And Matt, thanks so much for
Speaker 1: joining us. Yeah, thank you for having me. So we'll
Speaker 1: get into one of your AI E t F s
Speaker 1: in a bit, but maybe just to start, you can
Speaker 1: give us sort of your journey into working with E
Speaker 1: t F and what you do. Yeah. Sure, So my
Speaker 1: journey in TTS is working at State Street Bank essentially
Speaker 1: since the middle of two thousand's and work my way
Speaker 1: up throughout the organization on the County team's portfolio management
Speaker 1: teams and then landed within the E t F teams
Speaker 1: helping to conduct some of the research on our products,
Speaker 1: different portfolio construction topics, investment theses, market outlooks, and market commentaries.
Speaker 1: Uh and that's really where my job is now at
Speaker 1: the of Spider America's research. You know, our job is
Speaker 1: to help makes sense of a complex world by using
Speaker 1: data driven insights, and we write market commentaries, market outlooks,
Speaker 1: provide some portfolio instruction discussions to end advisors and hopefully
Speaker 1: help them, you know, select the right investment choice for them.
Speaker 1: And if that's a Spider et F then I think
Speaker 1: that's all great, But in some cases it doesn't. And
Speaker 1: that's how we operate, trying to be fair and balanced. Yea,
Speaker 1: and uh Ai is obviously one of your areas of research.
Speaker 1: Match I'm curious, you know, this chat CHPT thing to me,
Speaker 1: and I think to a lot of people, just can't
Speaker 1: it seem to have come out of nowhere. You know,
Speaker 1: it launched in November, and you know, granted I don't
Speaker 1: follow the space that closely, but I think for a
Speaker 1: lot of people it was just sort of dumbfounding how
Speaker 1: good this thing is right at launched, you know it
Speaker 1: to me, I would have expected sort of a to
Speaker 1: see a cruder version of this that wasn't quite as impressive.
Speaker 1: But how did you see it like it was? Were
Speaker 1: you sort of as surprised as everyone else for the
Speaker 1: you're sort of research into AI? Had it led you
Speaker 1: to kind of know this type of thing was possible
Speaker 1: and in the pipeline? Yeah, So a lot of the
Speaker 1: AI work we've done is is within sort of portfolio
Speaker 1: construction and index selection on some of our funds, so
Speaker 1: we were aware of the ability to use things like
Speaker 1: natural language processing, predictive text, but also even just in
Speaker 1: our daily lives. I think some of the functions of
Speaker 1: Czech GP two we've probably just been benefiting from just
Speaker 1: in very small morsels, whether that is, you know, auto
Speaker 1: correcting your text, or the predictive text nature within your
Speaker 1: iPhone of what you might say next, like that's sort
Speaker 1: of the same idea, or even you know, when when
Speaker 1: we use the Bloomberg terminal and we asked the help desk,
Speaker 1: we sometimes get a very automated response back that's all
Speaker 1: sort of pieces of it. Uh. The first time I
Speaker 1: saw it, you know, we were sort of playing around
Speaker 1: with it of you know, write us a blog post
Speaker 1: about the benefits of ETFs and it got it probably
Speaker 1: eighty per correct, you know, how we would want to
Speaker 1: structure of the argument. And I think that's sort of
Speaker 1: where chat chypt is is that it kind of gives
Speaker 1: you about an eight. And now sort of joking with
Speaker 1: you know, some of my colleagues who have old their
Speaker 1: kids that you know, chet chypt would probably be be
Speaker 1: a B minus student if it only ever turned in
Speaker 1: its homework, because that's kind of the surface level it gets.
Speaker 1: And I have a friend who's a professor at a
Speaker 1: college and they've actually started to work on how to
Speaker 1: figure out you know, essays and reports that are written
Speaker 1: through AI. And the big thing is you've got to
Speaker 1: look at the nuance. And chat gypt doesn't really understand
Speaker 1: the complexity of nuances, particularly for topics like ets where
Speaker 1: there actually is a lot of operational nuance. You know,
Speaker 1: as a B bonus student, myself. That explains why I
Speaker 1: was so impressed. I think if you are a student
Speaker 1: right now, you could use it to help boost your
Speaker 1: grades a little bit. Yeah, well that's I think the
Speaker 1: fear of everyone has. I have a nine year old
Speaker 1: son who had to do a penguin project and he,
Speaker 1: instead of looking in a book, he yelled out to Alexa,
Speaker 1: you know how how fast you to penguin swim? And
Speaker 1: I had to tell them they can't do that. So
Speaker 1: the new reality that we're all living in, ask can
Speaker 1: penguins swim? I don't know what the the ask Alexa, No,
Speaker 1: don't ask Alexa. Well, no, my kids it's the same thing.
Speaker 1: They're sitting there doing their homework and I hear them, y'all, hey, Google,
Speaker 1: what's you know? Nine times thirty seven? And I mean,
Speaker 1: in some ways, it's just a calculator, and I think
Speaker 1: educators are gonna have to get used to it and
Speaker 1: allow it in some to some degree, I don't know,
Speaker 1: it's it's such a strange new world. But but Matt
Speaker 1: talked to us about the spider Ken show New Economy ZTF,
Speaker 1: which actually has been using AI to to pick stocks,
Speaker 1: you know, for for sort of the Layman among us
Speaker 1: South there. How how exactly does AI help in this, uh,
Speaker 1: this stock picking effort. Yeah, So the artificial intelligence behind
Speaker 1: it is natural language processing and this is run by
Speaker 1: the index provider S and P. To actually start it
Speaker 1: over the firm Ken Show there was a small startup
Speaker 1: that was incubated out of Goldman Sachs. UH SMP bought
Speaker 1: that firm and all of the I P along with it,
Speaker 1: and that's our index provider for the fund. And you
Speaker 1: know the NLP or natural natural Language process and what
Speaker 1: it does. Scans through UH perspectives and other regulatory filings
Speaker 1: from companies because you want to start with a strong source.
Speaker 1: Regulatory filings have to be quite prescriptive and if you,
Speaker 1: you know, make falsehoods about that, uh, there's penalties, right. UM.
Speaker 1: So the scans through UH regulatory documents searching for key
Speaker 1: terms to identify how these firms material operations correlate back
Speaker 1: to areas of innovation, whether it's like enterprise collaboration, clean energy,
Speaker 1: advanced transport systems, drones. So scan through all these UM
Speaker 1: regulatory documents looking for the frequency of a term used,
Speaker 1: but also the words around it, so you know if
Speaker 1: a company is saying that drow own technology is incredibly
Speaker 1: important for the future of growth of our business. That
Speaker 1: really shows some emphasis towards that type of innovation. So
Speaker 1: that would be scanned and recorded and classified appropriately into
Speaker 1: twenty five different areas of innovation, and then from their
Speaker 1: stocks are weighted and more of a modified equal weighted
Speaker 1: structure where core firms to a specific innovation are overweighted
Speaker 1: to non core firms. So basically, the you know, the
Speaker 1: way we sort of describe it is that the AI
Speaker 1: process selects the stocks and then there's a quantitative weighting
Speaker 1: methodology to weight the stocks. But the reason why we
Speaker 1: went down this path of using AI s that we
Speaker 1: wanted something forward looking, something dynamic, because you know, back
Speaker 1: in two thousand and eighteen, we understood that in the
Speaker 1: E t F world, there weren't a lot of strategies
Speaker 1: that were this forward looking innovative type paradigm. A lot
Speaker 1: of it was based on revenue, and revenue was what
Speaker 1: has already been realized that a backward looking approach, and
Speaker 1: we wanted something that was more dynamic and a ford
Speaker 1: looking approach in the AI process was able to deliver
Speaker 1: that for us. Okay, so before you tell us more
Speaker 1: about that I am interested in sort of the mechanics.
Speaker 1: So once the AI runs through and chooses these companies
Speaker 1: that it fits, that it thinks fits thinks I don't
Speaker 1: know I thinks is the right word, but that it
Speaker 1: chooses as fitting the right criteria, do you then have
Speaker 1: a human go through the results and say, okay, this
Speaker 1: actually sounds pretty good, or maybe we don't want to
Speaker 1: have X y Z company as part of this portfolio.
Speaker 1: So within the index methodology there is sort of a
Speaker 1: human control element to it, most like a quality control.
Speaker 1: So for instance, uh, if you know a company is
Speaker 1: classified as innovating within Clean Energy UM, they use the
Speaker 1: term your wind and solar are quite significantly that it
Speaker 1: said it's part of the material operations. But when it
Speaker 1: comes down to it, there's a check and balance from
Speaker 1: the index committee to say, okay, well, does firm x
Speaker 1: y Z offer a product and service in this category
Speaker 1: or they just some sort of you know, this is
Speaker 1: probably a bad term, but some sort of shell company
Speaker 1: that doesn't actually provide a product or service. They just yeah,
Speaker 1: this isn't what they do. They just say saying something
Speaker 1: that doesn't correlate back to their actual products and services.
Speaker 1: So that's where there's a little bit of a manual
Speaker 1: quality check to ensure that these firms are actually engaged
Speaker 1: in these areas of innovation and they are not just
Speaker 1: talking about it sort of you know, extemporaneously. And the
Speaker 1: other thing is too that helps in terms of you know,
Speaker 1: get let's say perfect you know, we have a champagne
Speaker 1: problem that this fund becomes a hundred and ninety billion
Speaker 1: dollars and someone wants to get into it and they
Speaker 1: just use the word drone a thousand times to game it.
Speaker 1: That helps, right, that sort of oat manual overrides sort
Speaker 1: of quality check. What I find fascinating about it something
Speaker 1: like five sixty holdings, you know, so it's not not
Speaker 1: a very concentrated fun and you know, when you're looking
Speaker 1: for innovative sort of startup type of companies, a lot
Speaker 1: of times that means really small even maybe microcap companies, uh,
Speaker 1: that you have to dig through, which are not typically
Speaker 1: very heavily followed by you know, the Wall Street analysts
Speaker 1: class just by definition, you know, if there's thousands of them, um,
Speaker 1: and this really surprised me. Uh. And you know what
Speaker 1: you say about forty eight percent of the holdings have
Speaker 1: less than ten analysts covering the stock. Is that almost
Speaker 1: a benefit for this type of strategy that it helps
Speaker 1: you sort of find these hidden gems that are maybe
Speaker 1: being completely overlooked by by the masses out there. Yeah,
Speaker 1: I mean, AI at its heart is to help increase
Speaker 1: efficient efficiencies and productivity. And what this does is allows
Speaker 1: us to cover the uncovered. So if you're using an
Speaker 1: analyst recommendations, analysts can only cover so many stocks within
Speaker 1: a given day. And there's candies some firms that are
Speaker 1: quite innovative, they're you know, performing and producing some really
Speaker 1: interesting things within our economy. You know, whether it's things
Speaker 1: within advanced healthcare like wearables that aren't really covered by
Speaker 1: Wall Street analysts because they might be smaller capitalization securities.
Speaker 1: And we sort of just know this even from like
Speaker 1: traditional finance, that the majority of analysts recommendations are in
Speaker 1: that large caps space um and then small caps and
Speaker 1: maycaps sort of do you not get as much notoriety
Speaker 1: or coverage. And AI is basically is one way to
Speaker 1: solve that problem, to give you a deeper breath of
Speaker 1: opportunities and really broaden your scope of companies that may
Speaker 1: be considered innovative. So I want to give a shout
Speaker 1: out to Katie Greifield and Sam Potter on the Cross
Speaker 1: has a team at Bloomberg, because they had this really
Speaker 1: fascinating story that said something like, we asked chat GPT
Speaker 1: to create an e t F for us, and here's
Speaker 1: the results, and and actually it had done a really
Speaker 1: good job putting something together. And you were part of
Speaker 1: this story. And Katie and I were chatting about it afterwards,
Speaker 1: and she said, Matt had all these insights into the
Speaker 1: composition aspect of because you guys have your own AI
Speaker 1: E t F. And I do wonder about that, like,
Speaker 1: is the power of the AI being able to create
Speaker 1: an E t F? Is the power? Does it lie
Speaker 1: in the sheer amount of work that it can do,
Speaker 1: whereas you might not be able to have like a
Speaker 1: team of humans combing through so many different things to
Speaker 1: the point where they get to an e t F
Speaker 1: that has five and sixty components. Yeah, it's it's all
Speaker 1: about creating efficiencies and being able to capture, you know,
Speaker 1: undiscovered or unrepresented areas within the equity markets. You know,
Speaker 1: even just looking in core portfolios, disruption happens further down
Speaker 1: that cap spectrum. And that's why using something that is
Speaker 1: able to explore data sets that are really unstructured because
Speaker 1: revenue profiles balances those more structured data sets. But using
Speaker 1: textual language processing to identify firms based on what their
Speaker 1: material operations are saying is one way to help classify
Speaker 1: them into these areas of innovation. And I think one
Speaker 1: of the things about this fund in particular is that
Speaker 1: we do understand that it is not innovation does not
Speaker 1: just benefit the pure place, is that the ecosystem around
Speaker 1: it can benefit. You know, the whole idea during the
Speaker 1: gold rush of the eighteen hundreds of it would rather
Speaker 1: mind for gold or sell the pick axes and the
Speaker 1: tents to go along with it. You probably had a
Speaker 1: pretty good business model if you're selling a lot of
Speaker 1: pick axes in the eighteen hundreds. And that's sort of
Speaker 1: the idea here is, you know, the ecosystem is also beneficial,
Speaker 1: and how do you identify that ecosystem? Uh? You know,
Speaker 1: affirm like Video for example, they make all of the
Speaker 1: sensory technology with an autonomous vehicles. That's a supplier to
Speaker 1: that ecosystem. And as autonomous vehicles take off, they're going
Speaker 1: to benefit as well, so using AI to to detect
Speaker 1: that can really help create a really targeted, but diversified
Speaker 1: portfolio of innovative stocks. And basically this e t F
Speaker 1: has many more components than it would if a team
Speaker 1: of humans was putting it together. Right, yeah, so the
Speaker 1: you know the statistic, they're over ten analysts. So let's
Speaker 1: just say we use that as an example, like we
Speaker 1: needsily at least be covered by ten ten analysts will
Speaker 1: right then and there we lose half the portfolio and
Speaker 1: tends not a big number. So if we were to
Speaker 1: take more of a human based approach to it, it
Speaker 1: would be far more concentrated with portfolio. And that's what
Speaker 1: we see with the other broad innovation e t f
Speaker 1: s out there, is that they're far more concentrated and
Speaker 1: they're also far more geared towards large cap security. So
Speaker 1: you do not get the differentiation that you would want
Speaker 1: in something that is supposed to be innovative and you know,
Speaker 1: not largely represented within core portfolio. Is right when this
Speaker 1: fund was launched, I guess it was three or four
Speaker 1: years ago. You know, growth stocks, innovative disruptive stocks were
Speaker 1: you know, the hottest things going in the market, and
Speaker 1: the funded great, you know, a few thousand and nineteen
Speaker 1: up thirty seven, two thousand twenty up sixty one percent,
Speaker 1: up about four twenty one. Then obviously last year was
Speaker 1: kind of the rug pole out from under growth and
Speaker 1: innovation down. So I'm wondering, you know, is there a
Speaker 1: way to layer AI on top of a fund like
Speaker 1: this to allow to sort of shift to a value
Speaker 1: strategy or to kind of sniff out the market cycle
Speaker 1: into what's kind of the the new hot factor to
Speaker 1: get to UM. I know that's not the goal of
Speaker 1: this fund, but I wonder if you think about that,
Speaker 1: you know, is there is there a way to not
Speaker 1: only pick the the individual stocks under a certain theme
Speaker 1: or strategy like this, but so also have that strategy
Speaker 1: sort of evolve over time and try to you know,
Speaker 1: isolate the upcoming market cycle and what's gonna what's the
Speaker 1: leadership is gonna be? Uh in case growth does have
Speaker 1: a down draft like this, So I mean, that's when
Speaker 1: that becomes just market timing, so to speak. Right, and
Speaker 1: you're now you're now doing some former factor rotation. You know,
Speaker 1: I think you could but perhaps create more of a
Speaker 1: style style neutral innovative portfolio, but that becomes much harder
Speaker 1: because then you're going to have in an optimization framework,
Speaker 1: the optimizer is gonna be working really hard to mitigate
Speaker 1: any of that small cap bias, so and then you're
Speaker 1: just gonna basically look like you know, a large cap
Speaker 1: growth tech exposure. So then it's always this trade off
Speaker 1: of like, do I want to mitigate some of these
Speaker 1: implicit style factors and get you know, sort of close
Speaker 1: up that tracking risk to traditional benchmarks, or maintain the
Speaker 1: purity of what we're trying to do of innovative exposures.
Speaker 1: So you always try to find that balance. And if
Speaker 1: you try to create more style neutral or or something
Speaker 1: that is, you know, less impacted by market cyclical factors,
Speaker 1: then you're gonna lose some of the purity of your
Speaker 1: intended focus. And I think when we are having discussion
Speaker 1: ground performance, we always just go back to attribution and
Speaker 1: we will use you know, UH fundamental risk models. And
Speaker 1: if we look at it, since inception, industry and stock
Speaker 1: selection effects relative to UH, you know, the like the
Speaker 1: SMP fIF for example, industry and stock selection effects have
Speaker 1: been positive to UH. The funds return has been a
Speaker 1: dative to performance. The industry party is interesting because there
Speaker 1: are some industries like semiconductor software, um you uh, sort
Speaker 1: of wearable technologies within healthcare, those industries are gonna be
Speaker 1: more innovative than say some firms within like staples and
Speaker 1: you're sort of consumer goods products. So industry effects byproduct
Speaker 1: of the folks of innovation. Stock selection effects is by
Speaker 1: product of the AI selection methodology and then the waiting
Speaker 1: um process. The detractors of returns have been style factors,
Speaker 1: namely higher volatility, lower quality, and high high growth you know,
Speaker 1: since inception. But those factors are are implicit because it's
Speaker 1: not what we're we're seeking to obtain. But they're also cyclical.
Speaker 1: So high volatility, low quality, high growth were being famously rewarded,
Speaker 1: uh star and through you know sort of mid right,
Speaker 1: So that was as a tail wind two returns back then.
Speaker 1: So that's how we always like to frame the performance
Speaker 1: conversation is breaking those three components out, noting that the
Speaker 1: style components are going to be cyclical and move in
Speaker 1: and out based on market directions. I'm always curious how
Speaker 1: E t F issuers decide on a theme or a
Speaker 1: topic or you know, putting an e T F together.
Speaker 1: So a couple of years ago. Was AI something that
Speaker 1: you guys when you got together, we're thinking was going
Speaker 1: to be a big deal in the coming years, or
Speaker 1: is it sort of which I think this happens a
Speaker 1: lot in the E T F space. Let's just put
Speaker 1: it out there, give it a try, and see what happens.
Speaker 1: So it's definitely not the ladder within our firm, We're
Speaker 1: definitely not that. Yeah, we're we're not gonna be like, hey,
Speaker 1: this is a hot dot, let's throw it out there
Speaker 1: and see if it works. You know, that's just not
Speaker 1: what we do UM with respect these funds. We have
Speaker 1: a pretty strong heritage within sector and industry investing, and
Speaker 1: we know that there are thematic investors out there. We
Speaker 1: see it all the time within our traditional industry suite.
Speaker 1: You know, someone that wants to play a rally and
Speaker 1: oil stocks will go by xop our Oil and Gas
Speaker 1: ETF and that's a thematic investor. And we knew that
Speaker 1: thematic investing was was going to be UM on the
Speaker 1: rise because there's some thematics like auntonomous vehicles or cybersecurity
Speaker 1: or clean energy that are hard to to to gain
Speaker 1: exposure to under a traditional GETS framework. Because some of
Speaker 1: these firms are are operate across gig sectors. You know,
Speaker 1: clean energy is a perfect example. You have firms within
Speaker 1: the legacy energy sector, the utility sector, industrial sector, technology sector,
Speaker 1: so you want to go across the sectors. So we
Speaker 1: were like, well, how do you go about doing this again?
Speaker 1: We wanted something that was forward looking. We knew that
Speaker 1: revenue was back with game. So this is how we
Speaker 1: landed on, you know, firm like Ken Show and then
Speaker 1: later obviously S and P Ken Show as a combined
Speaker 1: entity of having a really unique value proposition of using
Speaker 1: natural language processing to detect firms that are listing out
Speaker 1: these innovative UM services or innovative corporate designs as part
Speaker 1: of their material operations. UM. So that's that was really
Speaker 1: the impetus for it. And I think, you know, I
Speaker 1: sort of remember one instance. It was I think it
Speaker 1: was obviously before we launched, was probably time frame when
Speaker 1: we're really starting to kick the tires on this. The
Speaker 1: Pokemon vert augmented reality iPhone app was just really really popular.
Speaker 1: I remember playing softball and seeing a bunch of people
Speaker 1: like hanging out by the left field tree. We had
Speaker 1: no idea why, and someone put a Pokemon stay. I
Speaker 1: don't play this, so I have no idea, and I
Speaker 1: remember talking to folks and internally like you would be
Speaker 1: really interesting if we could have something that focused on
Speaker 1: these type of firms, you know, innovating within virtual reality
Speaker 1: and augmented reality coincided at the same time as we're
Speaker 1: kicking the tires on on this process. And that's kind
Speaker 1: of the idea now, owning twenty stocks and augmented reality,
Speaker 1: is that, you know, pure play investment thesis for the
Speaker 1: long term, probably not, but having it part of a
Speaker 1: more diversified innovative exposure probably is. And that's sort of
Speaker 1: where we ended up. They were looking for for for
Speaker 1: Pokey Balls, I think, right, yeah, some rare Pokemon character
Speaker 1: or something. I don't know. I've never played it either,
Speaker 1: But do you remember people wandering around turning out their phones,
Speaker 1: pumped into each other. It was, I think the kind
Speaker 1: of cave and went though, which is weird, you know,
Speaker 1: it's it's uh. I almost thought that type of gaming
Speaker 1: would have caught on more, you know that using that
Speaker 1: location element of your phone more. But who knows, maybe
Speaker 1: maybe something is coming. I'm curious just if you can
Speaker 1: give us kind of a thirty ft view of how
Speaker 1: you're thinking about AI. Now. Like I said at the beginning,
Speaker 1: you know this chat GPT does seem to to sort
Speaker 1: of us layman like a big innovation, Like suddenly the
Speaker 1: innovation in in AI has accelerated faster than I think
Speaker 1: UM people realized. UM tell me if you agree with
Speaker 1: that or disagree, But also UM, in general, where do
Speaker 1: you see AI? What industries do you see being most
Speaker 1: susceptible to disruption from AI going forward? Yeah, I mean
Speaker 1: I think for the most part, you know, AI investment
Speaker 1: I think is projected to increase something in respected like
Speaker 1: a fift percent over the next three years. That, like,
Speaker 1: the statistics around AI investment is astounding. You see it
Speaker 1: every day, big numbers, big percentages. I think from an
Speaker 1: industry perspective, something that like paralegal services could be something
Speaker 1: like that UM research documentation. We're able to scan something
Speaker 1: very quickly, and I think you can even see that
Speaker 1: and some of the McKenzie studies that you talk about,
Speaker 1: how you know upwards of the workforce we need to
Speaker 1: change jobs as a result of advances in the artificial intelligence.
Speaker 1: Legal requests are are likely to be one of those
Speaker 1: because you know, going and pulling all of the specific
Speaker 1: you know, court cases over the last fifty years. Really
Speaker 1: the one topic you know that could be done quite
Speaker 1: easily through natural language processing is you know, using predictive
Speaker 1: tax searching for tax I think that's just one one
Speaker 1: of those. Just even within my team, we're trying to
Speaker 1: use some form of AI to help, you know, right,
Speaker 1: weekly notes for us, it's something that you know, some
Speaker 1: I put out on our plans for this year is
Speaker 1: just you know, again creating more efficiencies and some of
Speaker 1: the weekly notes are more about you know, fun flows
Speaker 1: and market performance and you're having something easily done quicker
Speaker 1: that there's also can be helped from a compliance perspective too,
Speaker 1: because everything's rublespaced. But yeah, that's the legal one of
Speaker 1: always ones that comes to mind any sort of documents search,
Speaker 1: document retrieval, UM. Those that's where a I think is
Speaker 1: some of the more low hung fruits. It doesn't sound
Speaker 1: as flashy, but you know that's um that's one Well,
Speaker 1: if you're a law firm, you're certainly gonna save a
Speaker 1: boatload of money, uh if you can you know, hire
Speaker 1: a fewer paralegals to do all that. That's sort of
Speaker 1: leg work. But I think podcast hosts are totally fine, right,
Speaker 1: don't chinx us, Yeah, don't chin us. I don't know
Speaker 1: that chat JBT wrote wrote some pretty good dad jokes,
Speaker 1: So I'm feeling threatened you might be out of that job. Yeah,
Speaker 1: even kind of. I mean, who would have thought that,
Speaker 1: you know, they would could create a young Luke Skywalker
Speaker 1: and the most recent or last seasons of the Mandalorian.
Speaker 1: You know, all of a sudden they can use you know,
Speaker 1: AI and some of the other stuff to create different
Speaker 1: voice structures. Who knows. I think podcast has have a go,
Speaker 1: have a good chance of out last night for at
Speaker 1: least the next twenty years. Fun fun podcast hosts. Maybe
Speaker 1: just to bring it back to the market, I'm wondering,
Speaker 1: like which sectors maybe can stand to benefit the most
Speaker 1: from AI. That sort of tough because I think, you know,
Speaker 1: obviously within technology, a lot of firms for already starting
Speaker 1: to use AI and their processes. I would probably say
Speaker 1: within the industrial sector for supply chain logistics, um, other
Speaker 1: sort of you know, consumer oriented areas in terms of
Speaker 1: consumer service. So you can obviously already see it with
Speaker 1: Amazon and some of the way they interact with consumers
Speaker 1: and using AI. Um, I would say probably those three
Speaker 1: probably the biggest round industrials. You know, you can supply
Speaker 1: chain and then consumer and then tech is just going
Speaker 1: to benefit because they're the ones sort of creating the innovation. Yeah, yeah,
Speaker 1: mat I know. So we've been talking all out about AI,
Speaker 1: which is one of your focuses, but not not the
Speaker 1: only ones. So I'm curious if you can just give
Speaker 1: us kind of the state of play in the TF
Speaker 1: market as a whole. You know, what, what kind of
Speaker 1: flows are you seeing? Uh this year? You know, market
Speaker 1: obviously off to this super strong start, growth and innovation
Speaker 1: doing well again. Where are you seeing the flows? Are
Speaker 1: people chasing that sort of rebound in innovation and growth
Speaker 1: or they still going into value? What's uh? What do
Speaker 1: the flows look like? Yeah, so thematic ETFs last year
Speaker 1: in two actually had outflows, and they had outflows for
Speaker 1: the first time since two thousand and thirteen. Now k
Speaker 1: OMP actually had inflows. A little bit of a divergence there,
Speaker 1: um maybe speaking to our efforts, but a lot of
Speaker 1: it was for our performance related. Roughly of all thematic
Speaker 1: ETFs on the et F industry beat the SP five
Speaker 1: hundred last year. That's actually been different this year. This
Speaker 1: year we're around eighties. Six of thematic ets are beating
Speaker 1: the sp five hundred. Yet at the end of January
Speaker 1: flows we're still negative for the broader category. So ETF
Speaker 1: investors are still little skeptical, which I think is not
Speaker 1: too surprising given the dour performance results from last year UM.
Speaker 1: But you know, again within our suite we've actually seen
Speaker 1: influence which you know, perhaps speaks to the efficacy of
Speaker 1: the UM, the product type, the structure, the rationale on
Speaker 1: the investor motivation. Matt, we can't let you go without
Speaker 1: asking you about Spy, which is probably the best known
Speaker 1: at F out there, and it just turned thirty years old.
Speaker 1: So happy birthday, just SPI. I know you guys through
Speaker 1: it a couple of birthday parties, but can you maybe
Speaker 1: tell us about this like it's been around for thirty
Speaker 1: years ow I think we had a story on Bloomberg saying,
Speaker 1: you know, it's held the crown for so long, but
Speaker 1: can it continue to hold on to this sort of
Speaker 1: the crown of being the most prominent and well known
Speaker 1: e t F. So maybe just tell us about by
Speaker 1: a little bit, just because we have you here and
Speaker 1: you're the sort of pre eminent figure I'm talking about this. Yeah,
Speaker 1: so I mean Spy. Like I said, you know, without Spy,
Speaker 1: there's a lot there's no k MP, but there's not
Speaker 1: a lot of other e t f s out there.
Speaker 1: It started the industry. U. The infrastructure that it has
Speaker 1: is the reason why we do have ETFs, the ability
Speaker 1: to in kind creation redemption. UH. And it's been time
Speaker 1: tested throughout those past thirty years. And well, I think
Speaker 1: this year taught us it. Spy had a record amount
Speaker 1: of users come to that product in terms of it
Speaker 1: had nine and a half trillion dollars of trading volume.
Speaker 1: It had a record amount of overall shares traded and
Speaker 1: you know, roughly uh is of all trading volume in
Speaker 1: et fs was on it was on Spy. UM. So
Speaker 1: I think that's just really you know, a good indicator
Speaker 1: of UM how much usage it still gets even though
Speaker 1: it's thirty years after its inception. And it was interesting
Speaker 1: we were talking with jet Chat GPT earlier that when
Speaker 1: you do ask chat GPT what is the best et F,
Speaker 1: it does come back Spy. And I think it's with reason.
Speaker 1: You know, it's it's the biggest, it's the most liquid,
Speaker 1: it's the longest trajectory um and for that reason, chat
Speaker 1: GPT recognizes being one of the better ets out in
Speaker 1: the marketplace, bringing in full circle there. I like it. Oh,
Speaker 1: Matt Bartolini's the head of Spider's America's research at State
Speaker 1: Street Global Advisors. Great stuff. We really appreciate your time.
Speaker 1: We cannot let you go though, and so uh we
Speaker 1: hear the craziest thing you've seen in markets this week?
Speaker 1: Data as always, why don't you get us started? Okay,
Speaker 1: So mine is in crypto this week, and it's this
Speaker 1: report by chain Alis this which I don't know if
Speaker 1: you don't know about the analysis. They sort of do
Speaker 1: like forensics basically of the blockchain and within the crypto space.
Speaker 1: So it's interesting that such a report will come from
Speaker 1: a crypto company specifically, But basically, they found that thieves
Speaker 1: stole a record three point eight billion dollars worth of
Speaker 1: cryptocurrency last year, and at North Korea itself, it's estimated
Speaker 1: uh still one point seven billion dollars in up from
Speaker 1: four million the year prior, which is just crazy amounts
Speaker 1: of money. Because you know, it's people in crypto don't
Speaker 1: like to talk about this aspect of crypto, but then
Speaker 1: you have this crypto company actually coming out with this
Speaker 1: report talking about if that's that's at current market prices
Speaker 1: were at it Probably it's probably at the price of
Speaker 1: the assets when they were stolen, I would imagine, But
Speaker 1: I would think so, yeah, I would think so, But
Speaker 1: I mean, yeah, but even if you think about where
Speaker 1: bitcoin was yeah a couple of months ago versus not. Yes,
Speaker 1: And they probably don't answer for this in that report.
Speaker 1: But I wonder how much of that is sort of
Speaker 1: trapped you have. Can you steal some crypto and it's
Speaker 1: stuck in a while and everyone knows it's there, and
Speaker 1: it's it's sometimes hard to launder that. I'd be curious
Speaker 1: to see how much of that actually, you know, these
Speaker 1: thieves are enjoying the benefits of that at the Yeah,
Speaker 1: there are some companies, some crypto like researchers that look
Speaker 1: into when sizeable sums of coins are moved, or like
Speaker 1: nineteen thousand coins that hadn't moved in ten years or
Speaker 1: some which I really interested. That's when you never know
Speaker 1: where they're going with That's when the thirties always catch them.
Speaker 1: Two is the minute you try to move it, and
Speaker 1: the something else than the exactly the FBI is watching.
Speaker 1: That's a pretty good How about you, Matt, you see
Speaker 1: anything crazy recently? I mean, I actually one of the
Speaker 1: craziest things the market reaction to the most recent Federal
Speaker 1: Reserve great hike I didn't think would be that overwhelmingly positive.
Speaker 1: Powell was still pretty persistent on the need to hike
Speaker 1: rate um and right now you have a two year
Speaker 1: yield that is roughly fifty basis points below what the
Speaker 1: Fed funds is and that doesn't really happen. I think
Speaker 1: that's pretty crazy. Is that, you know, borrowing money two
Speaker 1: years out is cheaper than overnight rates at the Reserve.
Speaker 1: So I think that's I'd be interesting what happens in
Speaker 1: the ensuing days if that course corrects. Yeah, that is
Speaker 1: a It is a bizarre upside down world. And uh,
Speaker 1: I don't think the market reaction was anything what he
Speaker 1: hadn't sended. I've joked that Palp should probably have a
Speaker 1: Bloomberg terminal in front of him when he's giving the
Speaker 1: press conference to it to amend his answers to to
Speaker 1: have the desired effect, because I don't think I don't
Speaker 1: think that's what he was after that day. But we
Speaker 1: should send him. Yeah, I bet he. Well he I'm
Speaker 1: assuming he has one. I know, I think he has one.
Speaker 1: All right, we'll give you mine. Yeah, well do as
Speaker 1: i've You know, I'm not really a car guy. I'm
Speaker 1: more of a pedestrian. But I real. But what happened
Speaker 1: to those four portions? Yeah, they're they're still imaginary. They
Speaker 1: still are imaginary. I am into when people pay ridiculous
Speaker 1: prices for collectible items. As you know though, So the
Speaker 1: story's courtesy of CNN. So if you ever heard of
Speaker 1: the car company Bugatti, they make these like hot rods, supercars.
Speaker 1: They call them. Uh yeah, I heard you have two
Speaker 1: of them? Yes, yes, matchbox size. But so Bugatti apparently
Speaker 1: is transitioning to electric. They're gonna go hybrid first. Um,
Speaker 1: but they're done making uh strictly gas powered cars. So
Speaker 1: they recently produced the last pure gas line powered car
Speaker 1: they're ever gonna make. It's a um, I'm probably gonna
Speaker 1: butcher this pronunciation. The Bugatti cheron profably. I believe I
Speaker 1: didn't take French, but I have something like that. So
Speaker 1: when up for auction, they instead of just selling it,
Speaker 1: they put it up for auctions with Southern beas I believe.
Speaker 1: I'm just gonna tell you what it went for on auction.
Speaker 1: It's boring to to name the price. Uh, ten points
Speaker 1: ten point seven million. This car million, brand new car
Speaker 1: set a record for the highest priced new car sold
Speaker 1: at auction. But what I'm gonna make you guys square
Speaker 1: off against each other in our game show is what
Speaker 1: do you think the max speed is that this vehicle
Speaker 1: is capable of reaching the fastest it can go? Oh
Speaker 1: my gosh, in miles per hour for ten point seven
Speaker 1: million dollar car? How fast do you think you get
Speaker 1: to go in that car when you flo It's only
Speaker 1: fair if we can name it in kilometers. All feel
Speaker 1: free to do that, but you need to translate it
Speaker 1: to miles for me, like like, oh my gosh, um,
Speaker 1: I'm guessing it's not as high. But I really I
Speaker 1: know nothing about cars. Am I going first? There's not
Speaker 1: going first? I think you go first? Yeah, fine, I'm
Speaker 1: gonna go with two sixty two d and sixty miles
Speaker 1: an hour. I don't know. Is that a lot? That's
Speaker 1: a lot that's way too much? A lot that's like
Speaker 1: a plane here? I would say I would probably like too,
Speaker 1: Oh my gosh, I think we uh I think we
Speaker 1: have our first tie in the Prices Precise to thirty six. Wow,
Speaker 1: so you guys are pretty close. Although traditional rules she
Speaker 1: went over vill Donna's, I think we gotta give it
Speaker 1: some run over. It's fine, the guests can win. That's
Speaker 1: that's right here, Prices Precise rules. Don't get our lawyers involved.
Speaker 1: This is called the Prices Precise Yet. Here is the
Speaker 1: crazier thing, though, is that's not the fastest car got
Speaker 1: he's ever sold. The fastest could go three hundred miles
Speaker 1: an hour, they say, quote in theory, and I'm not
Speaker 1: sure everyone anyone's ever managed to get it up to
Speaker 1: three hundred. I don't know if you could. Tom Cruise
Speaker 1: would if you gave him a chance for one of
Speaker 1: his good It's probably has several of these, but I'm
Speaker 1: not sure if you could. Theoretically, if you could drive
Speaker 1: a car three hundred miles an hour, I feel like
Speaker 1: it would take off like a rocket ship at that point,
Speaker 1: like I would my heart would burst from it, like
Speaker 1: I'd be so scared. You definitely have to live. If
Speaker 1: you're driving that fast, you gotta listen to your podcasts
Speaker 1: at double speed. I think so if we have any
Speaker 1: boogotten drivers out there allow them to double speed us
Speaker 1: two x X. Yeah, pretty good, though you guys are
Speaker 1: both in the ballpark. I'm not sure what I would
Speaker 1: have I would have guessed. I'm not sure if I
Speaker 1: would have gone over two hundred. It just seems insane
Speaker 1: to drive over two hundred miles an hour. But anyway,
Speaker 1: you don't go. You don't go two hundred miles an
Speaker 1: hour in the New Jersey Turnpike. Well, New Jersey Transit
Speaker 1: I do. Yeah, that's the when we're late anyway, Matt
Speaker 1: Partalini from State Treet Global ADVISORSS just real honor to
Speaker 1: be able to pick your brain on all these topics. Uh,
Speaker 1: wish you all the best and hopefully you'll come back
Speaker 1: and talk to us again some day. Yeah, thanks, thanks Matt,
Speaker 1: what goes up? We'll be back next week. And so
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Speaker 1: next time.