Speaker 1: Welcomer trains. I'm Joel Webber and I'm Eric Belchunas. Eric,
Speaker 1: this was your idea.
Speaker 2: I'm really curious about where this conversation goes today because
Speaker 2: all of this stuff was like candy as I've prepared
Speaker 2: for it.
Speaker 3: Yeah, you know, we're getting back to the basics today.
Speaker 3: You know, how do you value a stock?
Speaker 2: Now?
Speaker 3: That's his stock conversation. But stocks make up index funds,
Speaker 3: which make up ETFs, and a big wing of ETFs
Speaker 3: are called smart beta, which are ETFs that sort of
Speaker 3: use metrics that an active manager would use, price to earnings, price,
Speaker 3: the book, dividends, et cetera. And within there there's all
Speaker 3: kinds of variations. So in a way it's active even
Speaker 3: though it uses an index. And so there's an ETF
Speaker 3: that launched recently called the Sparkline Intangible Value ETF. I
Speaker 3: TAN is the ticker, and this uses the intangible value,
Speaker 3: which this person claims is a new factor. The reason
Speaker 3: to call my attention is I was at the Democratized
Speaker 3: quant event I don't know six months ago, which Wes
Speaker 3: Gray from alf Architect puts on, and I saw this
Speaker 3: guy Kai Wu debate Cliff Asnas who is like giant
Speaker 3: in the quant world. I mean, he's like heavyweight, a lister, right,
Speaker 3: and Kai has this ETF. It's indy and it was
Speaker 3: sort of like a David and Goliath debate, no offense,
Speaker 3: but Cliff had met his match here. I thought Kai
Speaker 3: made some very good arguments. I was more on his
Speaker 3: side by the end of the debate. Cliff was a
Speaker 3: good sport. It was a great discussion. I love the quants.
Speaker 3: They do a very academic type of rigorous debate when
Speaker 3: they have events, and I like that. It was two
Speaker 3: sides presented, and I thought we got to get this
Speaker 3: guy on because not only is intangible value interesting and
Speaker 3: people should know what that means. But when you think
Speaker 3: of smart baita ETFs, like a value ETF, many of
Speaker 3: them use price the book. Well what does that mean?
Speaker 3: What is book value? Well, a lot of the book
Speaker 3: values are old. They don't use things like the brand.
Speaker 3: They use like how much actual literal capital goods the
Speaker 3: company owns, and so they don't use real estate. So
Speaker 3: there's this huge debate in the quant world on how
Speaker 3: to actually define price the book, and that is a
Speaker 3: major pillar of how you define value. So if you're
Speaker 3: shopping for a value etf or value manager. This stuff
Speaker 3: is important to know.
Speaker 2: Joining us Kai Wu who's the founder chief investment officer
Speaker 2: of Sparkline Capital, as well as Chris Kane, who's a
Speaker 2: quant analyst at Bloomberg Intelligence, this time on Trillions the intangibles, Kai, Chris,
Speaker 2: Welcome to trillions.
Speaker 4: Thanks for having me, Thanks for having me.
Speaker 1: Okay, Kai, what is intangible value?
Speaker 5: So an intangible asset is, you know, as Eric was saying,
Speaker 5: anything that's not your kind of factories, your cash, your property.
Speaker 5: It's at Sparkline we have four pillars of intangibles. We
Speaker 5: talk about intellectual property, brand equity, human capital, and network effects.
Speaker 5: So again IP, human capital, brand, network effects, and these
Speaker 5: assets are becoming more and more important for companies today.
Speaker 1: Why is value investing not capturing this?
Speaker 5: So when you think about value investing, it was really
Speaker 5: popularized in the nineteen thirties with bang Gram's Security Analysis book.
Speaker 5: And you go back to the thirties, right, the economy
Speaker 5: was fully industrial. The big companies were railroads and textile mills,
Speaker 5: and as a result, you know, these sorts of intangils
Speaker 5: didn't really matter too much. But today the biggest companies
Speaker 5: are you know, Apple, Facebook, firms for whom their book
Speaker 5: value does not actually is not actually.
Speaker 4: Required to produce earnings.
Speaker 5: And so you know, think about you know Apple for example, right,
Speaker 5: it's their brand, it's their the network effects around the
Speaker 5: iPhone iOS ecosystem, it's the human capital and IP around
Speaker 5: you know, their internal processors and such that allow them
Speaker 5: to earn such fat profit margins.
Speaker 2: Why hadn't this been captured in an ETF until yours?
Speaker 4: You know, I don't know.
Speaker 5: There have been attempts to to kind of capture some
Speaker 5: of the intangible assets using accounting based metrics. So what
Speaker 5: the weird anomalies within the way accounting works that gap
Speaker 5: accounting is that they allow for the capitalization of physical
Speaker 5: capex but not intangial assets. Right, so there are they
Speaker 5: have been attempts to reverse that by saying, all right,
Speaker 5: you're gonna spend one hundred million dollars building a building.
Speaker 4: A factory that gets capitalized.
Speaker 5: You're gonna spende hundred million dollars doing R and D
Speaker 5: to develop a patent that gets capitalized, and doing so
Speaker 5: you can kind of create a more holistic version of
Speaker 5: book value is a little bit better. But what we
Speaker 5: found here at sparkline is that you know that only
Speaker 5: takes you so far, because again, like the link between
Speaker 5: the money you put into R and D and what
Speaker 5: you get out is super wide. So what we like
Speaker 5: to do here is to focus instead on the actual products,
Speaker 5: the actual outputs. You know, what patents do you actually get,
Speaker 5: how strong is the brand you actually build through your
Speaker 5: advertising efforts? And I think that's kind of a novel
Speaker 5: and unique approach that really only became available with the
Speaker 5: advent of you know, instructured data and natural language processing,
Speaker 5: which we'll get into in a bit.
Speaker 1: So it is your is your fund.
Speaker 3: It isn't a value ETF that actually just uses this
Speaker 3: one tweaks this one part. It's more of let's go
Speaker 3: after the companies with the highest in tangible value. Again,
Speaker 3: that's different then let's do a traditional value ETF. But
Speaker 3: let's correct how we define price the book, right yours
Speaker 3: is let's go after these stocks that are high and
Speaker 3: intangible value, right yeah.
Speaker 5: And I think one important thing to mention is that
Speaker 5: it's not like we're going to go after the companies
Speaker 5: with simply the most total overall innovative patents, let's say,
Speaker 5: because then that just map do large cap names. Right,
Speaker 5: what we carry about is how much as a shareholder
Speaker 5: you get per dollar invested. Right, So it's very similar
Speaker 5: like a dividend yield or like an earning yield. So
Speaker 5: for each dollar invest how many you know PhDs do
Speaker 5: I get? As an investor? How many you know Twitter
Speaker 5: followers do I get? And so these are kind of
Speaker 5: proxies for intangible assets. But again the key just being
Speaker 5: that they're price based, very similar to price to book,
Speaker 5: but you know, using kind of a more expansive set
Speaker 5: of variables.
Speaker 3: And let's bring in Chris Kane here, because Chris spends
Speaker 3: all day looking at this quantitative data he has builds indices,
Speaker 3: And I was curious, Chris, you know, sort of your
Speaker 3: take on this. And you have to have a price
Speaker 3: to book in your metrics and how you define that,
Speaker 3: and I'm just curious to get your you know, how
Speaker 3: you've considered this for your work.
Speaker 6: Sure, I mean, I you know, I love this concept.
Speaker 6: You know when I go to you know, customers and
Speaker 6: speak to them about value investing, you know a lot
Speaker 6: of the feedback I get it as well. These are
Speaker 6: old companies. This is an old way to do it.
Speaker 6: You know, this is kind of like anti innovation, and
Speaker 6: you know, I don't have to tell everyone, you know
Speaker 6: that we're kind of living through a gold native innovation
Speaker 6: in many ways. You look at the cues, you look
Speaker 6: at r KK, et cetera. So you know, do you
Speaker 6: do you?
Speaker 7: But what really always helped me back.
Speaker 6: From those type of funds is like they're anti factor, right,
Speaker 6: they're very high vaulved, they're very expensive. So do you
Speaker 6: view you know, your your fund more like a value
Speaker 6: tilt or you know, an innovation tilt, but without those
Speaker 6: like bad factor waitings.
Speaker 5: Yeah, I think that's a very fair way of characterizing
Speaker 5: the strategy, right, It's it's in an innovation fund without
Speaker 5: the kind of baggage, where as an investor, you don't
Speaker 5: have to sacrifice your value exposure or your quality exposure
Speaker 5: by going into it.
Speaker 7: Yeah, so interesting.
Speaker 6: So would you consider this like, I mean, would you
Speaker 6: consider more like a growth fund or like a traditional
Speaker 6: value fund, or would you consider it completely different and
Speaker 6: separate in the stins?
Speaker 4: Yeah?
Speaker 5: Look, I mean I don't love the whole value versus
Speaker 5: growth economy. I don't think it's it's fair to say
Speaker 5: you have to be either one or the other. You know,
Speaker 5: Warren Buffett has talked about this as well, as you know,
Speaker 5: this is kind of being a false construction. The way
Speaker 5: I would think about it is a traditional value ETF. Right,
Speaker 5: what are they trying to do. They're trying to look
Speaker 5: for stocks with low price to book ratios. In other words,
Speaker 5: book value is a proxy for tangible capital. So they're
Speaker 5: going to look within the tangible economy, the old economy
Speaker 5: as you point out, industrials, banks, energy materials, and find
Speaker 5: the cheapest names, which is a totally valid thing to do.
Speaker 5: But you know, obviously, you know, as we move forward
Speaker 5: in time with innovation, AI, et cetera, this is becoming
Speaker 5: a vanishingly small part of the stock market. So what
Speaker 5: we're trying to do with the intangible value ETF is
Speaker 5: the same exact thing. We're looking for cheap stocks, but
Speaker 5: relive not to tangible but intangible capital, which ends up
Speaker 5: mapping to consumer brands to you know, tech platforms, you
Speaker 5: know life sciences companies, and you know services businesses. So
Speaker 5: it's kind of the same concept, but apply to the
Speaker 5: other half, so to speak, of the stock market.
Speaker 3: So is what you're saying part of the reason that
Speaker 3: traditional value investing just sort of gets punched in the
Speaker 3: face all the time and just lags for with fifteen
Speaker 3: years at this point. I had a nice little run
Speaker 3: in twenty twenty two, I believe, but now it's kind
Speaker 3: of back in the gutter. Is that why traditional value
Speaker 3: doesn't ever seem to have like a true regime takeover.
Speaker 3: And at the same time, every time you think something
Speaker 3: is coming back, like small caps or international, the CUES
Speaker 3: just wakes up and says, uh uh, I'm going to
Speaker 3: run over you.
Speaker 1: Again and again, run away and again.
Speaker 3: Like Marshall Lynch when we was talking about running people over,
Speaker 3: He's like, I'm to smash you in the mouth again
Speaker 3: and again and again and again and then you finally
Speaker 3: am gonna run over you and then like you just
Speaker 3: talk about how we scores touch downs anyway, piece mode.
Speaker 3: The Cues is in constant bast mode mode. But is
Speaker 3: that is intangible value? The reason that that phenomenon exists
Speaker 3: again and again.
Speaker 5: Yeah, We've actually done some analysis on both the CUES
Speaker 5: and on ARKK and what we did was we said,
Speaker 5: let's look at a factory based framework, right, think about
Speaker 5: the Fama French model, which is, you know, there's the market,
Speaker 5: there's a small cap there's value so on and so forth.
Speaker 5: And we added a sixth factor, which is the intangible
Speaker 5: value factor. And we looked at the holdings of both
Speaker 5: of these funds and then decompose the return, say, can
Speaker 5: we retrospectively explain its performance by allocating to the six
Speaker 5: factors and then idiosyncretic risk their alpha right, And what
Speaker 5: was quite interesting was both of these funds actually had
Speaker 5: a very positive loading on intangible value and in fact,
Speaker 5: a lot of their outperformance relative to Yes and people
Speaker 5: one hundred the traditional stock market has been due to this,
Speaker 5: you know, this this exposure to innovative companies. That being said,
Speaker 5: there's also a lot of volatility around that, as you
Speaker 5: point out, Chris, due to say, exposure to you know,
Speaker 5: cheap press to bookstocks, which you know did really well
Speaker 5: and then did really poorly, and you know kind of
Speaker 5: cycles in these really wide gyrations. And also, you know,
Speaker 5: especially in the case of the ar KAK, the exposures
Speaker 5: earlier stage unprofitable tech companies has been you know, kind
Speaker 5: of a negative contributor to their returns. Just given that
Speaker 5: quality as a factor has just done so well the
Speaker 5: past two decades.
Speaker 2: Curious where the idea for the for for it came
Speaker 2: from was did you have the idea for the ETF
Speaker 2: or did you see a company? And we're like, that
Speaker 2: is the poster child for intangible value. I'm going to
Speaker 2: build a product around it.
Speaker 5: Well kind of both, right, I mean you look at
Speaker 5: the stock market, you look at companies like you know,
Speaker 5: McDonald's or Coca Cola, you know, for whom brands are obviously.
Speaker 4: Critical, Apple, Google.
Speaker 5: Right, and it just kind of makes sense that these
Speaker 5: are the things that should matter today. And it's shocking that,
Speaker 5: you know, the quantitative metrics that we've used for many
Speaker 5: many years.
Speaker 4: Are have not really evolved to do that.
Speaker 5: You know, I used to work for GMO Jeremy Grantham,
Speaker 5: who was a pioneer in developing a lot of systematic
Speaker 5: value strategies in the seventies and eighties, and so I've
Speaker 5: always been thinking about this this problem. And you know,
Speaker 5: we're talking on an ETF podcast value ets or like
Speaker 5: a multie hundred billion dollar if not trillion dollar category,
Speaker 5: if you you know, expand that to also include active
Speaker 5: managers hollow value strategies. So this is a huge question
Speaker 5: and one which I feel like up until you know,
Speaker 5: now you know, just hasn't really been kind of satisfactory,
Speaker 5: literally like addressed. You know, we need more research, more
Speaker 5: and more work to understand the valuation of these names.
Speaker 2: And what problem did you have to solve in order
Speaker 2: to make this thing a reality?
Speaker 5: Well, this goes back to your question about like timing,
Speaker 5: like why now you know, the big problem is that
Speaker 5: accounting accounting statements don't really contain enough insight into intangible assets,
Speaker 5: and so you really need to go to unstructured data
Speaker 5: or alternative data. Right, We're lucky that we live in
Speaker 5: an air now. It's just been exponential growth in big data.
Speaker 5: We have everything from we use LinkedIn glassdoor, you know,
Speaker 5: job postings, patents, mars, all this information you know, obviously
Speaker 5: just by first principles contains insight into intangible value. The
Speaker 5: challenge being that, like the information is kind of locked
Speaker 5: in there because you can't, you know, as a quant
Speaker 5: just take a linear aggression running over it at twenty
Speaker 5: thousand more document and get anything meaningful out.
Speaker 4: It's all just noise.
Speaker 5: And so that's why the advent of the transformer natural
Speaker 5: language processing. You know, we were actually talking about this
Speaker 5: in twenty twenty. We've wrote a paper saying, you know,
Speaker 5: the killer app of AI within investing is then natural
Speaker 5: processing language and NLP, you know toolkit, which allows us
Speaker 5: to take unstructured data and kind of create structured factors
Speaker 5: which can then be used as inputs into traditional valuation models.
Speaker 1: You know what this reminds me of, Joe. I'm going
Speaker 1: to go full metaphor here. Dark matter.
Speaker 3: You know it's out there, you just can't see it,
Speaker 3: and it is. It kind of explains some most of
Speaker 3: the universes comprised of U. Yes, this is why the
Speaker 3: cues are the cues. It's this dark matter of intangible
Speaker 3: value because I'm looking at the holdings here. You know, Amazon, Meta, Google, Cisco, Intel,
Speaker 3: those are some of the firms driving the cues. Chris,
Speaker 3: you know in your world again this concept of dark matter,
Speaker 3: you have to correctly capture factors, track them. How do
Speaker 3: you work this in so?
Speaker 6: I you know, I read the white paper and a
Speaker 6: big fan. You know, I do view this as a
Speaker 6: different type of factor. You know, I don't think as
Speaker 6: you did with your six factor model. I don't think
Speaker 6: you throw out per se traditional value as you showed
Speaker 6: in the paper. You know the correlation between traditional value
Speaker 6: and tangible value is pretty low. If I remember, actually
Speaker 6: the correlation was higher to quality with intangible value. So
Speaker 6: you know, to me, that's a value add I think,
Speaker 6: you know, it's you know, the economy has changed. I
Speaker 6: mean no one would say not right. I mean, it's
Speaker 6: not plants anymore, it's not those tangible things. So this
Speaker 6: is very logical. I view it as, you know, a
Speaker 6: separate factor at least somewhat, and it can certainly add
Speaker 6: value to a multi factor process.
Speaker 3: Yeah, but why why not just forget traditional value? Like
Speaker 3: why even use old Price the Book? Why isn't the
Speaker 3: quant world much more adjusting things for this? Because it
Speaker 3: does explain so much, and it just seems like if
Speaker 3: you're doing value investing using Price the Book, it's like
Speaker 3: using like a rotary phone or something. I don't understand, Like,
Speaker 3: why isn't this a bigger deal?
Speaker 5: You know, that's a great, great question, and I ask
Speaker 5: myself that each day. But no, But look, we're all
Speaker 5: as researchers kind of building on the edifice of what's
Speaker 5: what's come before us, and you know, Fama French in
Speaker 5: the mid nineties and Germany beforehand, you know, popularize this
Speaker 5: idea of this book to market factor, which is important.
Speaker 5: It's not that it doesn't matter, right to take the
Speaker 5: converse to companies with a lot of IP, but one
Speaker 5: has a huge real estate portfolio and a huge cash
Speaker 5: hoard and the other doesn't.
Speaker 4: Of course, that company should be worth more than the
Speaker 4: other one. So you don't want to not use this.
Speaker 5: It's just that you know, we can maybe do better
Speaker 5: by adding additional dimensions of risk and dimensions of corporate
Speaker 5: performance to our kind of mulo of factors.
Speaker 2: When you think about this and what you've created is
Speaker 2: your model just the model, and it finds the companies
Speaker 2: and then you just you know, balance rebalance quarterly like
Speaker 2: a smart beta fund or are you are you putting
Speaker 2: a little bit of finger on the scale.
Speaker 4: No finger on the scale.
Speaker 5: So I mean my involvements only as a researcher kind
Speaker 5: of setting up the parameters the model, figuring out what
Speaker 5: data sets to look at, and how to build the
Speaker 5: machine learning uh, you know infrastructure, but you know it's
Speaker 5: it's all systematic, it's all data driven, right. Every day,
Speaker 5: you know, new information comes in about you know, employee turnover,
Speaker 5: about you know, cultures, corporate culture increasing, decreasing, you know,
Speaker 5: scandals in the media or all all the good stuff
Speaker 5: new patents, new trademarks, and that kind of feeds into
Speaker 5: the models and it automatically adjusts the relative rankings of stocks.
Speaker 2: And how big of a universe are you able to
Speaker 2: come through right now? And where do you want to
Speaker 2: get to?
Speaker 4: Well, we'll start with the where I want to get to.
Speaker 5: You know, I've actually just been working on a super
Speaker 5: interesting project expanding the universe of stocks to global so
Speaker 5: you know, effectively MSCI all country world.
Speaker 4: I am.
Speaker 5: I so like the nine thousand stocks or so right
Speaker 5: now when you know, in terms of launching products, the
Speaker 5: itn ETF is focused on the top one thousand largest
Speaker 5: us ST so used larger medcap stocks. But obviously that
Speaker 5: if it's not there's no kind of technological reason why
Speaker 5: that was the case. We just wanted to start with
Speaker 5: a product that you know, most people could kind of
Speaker 5: get their heads around.
Speaker 6: You know, one thing I wanted to ask you it It
Speaker 6: was more kind of like the methodology of intangible value.
Speaker 6: You know, you don't have to share secret sauce here
Speaker 6: or anything, but.
Speaker 7: You know, or feel free to or if you want to.
Speaker 1: Yeah, it's probably in the perspective.
Speaker 7: But I you know, you mentioned that you use alternative data.
Speaker 6: I'm guessing as higher frequency data NLP techniques to to
Speaker 6: put some you know, context around it.
Speaker 7: So do you need to use alternative data?
Speaker 6: Could you you know, substitute more traditional like balance sheet
Speaker 6: data or financial statement data for that? How far would
Speaker 6: you get if you did do that? Or is the
Speaker 6: is there really the value add the NLP and the
Speaker 6: alternative data.
Speaker 5: So we use both traditional accounting based information and alternative data,
Speaker 5: and we actually I can give you a very clear answer.
Speaker 5: So if you look at like the performance historically of
Speaker 5: say the MSCI, you know value index right relatively in
Speaker 5: the markets in pretty bad. You know, as you point
Speaker 5: out the past fifteen years. If instead you say, well,
Speaker 5: well let's now allow the capitalization of intangible investment so
Speaker 5: R and D. You know, as you kind of invest
Speaker 5: R and D, you build up a balance sheet asset
Speaker 5: for that and then you appreciate it over time. Same
Speaker 5: for sales and marketing expenditures. Well you get a line
Speaker 5: that's a little bit less bad, but still no panacea, right,
Speaker 5: it still goes down. And then when we said well
Speaker 5: let's start adding you know, more sources of data like
Speaker 5: I mentioned patents, I mentioned LinkedIn, you know, to measure
Speaker 5: each of the pillars using unstructured data. And that's when
Speaker 5: the line starts to look pretty interesting. Right And if
Speaker 5: you look at just the name, so put us out
Speaker 5: even the historical performance, because that's just a back test.
Speaker 5: Is the names you know, changed dramatically as you kind
Speaker 5: of continually iterate and add more and more data sources
Speaker 5: to a portfolio that just looks more like what it
Speaker 5: should look like. Right Like if you if I said,
Speaker 5: like first principles, build me a portfolio companies that are
Speaker 5: you know, attractively valued relative to prodigious and tangibles, right
Speaker 5: that that portfolio looks a lot more like the result
Speaker 5: of having added alternative data than just making this simple
Speaker 5: accounting based changes.
Speaker 3: It seems to me that you know, most people would
Speaker 3: hear this and go, I get it. It's kind of
Speaker 3: like tech stocks, right They they don't have a lot
Speaker 3: of machinery lying around, they're mostly intangible value. But there
Speaker 3: are some companies here that aren't tech. Right, So just
Speaker 3: let's just go over how are these are intangible value?
Speaker 3: Wells Fargo and General Electric those almost seem more traditional value.
Speaker 5: Right, Well, I mean ge in particularly, it's it's mainly
Speaker 5: the brand that's kind of carrying that that company. Wells Fargo,
Speaker 5: like many banks, has obviously a large balance sheet, but
Speaker 5: for them, it's probably gonna be human capital. You know
Speaker 5: that that is its main contributor. And I've actually done
Speaker 5: this work. It's kind of quite interesting because you know,
Speaker 5: even if you look at the website for Ian, we
Speaker 5: do this analysis where we do a balance sheet dot composition,
Speaker 5: So we take all the stocks in the portfolio and
Speaker 5: assign it to a single pill pillar. So for example,
Speaker 5: like a clear example would be like Nike or maybe
Speaker 5: Harley Davidson would be clearly in brand. Right, then you
Speaker 5: have like Pfizer or like a m D clearly in IP.
Speaker 5: And then you know Goldman might be in might maybe
Speaker 5: be a non financial by the human capital, right, And
Speaker 5: when you do that, the balance sheet is you know, yes,
Speaker 5: you know IP, that pillar ends up being about forty percent,
Speaker 5: but it's closely followed by human capital, brand and then
Speaker 5: tangible being the least important. So it is a kind
Speaker 5: of relatively diversity portfolio across you know, a variety of
Speaker 5: different pillars.
Speaker 2: Okay, so if we've got your model and it's this
Speaker 2: heat seeking missile to find intangible value out there. How
Speaker 2: do you weight this in a portfolio? How do you
Speaker 2: look at Wells Fargo or Ge and go like, I'm
Speaker 2: we're gonna uh with the exposure to them?
Speaker 3: Wells Fargo has a one point five percent weight and
Speaker 3: Ge is a one percent, but Apple's a four percent?
Speaker 2: Yeah? Or Amazon or Meta? Like how you know if
Speaker 2: your robots gets to do what it does? Like, how
Speaker 2: do you decide who gets what percentage?
Speaker 7: Yeah?
Speaker 1: Look it's it's and how much does it change over time?
Speaker 4: So the methodology is consistent through time that does not change. Currently.
Speaker 5: What we're doing is there's always a trade off in
Speaker 5: a quant world, as you know, Chris, which is you
Speaker 5: know you have too few stocks and it ends up
Speaker 5: beingcoming like all driven by idiosyncratic risk. Oh you have
Speaker 5: an own you know, Twitter and and elon texts, something
Speaker 5: weird out and then you know you're done right, Like,
Speaker 5: so you want to have a certain amount of diversification
Speaker 5: to protect against that, but you don't want to be
Speaker 5: too many stocks. If you have a thousand of it,
Speaker 5: of a thousand stocks, it's basically just the index one
Speaker 5: at that point, right, So for us, we pick one
Speaker 5: hundred and fifty as our cut off. So it's like,
Speaker 5: you know, trying to strike a balance between being you know,
Speaker 5: concentrated enough around this factor, but also having diversification on
Speaker 5: the name sense. And then in terms of the waiting
Speaker 5: amongst those stocks, there's kind of two things that drive that.
Speaker 5: So the first is just the score, right, higher scores.
Speaker 4: Get more weight, that's obvious.
Speaker 5: The second thing we do, though, is this modified market
Speaker 5: cap waiting, right, And again this is to deal with
Speaker 5: a trade off. So imagine I were to create a
Speaker 5: you know, market cap weighted version of the strategy to say,
Speaker 5: all right, well, like Apple has ten x the market
Speaker 5: cap of stock you know two, so therefore it gets
Speaker 5: next to weight. Well, then you end up with like
Speaker 5: very little breath because you know, especially these megacaps have
Speaker 5: become so large and in the seas, it's you don't
Speaker 5: have much ability to kind of over underweight. On the
Speaker 5: other hand, if you do equal weight instead, you end
Speaker 5: up creating this huge bias towards the factor, right, where like, yes,
Speaker 5: you have a lot of active share, but it's all
Speaker 5: just kind of like junk food, right, It's all just like, oh,
Speaker 5: you know, I just have a small cap and so
Speaker 5: you know, for better or worse, your clients are gonna
Speaker 5: judge against the MP. And if you know, as it
Speaker 5: has played out the past two years, right equal weighted
Speaker 5: RSP for example, has underperformed SPY, you.
Speaker 4: Know you're going to look really bad.
Speaker 5: So we ended up doing this this middle ground where
Speaker 5: we basically half marketapp weight the stocks so that we
Speaker 5: can kind of like thread the needle between these two
Speaker 5: these two challenges.
Speaker 2: Okay, so obviously there's a product in the one fifty,
Speaker 2: but if you have this data, there's the other side
Speaker 2: of the spectrum with the companies that aren't doing so
Speaker 2: good at this. Have you thought about building a product
Speaker 2: that combines the two.
Speaker 4: Yeah.
Speaker 5: Look, I mean we've looked at short side as well, right,
Speaker 5: And if you look at like the so looking at
Speaker 5: the top fifteen percent and you short the bottom fifteen percent,
Speaker 5: that actually works well. Right Historically in back test the
Speaker 5: short side, these things do underperform, right, So in theory
Speaker 5: there is a product around that. Of course, like if
Speaker 5: we're in the ETF space, it's a little challenging to
Speaker 5: do long short, especially on single names, because it's transparent
Speaker 5: and people can kind of pick you off. So that
Speaker 5: hasn't been our starting points. But you know, I come
Speaker 5: from an institution in a world where I used to
Speaker 5: run you know, large hedge funds, and so that's totally
Speaker 5: like a product that could be available to the right client.
Speaker 5: But as it turns out, most of our investor base,
Speaker 5: they like the beta. They like you know, being you
Speaker 5: know long stock to stocks go up over time.
Speaker 3: Yeah, this is really fascinating, this idea of how to
Speaker 3: make a factor strategy, because the academics do long short
Speaker 3: because you're trying to isolate the factor. But when you
Speaker 3: do long short, you get a lot of offsetting, so
Speaker 3: your volatility goes down. So it's a nice easy ride.
Speaker 3: But it never has like a shiny object moment. It
Speaker 3: never has like breakout performance. This is the problem with
Speaker 3: the Jim Kramer ETF. It goes long short, and in
Speaker 3: the advisor world, I think, unlike institutions, they need a
Speaker 3: little shiny object moment. And Chris, you deal with this
Speaker 3: all the time. You do make long short in disease,
Speaker 3: but clearly, when you're actually trying to package some of
Speaker 3: what you do into an ETF marketplace, decisions have to
Speaker 3: be made.
Speaker 7: Sure. Yeah, I mean you know, kay, You know when
Speaker 7: you do long only.
Speaker 6: Obviously you have that equity beta, and I think a
Speaker 6: lot of advisors want that equity beta.
Speaker 7: You know, to me, with long short, you know your real.
Speaker 6: Value add there is a lower correlation, significantly lower correlation
Speaker 6: to traditional stocks and bonds. So if you're a traditional
Speaker 6: investor that has that already, I think that's really where
Speaker 6: long short shines. But long only factor investing is certainly,
Speaker 6: you know, a good approach as well.
Speaker 3: Also listening to Kai and going over the design of
Speaker 3: the ETF and all these decisions that are made, I
Speaker 3: would say you probably made twenty five decisions somewhere not
Speaker 3: to mention all the research. So we're talking like potentially
Speaker 3: one hundred things that you could tweak that would make
Speaker 3: the returns different. That's why I think smart beta is active.
Speaker 3: It's just it's just all the active is done in
Speaker 3: the design. It's like you're designing an active robot. Once
Speaker 3: you close the door and like, you know, screw in
Speaker 3: the bolts, it's now a robot, but all of the
Speaker 3: decisions you made before you close the door are active.
Speaker 1: Would you agree with that?
Speaker 3: Yeah, on hundred percent, Even though you don't do any
Speaker 3: you have no more control over it.
Speaker 1: It's like you are too.
Speaker 4: Now right, Yep.
Speaker 5: All the active decisions is upfront in the construction of
Speaker 5: the model. But then once you kind of finish that
Speaker 5: process and as you say, you you know, turn the
Speaker 5: key and you throw it away, then you know, it
Speaker 5: kind of runs on his own.
Speaker 1: And quants like the fact that the way just to
Speaker 1: clear are two D two active? Is that what you're saying?
Speaker 2: Very active?
Speaker 7: Yes?
Speaker 3: Not, well you heard him. He's coaching Luke and stuff.
Speaker 3: I mean he's pretty active.
Speaker 2: Yeah.
Speaker 1: Yeah, it's not like a dishwasher. That's like like an index.
Speaker 1: That's so there's other ones that were on the on
Speaker 1: the rig. Yeah, I don't know what C three PO is.
Speaker 1: That's a whole different thing there. But our changes okay,
Speaker 1: so droll.
Speaker 3: You know, these quants they love the idea that the
Speaker 3: humans don't get involved. So like there's traditional active like
Speaker 3: the sort of fidelity active manager that you're supposed to
Speaker 3: trust with your money. They're a five star manager. They're
Speaker 3: just good at picking stocks. Like Peter Lynch, I went
Speaker 3: to the mall, I saw these kids lined up. I
Speaker 3: bought Nike. These quants think that's all like BS no,
Speaker 3: they're like.
Speaker 1: Give me the data.
Speaker 3: Yeah, yeah, and then let's get the humans that hell
Speaker 3: out of this because we're only gonna screw it up.
Speaker 1: Yeah, but it's active and I'll be on the golf
Speaker 1: course checking out at the end of the quarter.
Speaker 4: Quan, don't golf, come on?
Speaker 1: Oh yeah, no, they might ski ball. Yeah.
Speaker 2: I'm curious Kai just about performance, because it's been you
Speaker 2: launched in twenty twenty one, you're below share prices below,
Speaker 2: then went way down, and then you've had a good
Speaker 2: year so far. Like when you try and make sense
Speaker 2: of it, what's been happening?
Speaker 5: Yeah, So the way we think about the strategy is
Speaker 5: against an internal benchmark of you know, factor neutralized you know,
Speaker 5: stock stock performance, and you know, on that on that metric,
Speaker 5: like we're actually quite happy with how things that have
Speaker 5: unfolded so far. Like obviously you can't control the exact
Speaker 5: timeing of launched, and like who we'll unfold you know,
Speaker 5: in a subsequent year or two, Like we launched June
Speaker 5: twenty one right right.
Speaker 4: Before you know a lot of tech stocks sold off.
Speaker 5: We actually you know, did better than you know, a
Speaker 5: lot of innovation focused ones you might you might say,
Speaker 5: and then you know, we've also enjoyed the ride op,
Speaker 5: but again, like it's a pretty short period, so we
Speaker 5: don't want to like over index on any particular regime
Speaker 5: that we happened to have come into.
Speaker 3: I'll give them a shout. It's out performing the Value
Speaker 3: Factory TF for my shares and the SMP, although losing
Speaker 3: to growth, but if you consider yourself somewhere in between,
Speaker 3: that's I think it was up eighteen percent. But you're right,
Speaker 3: the timing is crucial with these launches. You launch right
Speaker 3: before a market downturn, it takes some take, it takes
Speaker 3: a little while to come back, but it's all about
Speaker 3: relative performance as well.
Speaker 5: Yeah, and look, we're we're in this for the long run.
Speaker 5: Like I think, just intellectually we view this as the
Speaker 5: way that you're the way forward for value investors, and
Speaker 5: so we want to have products in the market. But
Speaker 5: ultimately the this is like a long game we're playing.
Speaker 2: When you when you were working on this and like
Speaker 2: doing the back testing everything, what was the what was
Speaker 2: the thing that from a performance standpoint that really jumped
Speaker 2: out to you and we're like we're onto something here.
Speaker 5: Well, I think it's quite interesting how you know the
Speaker 5: the individual pillars of this strategy kind of interact together.
Speaker 5: You think about you know, IP is kind of the
Speaker 5: most obvious, right, it tends to be technology names. It
Speaker 5: tends to be you know, some communications media companies, and
Speaker 5: you have like your consumer brands, and you have you know,
Speaker 5: human capital tends to be very financial services oriented as
Speaker 5: well as technology, network effects, more communication. But it's just
Speaker 5: interesting they tend to be uncorrelated. They kind of play
Speaker 5: well together and you know, contribute to an overall you
Speaker 5: know basket in a nice way. Right, Like you can
Speaker 5: have a company with like really strong IP, but if
Speaker 5: they have no marketing, like that's not going to succeed
Speaker 5: and vice versa. So you kind of need, you know,
Speaker 5: the collection of all these intellgible assets to really be
Speaker 5: to really thrive in the modern day.
Speaker 7: Sure, very very logical. One thing I wanted to ask
Speaker 7: you real fast.
Speaker 6: Uh, you know this kind of goes with you know,
Speaker 6: is intangible value a different factor or how's it interact
Speaker 6: with other factors.
Speaker 7: You have a great quote, I'm just going to quote you.
Speaker 6: You say, well, the quality factor seeks firms that are
Speaker 6: profitable today. In tangible value seeks firms that are profitable
Speaker 6: tomorrow and you have this fantastic graph that shows you
Speaker 6: know that, I believe it's like the difference in ROE
Speaker 6: is predicted by your intangible value factors. So can you
Speaker 6: talk about some of like the interactions there and and
Speaker 6: how how that relationship is is possible.
Speaker 4: Yeah, So if you step back, like what is what
Speaker 4: is quality today? It's what is the modern moat?
Speaker 5: It's an intangible asset, Like why can you know, no,
Speaker 5: don't notice charge so much money for Wigovi? Right, it's
Speaker 5: because they have a patent. Why can Urmas or LBMA
Speaker 5: charge so much for their handbags? Because they have these
Speaker 5: really strong like brands that they've built. But how do
Speaker 5: you actually get those things? They don't come for free.
Speaker 5: You have to invest upfront in eventually getting those assets.
Speaker 4: So you know, what is.
Speaker 5: Profitably what is quality is looking for companies with those
Speaker 5: moats today, right, But oftentimes the problem being that those
Speaker 5: things already priced by the market because it's pretty obvious.
Speaker 5: Whereas what's interesting about intangible value is you know, we're
Speaker 5: looking for names that are kind of making the investments
Speaker 5: today in advertising or in R and.
Speaker 4: D that will hopefully lead to that sort.
Speaker 5: Of moat down the line and hence the U the
Speaker 5: Roe upgrade that that comes in line with that, which
Speaker 5: is why, which is quite interesting, and I'm surprised to
Speaker 5: find this that the correlation between the quality factor and
Speaker 5: the intangible value factor or also zero. So it's not
Speaker 5: just with traditional value and intangible value, it's also intangible
Speaker 5: value with quality, which makes sense, and it kind of
Speaker 5: you as you think more about it, and kind of
Speaker 5: justifies why, you know, in a portfolio context, you'd want
Speaker 5: to have it slotted in there alongside the other you know,
Speaker 5: more traditional.
Speaker 7: So it was like for looking profitability factor exactly.
Speaker 4: Yeah, it's quality of the future.
Speaker 7: Very cool, very cool.
Speaker 2: Okay, So in the intro, Eric teased that you had
Speaker 2: this conversation with Cliff Fastness. I'm curious what did you
Speaker 2: What did you say that set him off?
Speaker 5: So, so, first of all, I have a ton of
Speaker 5: respect for Cliff and for AQR.
Speaker 4: He is a legend.
Speaker 5: So but basically the discussion was this, right, which was Cliff,
Speaker 5: you know, made the argument that the spread between the
Speaker 5: basket of stocks that are value stocks as opposed to
Speaker 5: growth stocks so expensive price to book or kind of
Speaker 5: a generational wides and then as a result of that,
Speaker 5: he said, therefore we should expect outperformance of value stocks
Speaker 5: relative to growth stocks. It was kind of a two
Speaker 5: phase argument, and he did a lot of really interesting
Speaker 5: robustness checks to like adjust for various factors, like excluding
Speaker 5: the magnificent seven, like things like that. You know, my
Speaker 5: you know, my argument was kind of twofold. So first
Speaker 5: I said, well, you know, on the definition of value, right,
Speaker 5: this goes back to your dark matter point, which is,
Speaker 5: you know, a lot of the phenomena we've seen in
Speaker 5: the world can be explained by this by intangible assets.
Speaker 5: So for example, the fact that the US has help
Speaker 5: performed international stocks, well, the US has invested in more
Speaker 5: intangible assets. We have the best universities, we have the
Speaker 5: best global brands, we have you know, so on and
Speaker 5: so forth. That kind of makes sense, right, It explains
Speaker 5: just the general absolute overvaluation of the market on traditional metrics. Well,
Speaker 5: if you don't adjust for all the investment we've made
Speaker 5: in these intangible assets, then yeah, of course the markets
Speaker 5: always going to seem expensive. And so I basically use
Speaker 5: that line of reasoning, you know, with some data of course,
Speaker 5: to kind of show that, Yeah, when you adjust, I
Speaker 5: think what Cliff showed was that the spread between value
Speaker 5: and growth stocks, you know, just headline number was like
Speaker 5: a two standard deviation, like really wide number. But once
Speaker 5: what I show was that once you adjust for intangible,
Speaker 5: it comes down just still being expensive, but maybe that
Speaker 5: point five so within the range of noise. And that
Speaker 5: was kind of the second point, which was, you know,
Speaker 5: Cliff was arguing that you know, a widespread should mean
Speaker 5: you know, high perspective returns, and you know, I actually
Speaker 5: looked at one of the papers that he wrote, Cliff
Speaker 5: and his co authors a few years ago, where we
Speaker 5: actually showed that, you know, yes, at extremes it matters,
Speaker 5: but really within this middle band it's kind of not statistically.
Speaker 4: You know, meaningful.
Speaker 5: Right, So the conclusion being that, all right, well it's
Speaker 5: not that wide, then, you know, should we be really
Speaker 5: kind of pounding the table today?
Speaker 3: This is fascinating because what quants do is they take
Speaker 3: what's work for active where they found alpha, and they
Speaker 3: turn it into beta. So like values said, oh, over
Speaker 3: the years, this person just outperformed because they just went
Speaker 3: to cheap stocks. So they're like, oh, we'll just make
Speaker 3: an index out of that. Bam, now that's done. They
Speaker 3: did it with quality they did it with we'll say
Speaker 3: momentum they did it with size. Intentional value does seem
Speaker 3: like that latest thing, like what have the people been
Speaker 3: leaning on to get that out performance in mojo? Like
Speaker 3: how do you explain the cues being the S and
Speaker 3: P all the time you take intangible value, it probably
Speaker 3: does go in line a little more and explain it.
Speaker 3: It makes you think, if this is a true factor
Speaker 3: and you've now captured it and turn it into beta,
Speaker 3: is there any alpha left?
Speaker 1: What else can you do?
Speaker 4: There's always going to be more alpha out there.
Speaker 5: Look, I mean, what we're trying to do is, as
Speaker 5: you point out, just like trying to capture what is
Speaker 5: it that a smart invester would do, like a smart
Speaker 5: fundamental guy at like a top edge fund, what sorts
Speaker 5: of things where they look at when they evaluate a
Speaker 5: copy of like Disney or in the video or these
Speaker 5: are just like kind of common sense things that to
Speaker 5: the extent where we can use AI, we can use
Speaker 5: all the new data available to make it into beta,
Speaker 5: to make it into a systematic factor. That's good, But
Speaker 5: then you know, the the smart guys, once it is
Speaker 5: table stakes will find the next thing to lean on,
Speaker 5: right and I have.
Speaker 1: What's the next thing?
Speaker 4: I don't know. I mean if I knew, then you
Speaker 4: know it would.
Speaker 1: Be you wouldn't be here. Yeah, exactly whatever, yea, exactly.
Speaker 2: All right, we're gonna leave it there.
Speaker 7: Kai.
Speaker 2: One final question. Uh, it's a question we ask everyone
Speaker 2: on the on the program. Uh, what is your favorite
Speaker 2: ETF ticker other than your own?
Speaker 4: Oh?
Speaker 1: I know what he's gonna pick. I just know, go ahead.
Speaker 5: Well, I don't think I don't I don't think what
Speaker 5: my opinion is matters. I think what matters is what
Speaker 5: the market would say, and the market would say, M
Speaker 5: E T A meadow It's like an eight figure ticker.
Speaker 3: Right, answered, like a true quant Well, meta is the
Speaker 3: is the ticker that was sold to Martin. So yeah,
Speaker 3: you're right, that is the most valuable ticker.
Speaker 4: Right.
Speaker 1: So I don't know why will hersh she is still
Speaker 1: working around him. I don't understand that.
Speaker 3: Yeah, that was that was talking about a guy whohould
Speaker 3: be on an island somewhere. Yeah, your that's a very
Speaker 3: smart answer, by the way.
Speaker 4: So he So here's my thing.
Speaker 5: If if Tim Cook wants a rebrand Apple as Itan, Yeah.
Speaker 2: All right, Uh, Kai, Chris, thanks for joining us in trillion.
Speaker 2: Thank you, Thank you, thanks for listening to Trillions. Until
Speaker 2: next time. You can find us on the Bloomberg Terminal,
Speaker 2: Bloomberg dot com, Apple Podcast, Spotify, or wherever else you'd
Speaker 2: like to listen. We'd love to hear from you. We're
Speaker 2: on Twitter, I'm at Joel Webber Show. He's at Eric Balcuna's.
Speaker 2: This episode of Trillions was produced by Magnus Hendrickson. Bye