November 23, 2023 • 34 mins
When it comes to evaluating companies for different exchange-traded fund strategies, some things can be easily measured—such as dividends and price-to-earnings ratios. Others are harder to gauge. This so-called dark matter of the stock market universe includes thinks like a company's brand power, human capital and intellectual property.
The latter is something called “intangible value.” And now there’s an ETF for that, too.
On this episode, Joel and Eric speak with Kai Wu, founder and chief investment officer of Sparkline Capital, the issuer behind the Sparkline Intangible Value ETF (or ITAN), as well as Chis Cain, a quant analyst with Bloomberg Intelligence. Topics discussed include defining intangible value, how it explains the relentless returns of the Super 7 stocks and how much of it is being captures by popular ETFs.
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Episode Transcript
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Speaker 1 (00:06):
Welcomer trains. I'm Joel Webber and I'm Eric Belchunas. Eric,
this was your idea.
Speaker 2 (00:15):
I'm really curious about where this conversation goes today because
all of this stuff was like candy as I've prepared
for it.
Speaker 3 (00:21):
Yeah, you know, we're getting back to the basics today.
You know, how do you value a stock?
Speaker 2 (00:26):
Now?
Speaker 3 (00:26):
That's his stock conversation. But stocks make up index funds,
which make up ETFs, and a big wing of ETFs
are called smart beta, which are ETFs that sort of
use metrics that an active manager would use, price to earnings, price,
the book, dividends, et cetera. And within there there's all
kinds of variations. So in a way it's active even
though it uses an index. And so there's an ETF
(00:48):
that launched recently called the Sparkline Intangible Value ETF. I
TAN is the ticker, and this uses the intangible value,
which this person claims is a new factor. The reason
to call my attention is I was at the Democratized
quant event I don't know six months ago, which Wes
Gray from alf Architect puts on, and I saw this
(01:09):
guy Kai Wu debate Cliff Asnas who is like giant
in the quant world. I mean, he's like heavyweight, a lister, right,
and Kai has this ETF. It's indy and it was
sort of like a David and Goliath debate, no offense,
but Cliff had met his match here. I thought Kai
made some very good arguments. I was more on his
side by the end of the debate. Cliff was a
(01:30):
good sport. It was a great discussion. I love the quants.
They do a very academic type of rigorous debate when
they have events, and I like that. It was two
sides presented, and I thought we got to get this
guy on because not only is intangible value interesting and
people should know what that means. But when you think
of smart baita ETFs, like a value ETF, many of
them use price the book. Well what does that mean?
What is book value? Well, a lot of the book
(01:52):
values are old. They don't use things like the brand.
They use like how much actual literal capital goods the
company owns, and so they don't use real estate. So
there's this huge debate in the quant world on how
to actually define price the book, and that is a
major pillar of how you define value. So if you're
shopping for a value etf or value manager. This stuff
(02:13):
is important to know.
Speaker 2 (02:14):
Joining us Kai Wu who's the founder chief investment officer
of Sparkline Capital, as well as Chris Kane, who's a
quant analyst at Bloomberg Intelligence, this time on Trillions the intangibles, Kai, Chris,
Welcome to trillions.
Speaker 4 (02:32):
Thanks for having me, Thanks for having me.
Speaker 1 (02:35):
Okay, Kai, what is intangible value?
Speaker 5 (02:38):
So an intangible asset is, you know, as Eric was saying,
anything that's not your kind of factories, your cash, your property.
It's at Sparkline we have four pillars of intangibles. We
talk about intellectual property, brand equity, human capital, and network effects.
So again IP, human capital, brand, network effects, and these
assets are becoming more and more important for companies today.
Speaker 1 (03:01):
Why is value investing not capturing this?
Speaker 5 (03:05):
So when you think about value investing, it was really
popularized in the nineteen thirties with bang Gram's Security Analysis book.
And you go back to the thirties, right, the economy
was fully industrial. The big companies were railroads and textile mills,
and as a result, you know, these sorts of intangils
didn't really matter too much. But today the biggest companies
are you know, Apple, Facebook, firms for whom their book
(03:26):
value does not actually is not actually.
Speaker 4 (03:28):
Required to produce earnings.
Speaker 5 (03:30):
And so you know, think about you know Apple for example, right,
it's their brand, it's their the network effects around the
iPhone iOS ecosystem, it's the human capital and IP around
you know, their internal processors and such that allow them
to earn such fat profit margins.
Speaker 2 (03:43):
Why hadn't this been captured in an ETF until yours?
Speaker 4 (03:48):
You know, I don't know.
Speaker 5 (03:50):
There have been attempts to to kind of capture some
of the intangible assets using accounting based metrics. So what
the weird anomalies within the way accounting works that gap
accounting is that they allow for the capitalization of physical
capex but not intangial assets. Right, so there are they
(04:11):
have been attempts to reverse that by saying, all right,
you're gonna spend one hundred million dollars building a building.
Speaker 4 (04:16):
A factory that gets capitalized.
Speaker 5 (04:17):
You're gonna spende hundred million dollars doing R and D
to develop a patent that gets capitalized, and doing so
you can kind of create a more holistic version of
book value is a little bit better. But what we
found here at sparkline is that you know that only
takes you so far, because again, like the link between
the money you put into R and D and what
you get out is super wide. So what we like
to do here is to focus instead on the actual products,
(04:40):
the actual outputs. You know, what patents do you actually get,
how strong is the brand you actually build through your
advertising efforts? And I think that's kind of a novel
and unique approach that really only became available with the
advent of you know, instructured data and natural language processing,
which we'll get into in a bit.
Speaker 1 (04:54):
So it is your is your fund.
Speaker 3 (04:57):
It isn't a value ETF that actually just uses this
one tweaks this one part. It's more of let's go
after the companies with the highest in tangible value. Again,
that's different then let's do a traditional value ETF. But
let's correct how we define price the book, right yours
is let's go after these stocks that are high and
intangible value, right yeah.
Speaker 5 (05:16):
And I think one important thing to mention is that
it's not like we're going to go after the companies
with simply the most total overall innovative patents, let's say,
because then that just map do large cap names. Right,
what we carry about is how much as a shareholder
you get per dollar invested. Right, So it's very similar
like a dividend yield or like an earning yield. So
for each dollar invest how many you know PhDs do
I get? As an investor? How many you know Twitter
(05:38):
followers do I get? And so these are kind of
proxies for intangible assets. But again the key just being
that they're price based, very similar to price to book,
but you know, using kind of a more expansive set
of variables.
Speaker 3 (05:50):
And let's bring in Chris Kane here, because Chris spends
all day looking at this quantitative data he has builds indices,
And I was curious, Chris, you know, sort of your
take on this. And you have to have a price
to book in your metrics and how you define that,
and I'm just curious to get your you know, how
you've considered this for your work.
Speaker 6 (06:11):
Sure, I mean, I you know, I love this concept.
You know when I go to you know, customers and
speak to them about value investing, you know a lot
of the feedback I get it as well. These are
old companies. This is an old way to do it.
You know, this is kind of like anti innovation, and
you know, I don't have to tell everyone, you know
that we're kind of living through a gold native innovation
in many ways. You look at the cues, you look
at r KK, et cetera. So you know, do you
(06:35):
do you?
Speaker 7 (06:36):
But what really always helped me back.
Speaker 6 (06:38):
From those type of funds is like they're anti factor, right,
they're very high vaulved, they're very expensive. So do you
view you know, your your fund more like a value
tilt or you know, an innovation tilt, but without those
like bad factor waitings.
Speaker 5 (06:53):
Yeah, I think that's a very fair way of characterizing
the strategy, right, It's it's in an innovation fund without
the kind of baggage, where as an investor, you don't
have to sacrifice your value exposure or your quality exposure
by going into it.
Speaker 7 (07:07):
Yeah, so interesting.
Speaker 6 (07:08):
So would you consider this like, I mean, would you
consider more like a growth fund or like a traditional
value fund, or would you consider it completely different and
separate in the stins?
Speaker 4 (07:17):
Yeah?
Speaker 5 (07:17):
Look, I mean I don't love the whole value versus
growth economy. I don't think it's it's fair to say
you have to be either one or the other. You know,
Warren Buffett has talked about this as well, as you know,
this is kind of being a false construction. The way
I would think about it is a traditional value ETF. Right,
what are they trying to do. They're trying to look
for stocks with low price to book ratios. In other words,
book value is a proxy for tangible capital. So they're
(07:39):
going to look within the tangible economy, the old economy
as you point out, industrials, banks, energy materials, and find
the cheapest names, which is a totally valid thing to do.
But you know, obviously, you know, as we move forward
in time with innovation, AI, et cetera, this is becoming
a vanishingly small part of the stock market. So what
we're trying to do with the intangible value ETF is
the same exact thing. We're looking for cheap stocks, but
(07:59):
relive not to tangible but intangible capital, which ends up
mapping to consumer brands to you know, tech platforms, you
know life sciences companies, and you know services businesses. So
it's kind of the same concept, but apply to the
other half, so to speak, of the stock market.
Speaker 3 (08:22):
So is what you're saying part of the reason that
traditional value investing just sort of gets punched in the
face all the time and just lags for with fifteen
years at this point. I had a nice little run
in twenty twenty two, I believe, but now it's kind
of back in the gutter. Is that why traditional value
doesn't ever seem to have like a true regime takeover.
(08:46):
And at the same time, every time you think something
is coming back, like small caps or international, the CUES
just wakes up and says, uh uh, I'm going to
run over you.
Speaker 1 (08:54):
Again and again, run away and again.
Speaker 3 (08:56):
Like Marshall Lynch when we was talking about running people over,
He's like, I'm to smash you in the mouth again
and again and again and again and then you finally
am gonna run over you and then like you just
talk about how we scores touch downs anyway, piece mode.
The Cues is in constant bast mode mode. But is
that is intangible value? The reason that that phenomenon exists
again and again.
Speaker 5 (09:16):
Yeah, We've actually done some analysis on both the CUES
and on ARKK and what we did was we said,
let's look at a factory based framework, right, think about
the Fama French model, which is, you know, there's the market,
there's a small cap there's value so on and so forth.
And we added a sixth factor, which is the intangible
value factor. And we looked at the holdings of both
of these funds and then decompose the return, say, can
we retrospectively explain its performance by allocating to the six
(09:40):
factors and then idiosyncretic risk their alpha right, And what
was quite interesting was both of these funds actually had
a very positive loading on intangible value and in fact,
a lot of their outperformance relative to Yes and people
one hundred the traditional stock market has been due to this,
you know, this this exposure to innovative companies. That being said,
there's also a lot of volatility around that, as you
(10:01):
point out, Chris, due to say, exposure to you know,
cheap press to bookstocks, which you know did really well
and then did really poorly, and you know kind of
cycles in these really wide gyrations. And also, you know,
especially in the case of the ar KAK, the exposures
earlier stage unprofitable tech companies has been you know, kind
of a negative contributor to their returns. Just given that
(10:23):
quality as a factor has just done so well the
past two decades.
Speaker 2 (10:26):
Curious where the idea for the for for it came
from was did you have the idea for the ETF
or did you see a company? And we're like, that
is the poster child for intangible value. I'm going to
build a product around it.
Speaker 5 (10:38):
Well kind of both, right, I mean you look at
the stock market, you look at companies like you know,
McDonald's or Coca Cola, you know, for whom brands are obviously.
Speaker 4 (10:45):
Critical, Apple, Google.
Speaker 5 (10:47):
Right, and it just kind of makes sense that these
are the things that should matter today. And it's shocking that,
you know, the quantitative metrics that we've used for many
many years.
Speaker 4 (10:55):
Are have not really evolved to do that.
Speaker 5 (10:58):
You know, I used to work for GMO Jeremy Grantham,
who was a pioneer in developing a lot of systematic
value strategies in the seventies and eighties, and so I've
always been thinking about this this problem. And you know,
we're talking on an ETF podcast value ets or like
a multie hundred billion dollar if not trillion dollar category,
if you you know, expand that to also include active
managers hollow value strategies. So this is a huge question
(11:20):
and one which I feel like up until you know,
now you know, just hasn't really been kind of satisfactory,
literally like addressed. You know, we need more research, more
and more work to understand the valuation of these names.
Speaker 2 (11:31):
And what problem did you have to solve in order
to make this thing a reality?
Speaker 5 (11:35):
Well, this goes back to your question about like timing,
like why now you know, the big problem is that
accounting accounting statements don't really contain enough insight into intangible assets,
and so you really need to go to unstructured data
or alternative data. Right, We're lucky that we live in
an air now. It's just been exponential growth in big data.
We have everything from we use LinkedIn glassdoor, you know,
(11:58):
job postings, patents, mars, all this information you know, obviously
just by first principles contains insight into intangible value. The
challenge being that, like the information is kind of locked
in there because you can't, you know, as a quant
just take a linear aggression running over it at twenty
thousand more document and get anything meaningful out.
Speaker 4 (12:13):
It's all just noise.
Speaker 5 (12:14):
And so that's why the advent of the transformer natural
language processing. You know, we were actually talking about this
in twenty twenty. We've wrote a paper saying, you know,
the killer app of AI within investing is then natural
processing language and NLP, you know toolkit, which allows us
to take unstructured data and kind of create structured factors
which can then be used as inputs into traditional valuation models.
Speaker 1 (12:36):
You know what this reminds me of, Joe. I'm going
to go full metaphor here. Dark matter.
Speaker 3 (12:40):
You know it's out there, you just can't see it,
and it is. It kind of explains some most of
the universes comprised of U. Yes, this is why the
cues are the cues. It's this dark matter of intangible
value because I'm looking at the holdings here. You know, Amazon, Meta, Google, Cisco, Intel,
those are some of the firms driving the cues. Chris,
you know in your world again this concept of dark matter,
(13:03):
you have to correctly capture factors, track them. How do
you work this in so?
Speaker 6 (13:09):
I you know, I read the white paper and a
big fan. You know, I do view this as a
different type of factor. You know, I don't think as
you did with your six factor model. I don't think
you throw out per se traditional value as you showed
in the paper. You know the correlation between traditional value
and tangible value is pretty low. If I remember, actually
the correlation was higher to quality with intangible value. So
(13:30):
you know, to me, that's a value add I think,
you know, it's you know, the economy has changed. I
mean no one would say not right. I mean, it's
not plants anymore, it's not those tangible things. So this
is very logical. I view it as, you know, a
separate factor at least somewhat, and it can certainly add
value to a multi factor process.
Speaker 3 (13:49):
Yeah, but why why not just forget traditional value? Like
why even use old Price the Book? Why isn't the
quant world much more adjusting things for this? Because it
does explain so much, and it just seems like if
you're doing value investing using Price the Book, it's like
using like a rotary phone or something. I don't understand, Like,
(14:09):
why isn't this a bigger deal?
Speaker 5 (14:12):
You know, that's a great, great question, and I ask
myself that each day. But no, But look, we're all
as researchers kind of building on the edifice of what's
what's come before us, and you know, Fama French in
the mid nineties and Germany beforehand, you know, popularize this
idea of this book to market factor, which is important.
It's not that it doesn't matter, right to take the
converse to companies with a lot of IP, but one
(14:33):
has a huge real estate portfolio and a huge cash
hoard and the other doesn't.
Speaker 4 (14:36):
Of course, that company should be worth more than the
other one. So you don't want to not use this.
Speaker 5 (14:41):
It's just that you know, we can maybe do better
by adding additional dimensions of risk and dimensions of corporate
performance to our kind of mulo of factors.
Speaker 2 (14:50):
When you think about this and what you've created is
your model just the model, and it finds the companies
and then you just you know, balance rebalance quarterly like
a smart beta fund or are you are you putting
a little bit of finger on the scale.
Speaker 4 (15:05):
No finger on the scale.
Speaker 5 (15:06):
So I mean my involvements only as a researcher kind
of setting up the parameters the model, figuring out what
data sets to look at, and how to build the
machine learning uh, you know infrastructure, but you know it's
it's all systematic, it's all data driven, right. Every day,
you know, new information comes in about you know, employee turnover,
about you know, cultures, corporate culture increasing, decreasing, you know,
scandals in the media or all all the good stuff
(15:28):
new patents, new trademarks, and that kind of feeds into
the models and it automatically adjusts the relative rankings of stocks.
Speaker 2 (15:35):
And how big of a universe are you able to
come through right now? And where do you want to
get to?
Speaker 4 (15:39):
Well, we'll start with the where I want to get to.
Speaker 5 (15:41):
You know, I've actually just been working on a super
interesting project expanding the universe of stocks to global so
you know, effectively MSCI all country world.
Speaker 4 (15:50):
I am.
Speaker 5 (15:50):
I so like the nine thousand stocks or so right
now when you know, in terms of launching products, the
itn ETF is focused on the top one thousand largest
us ST so used larger medcap stocks. But obviously that
if it's not there's no kind of technological reason why
that was the case. We just wanted to start with
a product that you know, most people could kind of
get their heads around.
Speaker 6 (16:09):
You know, one thing I wanted to ask you it It
was more kind of like the methodology of intangible value.
You know, you don't have to share secret sauce here
or anything, but.
Speaker 7 (16:16):
You know, or feel free to or if you want to.
Speaker 1 (16:18):
Yeah, it's probably in the perspective.
Speaker 7 (16:22):
But I you know, you mentioned that you use alternative data.
Speaker 6 (16:24):
I'm guessing as higher frequency data NLP techniques to to
put some you know, context around it.
Speaker 7 (16:31):
So do you need to use alternative data?
Speaker 6 (16:34):
Could you you know, substitute more traditional like balance sheet
data or financial statement data for that? How far would
you get if you did do that? Or is the
is there really the value add the NLP and the
alternative data.
Speaker 5 (16:47):
So we use both traditional accounting based information and alternative data,
and we actually I can give you a very clear answer.
So if you look at like the performance historically of
say the MSCI, you know value index right relatively in
the markets in pretty bad. You know, as you point
out the past fifteen years. If instead you say, well,
well let's now allow the capitalization of intangible investment so
(17:07):
R and D. You know, as you kind of invest
R and D, you build up a balance sheet asset
for that and then you appreciate it over time. Same
for sales and marketing expenditures. Well you get a line
that's a little bit less bad, but still no panacea, right,
it still goes down. And then when we said well
let's start adding you know, more sources of data like
I mentioned patents, I mentioned LinkedIn, you know, to measure
each of the pillars using unstructured data. And that's when
(17:29):
the line starts to look pretty interesting. Right And if
you look at just the name, so put us out
even the historical performance, because that's just a back test.
Is the names you know, changed dramatically as you kind
of continually iterate and add more and more data sources
to a portfolio that just looks more like what it
should look like. Right Like if you if I said,
like first principles, build me a portfolio companies that are
you know, attractively valued relative to prodigious and tangibles, right
(17:51):
that that portfolio looks a lot more like the result
of having added alternative data than just making this simple
accounting based changes.
Speaker 3 (17:59):
It seems to me that you know, most people would
hear this and go, I get it. It's kind of
like tech stocks, right They they don't have a lot
of machinery lying around, they're mostly intangible value. But there
are some companies here that aren't tech. Right, So just
let's just go over how are these are intangible value?
Wells Fargo and General Electric those almost seem more traditional value.
Speaker 5 (18:21):
Right, Well, I mean ge in particularly, it's it's mainly
the brand that's kind of carrying that that company. Wells Fargo,
like many banks, has obviously a large balance sheet, but
for them, it's probably gonna be human capital. You know
that that is its main contributor. And I've actually done
this work. It's kind of quite interesting because you know,
even if you look at the website for Ian, we
do this analysis where we do a balance sheet dot composition,
(18:42):
So we take all the stocks in the portfolio and
assign it to a single pill pillar. So for example,
like a clear example would be like Nike or maybe
Harley Davidson would be clearly in brand. Right, then you
have like Pfizer or like a m D clearly in IP.
And then you know Goldman might be in might maybe
be a non financial by the human capital, right, And
when you do that, the balance sheet is you know, yes,
(19:03):
you know IP, that pillar ends up being about forty percent,
but it's closely followed by human capital, brand and then
tangible being the least important. So it is a kind
of relatively diversity portfolio across you know, a variety of
different pillars.
Speaker 2 (19:16):
Okay, so if we've got your model and it's this
heat seeking missile to find intangible value out there. How
do you weight this in a portfolio? How do you
look at Wells Fargo or Ge and go like, I'm
we're gonna uh with the exposure to them?
Speaker 3 (19:32):
Wells Fargo has a one point five percent weight and
Ge is a one percent, but Apple's a four percent?
Speaker 2 (19:37):
Yeah? Or Amazon or Meta? Like how you know if
your robots gets to do what it does? Like, how
do you decide who gets what percentage?
Speaker 7 (19:43):
Yeah?
Speaker 1 (19:44):
Look it's it's and how much does it change over time?
Speaker 4 (19:46):
So the methodology is consistent through time that does not change. Currently.
Speaker 5 (19:50):
What we're doing is there's always a trade off in
a quant world, as you know, Chris, which is you
know you have too few stocks and it ends up
beingcoming like all driven by idiosyncratic risk. Oh you have
an own you know, Twitter and and elon texts, something
weird out and then you know you're done right, Like,
so you want to have a certain amount of diversification
to protect against that, but you don't want to be
too many stocks. If you have a thousand of it,
of a thousand stocks, it's basically just the index one
(20:12):
at that point, right, So for us, we pick one
hundred and fifty as our cut off. So it's like,
you know, trying to strike a balance between being you know,
concentrated enough around this factor, but also having diversification on
the name sense. And then in terms of the waiting
amongst those stocks, there's kind of two things that drive that.
So the first is just the score, right, higher scores.
Speaker 4 (20:30):
Get more weight, that's obvious.
Speaker 5 (20:32):
The second thing we do, though, is this modified market
cap waiting, right, And again this is to deal with
a trade off. So imagine I were to create a
you know, market cap weighted version of the strategy to say,
all right, well, like Apple has ten x the market
cap of stock you know two, so therefore it gets
next to weight. Well, then you end up with like
very little breath because you know, especially these megacaps have
become so large and in the seas, it's you don't
(20:53):
have much ability to kind of over underweight. On the
other hand, if you do equal weight instead, you end
up creating this huge bias towards the factor, right, where like, yes,
you have a lot of active share, but it's all
just kind of like junk food, right, It's all just like, oh,
you know, I just have a small cap and so
you know, for better or worse, your clients are gonna
judge against the MP. And if you know, as it
has played out the past two years, right equal weighted
RSP for example, has underperformed SPY, you.
Speaker 4 (21:17):
Know you're going to look really bad.
Speaker 5 (21:18):
So we ended up doing this this middle ground where
we basically half marketapp weight the stocks so that we
can kind of like thread the needle between these two
these two challenges.
Speaker 2 (21:26):
Okay, so obviously there's a product in the one fifty,
but if you have this data, there's the other side
of the spectrum with the companies that aren't doing so
good at this. Have you thought about building a product
that combines the two.
Speaker 4 (21:38):
Yeah.
Speaker 5 (21:38):
Look, I mean we've looked at short side as well, right,
And if you look at like the so looking at
the top fifteen percent and you short the bottom fifteen percent,
that actually works well. Right Historically in back test the
short side, these things do underperform, right, So in theory
there is a product around that. Of course, like if
we're in the ETF space, it's a little challenging to
do long short, especially on single names, because it's transparent
(21:58):
and people can kind of pick you off. So that
hasn't been our starting points. But you know, I come
from an institution in a world where I used to
run you know, large hedge funds, and so that's totally
like a product that could be available to the right client.
But as it turns out, most of our investor base,
they like the beta. They like you know, being you
know long stock to stocks go up over time.
Speaker 3 (22:15):
Yeah, this is really fascinating, this idea of how to
make a factor strategy, because the academics do long short
because you're trying to isolate the factor. But when you
do long short, you get a lot of offsetting, so
your volatility goes down. So it's a nice easy ride.
But it never has like a shiny object moment. It
never has like breakout performance. This is the problem with
the Jim Kramer ETF. It goes long short, and in
(22:36):
the advisor world, I think, unlike institutions, they need a
little shiny object moment. And Chris, you deal with this
all the time. You do make long short in disease,
but clearly, when you're actually trying to package some of
what you do into an ETF marketplace, decisions have to
be made.
Speaker 7 (22:53):
Sure. Yeah, I mean you know, kay, You know when
you do long only.
Speaker 6 (22:56):
Obviously you have that equity beta, and I think a
lot of advisors want that equity beta.
Speaker 7 (23:01):
You know, to me, with long short, you know your real.
Speaker 6 (23:03):
Value add there is a lower correlation, significantly lower correlation
to traditional stocks and bonds. So if you're a traditional
investor that has that already, I think that's really where
long short shines. But long only factor investing is certainly,
you know, a good approach as well.
Speaker 3 (23:19):
Also listening to Kai and going over the design of
the ETF and all these decisions that are made, I
would say you probably made twenty five decisions somewhere not
to mention all the research. So we're talking like potentially
one hundred things that you could tweak that would make
the returns different. That's why I think smart beta is active.
It's just it's just all the active is done in
(23:42):
the design. It's like you're designing an active robot. Once
you close the door and like, you know, screw in
the bolts, it's now a robot, but all of the
decisions you made before you close the door are active.
Speaker 1 (23:53):
Would you agree with that?
Speaker 3 (23:54):
Yeah, on hundred percent, Even though you don't do any
you have no more control over it.
Speaker 1 (23:58):
It's like you are too.
Speaker 4 (24:00):
Now right, Yep.
Speaker 5 (24:02):
All the active decisions is upfront in the construction of
the model. But then once you kind of finish that
process and as you say, you you know, turn the
key and you throw it away, then you know, it
kind of runs on his own.
Speaker 1 (24:13):
And quants like the fact that the way just to
clear are two D two active? Is that what you're saying?
Speaker 2 (24:18):
Very active?
Speaker 7 (24:19):
Yes?
Speaker 3 (24:19):
Not, well you heard him. He's coaching Luke and stuff.
I mean he's pretty active.
Speaker 2 (24:22):
Yeah.
Speaker 1 (24:23):
Yeah, it's not like a dishwasher. That's like like an index.
That's so there's other ones that were on the on
the rig. Yeah, I don't know what C three PO is.
That's a whole different thing there. But our changes okay,
so droll.
Speaker 3 (24:38):
You know, these quants they love the idea that the
humans don't get involved. So like there's traditional active like
the sort of fidelity active manager that you're supposed to
trust with your money. They're a five star manager. They're
just good at picking stocks. Like Peter Lynch, I went
to the mall, I saw these kids lined up. I
bought Nike. These quants think that's all like BS no,
(24:59):
they're like.
Speaker 1 (24:59):
Give me the data.
Speaker 3 (25:00):
Yeah, yeah, and then let's get the humans that hell
out of this because we're only gonna screw it up.
Speaker 1 (25:04):
Yeah, but it's active and I'll be on the golf
course checking out at the end of the quarter.
Speaker 4 (25:09):
Quan, don't golf, come on?
Speaker 1 (25:10):
Oh yeah, no, they might ski ball. Yeah.
Speaker 2 (25:21):
I'm curious Kai just about performance, because it's been you
launched in twenty twenty one, you're below share prices below,
then went way down, and then you've had a good
year so far. Like when you try and make sense
of it, what's been happening?
Speaker 5 (25:37):
Yeah, So the way we think about the strategy is
against an internal benchmark of you know, factor neutralized you know,
stock stock performance, and you know, on that on that metric,
like we're actually quite happy with how things that have
unfolded so far. Like obviously you can't control the exact
timeing of launched, and like who we'll unfold you know,
in a subsequent year or two, Like we launched June
twenty one right right.
Speaker 4 (25:57):
Before you know a lot of tech stocks sold off.
Speaker 5 (25:59):
We actually you know, did better than you know, a
lot of innovation focused ones you might you might say,
and then you know, we've also enjoyed the ride op,
but again, like it's a pretty short period, so we
don't want to like over index on any particular regime
that we happened to have come into.
Speaker 3 (26:14):
I'll give them a shout. It's out performing the Value
Factory TF for my shares and the SMP, although losing
to growth, but if you consider yourself somewhere in between,
that's I think it was up eighteen percent. But you're right,
the timing is crucial with these launches. You launch right
before a market downturn, it takes some take, it takes
a little while to come back, but it's all about
relative performance as well.
Speaker 5 (26:33):
Yeah, and look, we're we're in this for the long run.
Like I think, just intellectually we view this as the
way that you're the way forward for value investors, and
so we want to have products in the market. But
ultimately the this is like a long game we're playing.
Speaker 2 (26:44):
When you when you were working on this and like
doing the back testing everything, what was the what was
the thing that from a performance standpoint that really jumped
out to you and we're like we're onto something here.
Speaker 5 (26:53):
Well, I think it's quite interesting how you know the
the individual pillars of this strategy kind of interact together.
You think about you know, IP is kind of the
most obvious, right, it tends to be technology names. It
tends to be you know, some communications media companies, and
you have like your consumer brands, and you have you know,
human capital tends to be very financial services oriented as
well as technology, network effects, more communication. But it's just
(27:15):
interesting they tend to be uncorrelated. They kind of play
well together and you know, contribute to an overall you
know basket in a nice way. Right, Like you can
have a company with like really strong IP, but if
they have no marketing, like that's not going to succeed
and vice versa. So you kind of need, you know,
the collection of all these intellgible assets to really be
to really thrive in the modern day.
Speaker 7 (27:34):
Sure, very very logical. One thing I wanted to ask
you real fast.
Speaker 6 (27:37):
Uh, you know this kind of goes with you know,
is intangible value a different factor or how's it interact
with other factors.
Speaker 7 (27:42):
You have a great quote, I'm just going to quote you.
Speaker 6 (27:43):
You say, well, the quality factor seeks firms that are
profitable today. In tangible value seeks firms that are profitable
tomorrow and you have this fantastic graph that shows you
know that, I believe it's like the difference in ROE
is predicted by your intangible value factors. So can you
talk about some of like the interactions there and and
how how that relationship is is possible.
Speaker 4 (28:05):
Yeah, So if you step back, like what is what
is quality today? It's what is the modern moat?
Speaker 5 (28:09):
It's an intangible asset, Like why can you know, no,
don't notice charge so much money for Wigovi? Right, it's
because they have a patent. Why can Urmas or LBMA
charge so much for their handbags? Because they have these
really strong like brands that they've built. But how do
you actually get those things? They don't come for free.
You have to invest upfront in eventually getting those assets.
Speaker 4 (28:29):
So you know, what is.
Speaker 5 (28:30):
Profitably what is quality is looking for companies with those
moats today, right, But oftentimes the problem being that those
things already priced by the market because it's pretty obvious.
Whereas what's interesting about intangible value is you know, we're
looking for names that are kind of making the investments
today in advertising or in R and.
Speaker 4 (28:45):
D that will hopefully lead to that sort.
Speaker 5 (28:49):
Of moat down the line and hence the U the
Roe upgrade that that comes in line with that, which
is why, which is quite interesting, and I'm surprised to
find this that the correlation between the quality factor and
the intangible value factor or also zero. So it's not
just with traditional value and intangible value, it's also intangible
value with quality, which makes sense, and it kind of
you as you think more about it, and kind of
(29:09):
justifies why, you know, in a portfolio context, you'd want
to have it slotted in there alongside the other you know,
more traditional.
Speaker 7 (29:15):
So it was like for looking profitability factor exactly.
Speaker 4 (29:18):
Yeah, it's quality of the future.
Speaker 7 (29:19):
Very cool, very cool.
Speaker 2 (29:20):
Okay, So in the intro, Eric teased that you had
this conversation with Cliff Fastness. I'm curious what did you
What did you say that set him off?
Speaker 5 (29:32):
So, so, first of all, I have a ton of
respect for Cliff and for AQR.
Speaker 4 (29:35):
He is a legend.
Speaker 5 (29:37):
So but basically the discussion was this, right, which was Cliff,
you know, made the argument that the spread between the
basket of stocks that are value stocks as opposed to
growth stocks so expensive price to book or kind of
a generational wides and then as a result of that,
he said, therefore we should expect outperformance of value stocks
relative to growth stocks. It was kind of a two
(29:58):
phase argument, and he did a lot of really interesting
robustness checks to like adjust for various factors, like excluding
the magnificent seven, like things like that. You know, my
you know, my argument was kind of twofold. So first
I said, well, you know, on the definition of value, right,
this goes back to your dark matter point, which is,
you know, a lot of the phenomena we've seen in
(30:19):
the world can be explained by this by intangible assets.
So for example, the fact that the US has help
performed international stocks, well, the US has invested in more
intangible assets. We have the best universities, we have the
best global brands, we have you know, so on and
so forth. That kind of makes sense, right, It explains
just the general absolute overvaluation of the market on traditional metrics. Well,
if you don't adjust for all the investment we've made
(30:39):
in these intangible assets, then yeah, of course the markets
always going to seem expensive. And so I basically use
that line of reasoning, you know, with some data of course,
to kind of show that, Yeah, when you adjust, I
think what Cliff showed was that the spread between value
and growth stocks, you know, just headline number was like
a two standard deviation, like really wide number. But once
what I show was that once you adjust for intangible,
(31:00):
it comes down just still being expensive, but maybe that
point five so within the range of noise. And that
was kind of the second point, which was, you know,
Cliff was arguing that you know, a widespread should mean
you know, high perspective returns, and you know, I actually
looked at one of the papers that he wrote, Cliff
and his co authors a few years ago, where we
actually showed that, you know, yes, at extremes it matters,
but really within this middle band it's kind of not statistically.
Speaker 4 (31:23):
You know, meaningful.
Speaker 5 (31:24):
Right, So the conclusion being that, all right, well it's
not that wide, then, you know, should we be really
kind of pounding the table today?
Speaker 3 (31:30):
This is fascinating because what quants do is they take
what's work for active where they found alpha, and they
turn it into beta. So like values said, oh, over
the years, this person just outperformed because they just went
to cheap stocks. So they're like, oh, we'll just make
an index out of that. Bam, now that's done. They
did it with quality they did it with we'll say
momentum they did it with size. Intentional value does seem
(31:51):
like that latest thing, like what have the people been
leaning on to get that out performance in mojo? Like
how do you explain the cues being the S and
P all the time you take intangible value, it probably
does go in line a little more and explain it.
It makes you think, if this is a true factor
and you've now captured it and turn it into beta,
is there any alpha left?
Speaker 1 (32:12):
What else can you do?
Speaker 4 (32:13):
There's always going to be more alpha out there.
Speaker 5 (32:16):
Look, I mean, what we're trying to do is, as
you point out, just like trying to capture what is
it that a smart invester would do, like a smart
fundamental guy at like a top edge fund, what sorts
of things where they look at when they evaluate a
copy of like Disney or in the video or these
are just like kind of common sense things that to
the extent where we can use AI, we can use
all the new data available to make it into beta,
to make it into a systematic factor. That's good, But
(32:38):
then you know, the the smart guys, once it is
table stakes will find the next thing to lean on,
right and I have.
Speaker 1 (32:43):
What's the next thing?
Speaker 4 (32:44):
I don't know. I mean if I knew, then you
know it would.
Speaker 1 (32:46):
Be you wouldn't be here. Yeah, exactly whatever, yea, exactly.
Speaker 2 (32:52):
All right, we're gonna leave it there.
Speaker 7 (32:53):
Kai.
Speaker 2 (32:54):
One final question. Uh, it's a question we ask everyone
on the on the program. Uh, what is your favorite
ETF ticker other than your own?
Speaker 4 (33:01):
Oh?
Speaker 1 (33:03):
I know what he's gonna pick. I just know, go ahead.
Speaker 5 (33:06):
Well, I don't think I don't I don't think what
my opinion is matters. I think what matters is what
the market would say, and the market would say, M
E T A meadow It's like an eight figure ticker.
Speaker 3 (33:16):
Right, answered, like a true quant Well, meta is the
is the ticker that was sold to Martin. So yeah,
you're right, that is the most valuable ticker.
Speaker 4 (33:25):
Right.
Speaker 1 (33:25):
So I don't know why will hersh she is still
working around him. I don't understand that.
Speaker 3 (33:29):
Yeah, that was that was talking about a guy whohould
be on an island somewhere. Yeah, your that's a very
smart answer, by the way.
Speaker 4 (33:34):
So he So here's my thing.
Speaker 5 (33:36):
If if Tim Cook wants a rebrand Apple as Itan, Yeah.
Speaker 2 (33:43):
All right, Uh, Kai, Chris, thanks for joining us in trillion.
Thank you, Thank you, thanks for listening to Trillions. Until
next time. You can find us on the Bloomberg Terminal,
Bloomberg dot com, Apple Podcast, Spotify, or wherever else you'd
like to listen. We'd love to hear from you. We're
(34:05):
on Twitter, I'm at Joel Webber Show. He's at Eric Balcuna's.
This episode of Trillions was produced by Magnus Hendrickson. Bye
Welcomer trains. I'm Joel Webber and I'm Eric Belchunas. Eric,
this was your idea.
Speaker 2 (00:15):
I'm really curious about where this conversation goes today because
all of this stuff was like candy as I've prepared
for it.
Speaker 3 (00:21):
Yeah, you know, we're getting back to the basics today.
You know, how do you value a stock?
Speaker 2 (00:26):
Now?
Speaker 3 (00:26):
That's his stock conversation. But stocks make up index funds,
which make up ETFs, and a big wing of ETFs
are called smart beta, which are ETFs that sort of
use metrics that an active manager would use, price to earnings, price,
the book, dividends, et cetera. And within there there's all
kinds of variations. So in a way it's active even
though it uses an index. And so there's an ETF
(00:48):
that launched recently called the Sparkline Intangible Value ETF. I
TAN is the ticker, and this uses the intangible value,
which this person claims is a new factor. The reason
to call my attention is I was at the Democratized
quant event I don't know six months ago, which Wes
Gray from alf Architect puts on, and I saw this
(01:09):
guy Kai Wu debate Cliff Asnas who is like giant
in the quant world. I mean, he's like heavyweight, a lister, right,
and Kai has this ETF. It's indy and it was
sort of like a David and Goliath debate, no offense,
but Cliff had met his match here. I thought Kai
made some very good arguments. I was more on his
side by the end of the debate. Cliff was a
(01:30):
good sport. It was a great discussion. I love the quants.
They do a very academic type of rigorous debate when
they have events, and I like that. It was two
sides presented, and I thought we got to get this
guy on because not only is intangible value interesting and
people should know what that means. But when you think
of smart baita ETFs, like a value ETF, many of
them use price the book. Well what does that mean?
What is book value? Well, a lot of the book
(01:52):
values are old. They don't use things like the brand.
They use like how much actual literal capital goods the
company owns, and so they don't use real estate. So
there's this huge debate in the quant world on how
to actually define price the book, and that is a
major pillar of how you define value. So if you're
shopping for a value etf or value manager. This stuff
(02:13):
is important to know.
Speaker 2 (02:14):
Joining us Kai Wu who's the founder chief investment officer
of Sparkline Capital, as well as Chris Kane, who's a
quant analyst at Bloomberg Intelligence, this time on Trillions the intangibles, Kai, Chris,
Welcome to trillions.
Speaker 4 (02:32):
Thanks for having me, Thanks for having me.
Speaker 1 (02:35):
Okay, Kai, what is intangible value?
Speaker 5 (02:38):
So an intangible asset is, you know, as Eric was saying,
anything that's not your kind of factories, your cash, your property.
It's at Sparkline we have four pillars of intangibles. We
talk about intellectual property, brand equity, human capital, and network effects.
So again IP, human capital, brand, network effects, and these
assets are becoming more and more important for companies today.
Speaker 1 (03:01):
Why is value investing not capturing this?
Speaker 5 (03:05):
So when you think about value investing, it was really
popularized in the nineteen thirties with bang Gram's Security Analysis book.
And you go back to the thirties, right, the economy
was fully industrial. The big companies were railroads and textile mills,
and as a result, you know, these sorts of intangils
didn't really matter too much. But today the biggest companies
are you know, Apple, Facebook, firms for whom their book
(03:26):
value does not actually is not actually.
Speaker 4 (03:28):
Required to produce earnings.
Speaker 5 (03:30):
And so you know, think about you know Apple for example, right,
it's their brand, it's their the network effects around the
iPhone iOS ecosystem, it's the human capital and IP around
you know, their internal processors and such that allow them
to earn such fat profit margins.
Speaker 2 (03:43):
Why hadn't this been captured in an ETF until yours?
Speaker 4 (03:48):
You know, I don't know.
Speaker 5 (03:50):
There have been attempts to to kind of capture some
of the intangible assets using accounting based metrics. So what
the weird anomalies within the way accounting works that gap
accounting is that they allow for the capitalization of physical
capex but not intangial assets. Right, so there are they
(04:11):
have been attempts to reverse that by saying, all right,
you're gonna spend one hundred million dollars building a building.
Speaker 4 (04:16):
A factory that gets capitalized.
Speaker 5 (04:17):
You're gonna spende hundred million dollars doing R and D
to develop a patent that gets capitalized, and doing so
you can kind of create a more holistic version of
book value is a little bit better. But what we
found here at sparkline is that you know that only
takes you so far, because again, like the link between
the money you put into R and D and what
you get out is super wide. So what we like
to do here is to focus instead on the actual products,
(04:40):
the actual outputs. You know, what patents do you actually get,
how strong is the brand you actually build through your
advertising efforts? And I think that's kind of a novel
and unique approach that really only became available with the
advent of you know, instructured data and natural language processing,
which we'll get into in a bit.
Speaker 1 (04:54):
So it is your is your fund.
Speaker 3 (04:57):
It isn't a value ETF that actually just uses this
one tweaks this one part. It's more of let's go
after the companies with the highest in tangible value. Again,
that's different then let's do a traditional value ETF. But
let's correct how we define price the book, right yours
is let's go after these stocks that are high and
intangible value, right yeah.
Speaker 5 (05:16):
And I think one important thing to mention is that
it's not like we're going to go after the companies
with simply the most total overall innovative patents, let's say,
because then that just map do large cap names. Right,
what we carry about is how much as a shareholder
you get per dollar invested. Right, So it's very similar
like a dividend yield or like an earning yield. So
for each dollar invest how many you know PhDs do
I get? As an investor? How many you know Twitter
(05:38):
followers do I get? And so these are kind of
proxies for intangible assets. But again the key just being
that they're price based, very similar to price to book,
but you know, using kind of a more expansive set
of variables.
Speaker 3 (05:50):
And let's bring in Chris Kane here, because Chris spends
all day looking at this quantitative data he has builds indices,
And I was curious, Chris, you know, sort of your
take on this. And you have to have a price
to book in your metrics and how you define that,
and I'm just curious to get your you know, how
you've considered this for your work.
Speaker 6 (06:11):
Sure, I mean, I you know, I love this concept.
You know when I go to you know, customers and
speak to them about value investing, you know a lot
of the feedback I get it as well. These are
old companies. This is an old way to do it.
You know, this is kind of like anti innovation, and
you know, I don't have to tell everyone, you know
that we're kind of living through a gold native innovation
in many ways. You look at the cues, you look
at r KK, et cetera. So you know, do you
(06:35):
do you?
Speaker 7 (06:36):
But what really always helped me back.
Speaker 6 (06:38):
From those type of funds is like they're anti factor, right,
they're very high vaulved, they're very expensive. So do you
view you know, your your fund more like a value
tilt or you know, an innovation tilt, but without those
like bad factor waitings.
Speaker 5 (06:53):
Yeah, I think that's a very fair way of characterizing
the strategy, right, It's it's in an innovation fund without
the kind of baggage, where as an investor, you don't
have to sacrifice your value exposure or your quality exposure
by going into it.
Speaker 7 (07:07):
Yeah, so interesting.
Speaker 6 (07:08):
So would you consider this like, I mean, would you
consider more like a growth fund or like a traditional
value fund, or would you consider it completely different and
separate in the stins?
Speaker 4 (07:17):
Yeah?
Speaker 5 (07:17):
Look, I mean I don't love the whole value versus
growth economy. I don't think it's it's fair to say
you have to be either one or the other. You know,
Warren Buffett has talked about this as well, as you know,
this is kind of being a false construction. The way
I would think about it is a traditional value ETF. Right,
what are they trying to do. They're trying to look
for stocks with low price to book ratios. In other words,
book value is a proxy for tangible capital. So they're
(07:39):
going to look within the tangible economy, the old economy
as you point out, industrials, banks, energy materials, and find
the cheapest names, which is a totally valid thing to do.
But you know, obviously, you know, as we move forward
in time with innovation, AI, et cetera, this is becoming
a vanishingly small part of the stock market. So what
we're trying to do with the intangible value ETF is
the same exact thing. We're looking for cheap stocks, but
(07:59):
relive not to tangible but intangible capital, which ends up
mapping to consumer brands to you know, tech platforms, you
know life sciences companies, and you know services businesses. So
it's kind of the same concept, but apply to the
other half, so to speak, of the stock market.
Speaker 3 (08:22):
So is what you're saying part of the reason that
traditional value investing just sort of gets punched in the
face all the time and just lags for with fifteen
years at this point. I had a nice little run
in twenty twenty two, I believe, but now it's kind
of back in the gutter. Is that why traditional value
doesn't ever seem to have like a true regime takeover.
(08:46):
And at the same time, every time you think something
is coming back, like small caps or international, the CUES
just wakes up and says, uh uh, I'm going to
run over you.
Speaker 1 (08:54):
Again and again, run away and again.
Speaker 3 (08:56):
Like Marshall Lynch when we was talking about running people over,
He's like, I'm to smash you in the mouth again
and again and again and again and then you finally
am gonna run over you and then like you just
talk about how we scores touch downs anyway, piece mode.
The Cues is in constant bast mode mode. But is
that is intangible value? The reason that that phenomenon exists
again and again.
Speaker 5 (09:16):
Yeah, We've actually done some analysis on both the CUES
and on ARKK and what we did was we said,
let's look at a factory based framework, right, think about
the Fama French model, which is, you know, there's the market,
there's a small cap there's value so on and so forth.
And we added a sixth factor, which is the intangible
value factor. And we looked at the holdings of both
of these funds and then decompose the return, say, can
we retrospectively explain its performance by allocating to the six
(09:40):
factors and then idiosyncretic risk their alpha right, And what
was quite interesting was both of these funds actually had
a very positive loading on intangible value and in fact,
a lot of their outperformance relative to Yes and people
one hundred the traditional stock market has been due to this,
you know, this this exposure to innovative companies. That being said,
there's also a lot of volatility around that, as you
(10:01):
point out, Chris, due to say, exposure to you know,
cheap press to bookstocks, which you know did really well
and then did really poorly, and you know kind of
cycles in these really wide gyrations. And also, you know,
especially in the case of the ar KAK, the exposures
earlier stage unprofitable tech companies has been you know, kind
of a negative contributor to their returns. Just given that
(10:23):
quality as a factor has just done so well the
past two decades.
Speaker 2 (10:26):
Curious where the idea for the for for it came
from was did you have the idea for the ETF
or did you see a company? And we're like, that
is the poster child for intangible value. I'm going to
build a product around it.
Speaker 5 (10:38):
Well kind of both, right, I mean you look at
the stock market, you look at companies like you know,
McDonald's or Coca Cola, you know, for whom brands are obviously.
Speaker 4 (10:45):
Critical, Apple, Google.
Speaker 5 (10:47):
Right, and it just kind of makes sense that these
are the things that should matter today. And it's shocking that,
you know, the quantitative metrics that we've used for many
many years.
Speaker 4 (10:55):
Are have not really evolved to do that.
Speaker 5 (10:58):
You know, I used to work for GMO Jeremy Grantham,
who was a pioneer in developing a lot of systematic
value strategies in the seventies and eighties, and so I've
always been thinking about this this problem. And you know,
we're talking on an ETF podcast value ets or like
a multie hundred billion dollar if not trillion dollar category,
if you you know, expand that to also include active
managers hollow value strategies. So this is a huge question
(11:20):
and one which I feel like up until you know,
now you know, just hasn't really been kind of satisfactory,
literally like addressed. You know, we need more research, more
and more work to understand the valuation of these names.
Speaker 2 (11:31):
And what problem did you have to solve in order
to make this thing a reality?
Speaker 5 (11:35):
Well, this goes back to your question about like timing,
like why now you know, the big problem is that
accounting accounting statements don't really contain enough insight into intangible assets,
and so you really need to go to unstructured data
or alternative data. Right, We're lucky that we live in
an air now. It's just been exponential growth in big data.
We have everything from we use LinkedIn glassdoor, you know,
(11:58):
job postings, patents, mars, all this information you know, obviously
just by first principles contains insight into intangible value. The
challenge being that, like the information is kind of locked
in there because you can't, you know, as a quant
just take a linear aggression running over it at twenty
thousand more document and get anything meaningful out.
Speaker 4 (12:13):
It's all just noise.
Speaker 5 (12:14):
And so that's why the advent of the transformer natural
language processing. You know, we were actually talking about this
in twenty twenty. We've wrote a paper saying, you know,
the killer app of AI within investing is then natural
processing language and NLP, you know toolkit, which allows us
to take unstructured data and kind of create structured factors
which can then be used as inputs into traditional valuation models.
Speaker 1 (12:36):
You know what this reminds me of, Joe. I'm going
to go full metaphor here. Dark matter.
Speaker 3 (12:40):
You know it's out there, you just can't see it,
and it is. It kind of explains some most of
the universes comprised of U. Yes, this is why the
cues are the cues. It's this dark matter of intangible
value because I'm looking at the holdings here. You know, Amazon, Meta, Google, Cisco, Intel,
those are some of the firms driving the cues. Chris,
you know in your world again this concept of dark matter,
(13:03):
you have to correctly capture factors, track them. How do
you work this in so?
Speaker 6 (13:09):
I you know, I read the white paper and a
big fan. You know, I do view this as a
different type of factor. You know, I don't think as
you did with your six factor model. I don't think
you throw out per se traditional value as you showed
in the paper. You know the correlation between traditional value
and tangible value is pretty low. If I remember, actually
the correlation was higher to quality with intangible value. So
(13:30):
you know, to me, that's a value add I think,
you know, it's you know, the economy has changed. I
mean no one would say not right. I mean, it's
not plants anymore, it's not those tangible things. So this
is very logical. I view it as, you know, a
separate factor at least somewhat, and it can certainly add
value to a multi factor process.
Speaker 3 (13:49):
Yeah, but why why not just forget traditional value? Like
why even use old Price the Book? Why isn't the
quant world much more adjusting things for this? Because it
does explain so much, and it just seems like if
you're doing value investing using Price the Book, it's like
using like a rotary phone or something. I don't understand, Like,
(14:09):
why isn't this a bigger deal?
Speaker 5 (14:12):
You know, that's a great, great question, and I ask
myself that each day. But no, But look, we're all
as researchers kind of building on the edifice of what's
what's come before us, and you know, Fama French in
the mid nineties and Germany beforehand, you know, popularize this
idea of this book to market factor, which is important.
It's not that it doesn't matter, right to take the
converse to companies with a lot of IP, but one
(14:33):
has a huge real estate portfolio and a huge cash
hoard and the other doesn't.
Speaker 4 (14:36):
Of course, that company should be worth more than the
other one. So you don't want to not use this.
Speaker 5 (14:41):
It's just that you know, we can maybe do better
by adding additional dimensions of risk and dimensions of corporate
performance to our kind of mulo of factors.
Speaker 2 (14:50):
When you think about this and what you've created is
your model just the model, and it finds the companies
and then you just you know, balance rebalance quarterly like
a smart beta fund or are you are you putting
a little bit of finger on the scale.
Speaker 4 (15:05):
No finger on the scale.
Speaker 5 (15:06):
So I mean my involvements only as a researcher kind
of setting up the parameters the model, figuring out what
data sets to look at, and how to build the
machine learning uh, you know infrastructure, but you know it's
it's all systematic, it's all data driven, right. Every day,
you know, new information comes in about you know, employee turnover,
about you know, cultures, corporate culture increasing, decreasing, you know,
scandals in the media or all all the good stuff
(15:28):
new patents, new trademarks, and that kind of feeds into
the models and it automatically adjusts the relative rankings of stocks.
Speaker 2 (15:35):
And how big of a universe are you able to
come through right now? And where do you want to
get to?
Speaker 4 (15:39):
Well, we'll start with the where I want to get to.
Speaker 5 (15:41):
You know, I've actually just been working on a super
interesting project expanding the universe of stocks to global so
you know, effectively MSCI all country world.
Speaker 4 (15:50):
I am.
Speaker 5 (15:50):
I so like the nine thousand stocks or so right
now when you know, in terms of launching products, the
itn ETF is focused on the top one thousand largest
us ST so used larger medcap stocks. But obviously that
if it's not there's no kind of technological reason why
that was the case. We just wanted to start with
a product that you know, most people could kind of
get their heads around.
Speaker 6 (16:09):
You know, one thing I wanted to ask you it It
was more kind of like the methodology of intangible value.
You know, you don't have to share secret sauce here
or anything, but.
Speaker 7 (16:16):
You know, or feel free to or if you want to.
Speaker 1 (16:18):
Yeah, it's probably in the perspective.
Speaker 7 (16:22):
But I you know, you mentioned that you use alternative data.
Speaker 6 (16:24):
I'm guessing as higher frequency data NLP techniques to to
put some you know, context around it.
Speaker 7 (16:31):
So do you need to use alternative data?
Speaker 6 (16:34):
Could you you know, substitute more traditional like balance sheet
data or financial statement data for that? How far would
you get if you did do that? Or is the
is there really the value add the NLP and the
alternative data.
Speaker 5 (16:47):
So we use both traditional accounting based information and alternative data,
and we actually I can give you a very clear answer.
So if you look at like the performance historically of
say the MSCI, you know value index right relatively in
the markets in pretty bad. You know, as you point
out the past fifteen years. If instead you say, well,
well let's now allow the capitalization of intangible investment so
(17:07):
R and D. You know, as you kind of invest
R and D, you build up a balance sheet asset
for that and then you appreciate it over time. Same
for sales and marketing expenditures. Well you get a line
that's a little bit less bad, but still no panacea, right,
it still goes down. And then when we said well
let's start adding you know, more sources of data like
I mentioned patents, I mentioned LinkedIn, you know, to measure
each of the pillars using unstructured data. And that's when
(17:29):
the line starts to look pretty interesting. Right And if
you look at just the name, so put us out
even the historical performance, because that's just a back test.
Is the names you know, changed dramatically as you kind
of continually iterate and add more and more data sources
to a portfolio that just looks more like what it
should look like. Right Like if you if I said,
like first principles, build me a portfolio companies that are
you know, attractively valued relative to prodigious and tangibles, right
(17:51):
that that portfolio looks a lot more like the result
of having added alternative data than just making this simple
accounting based changes.
Speaker 3 (17:59):
It seems to me that you know, most people would
hear this and go, I get it. It's kind of
like tech stocks, right They they don't have a lot
of machinery lying around, they're mostly intangible value. But there
are some companies here that aren't tech. Right, So just
let's just go over how are these are intangible value?
Wells Fargo and General Electric those almost seem more traditional value.
Speaker 5 (18:21):
Right, Well, I mean ge in particularly, it's it's mainly
the brand that's kind of carrying that that company. Wells Fargo,
like many banks, has obviously a large balance sheet, but
for them, it's probably gonna be human capital. You know
that that is its main contributor. And I've actually done
this work. It's kind of quite interesting because you know,
even if you look at the website for Ian, we
do this analysis where we do a balance sheet dot composition,
(18:42):
So we take all the stocks in the portfolio and
assign it to a single pill pillar. So for example,
like a clear example would be like Nike or maybe
Harley Davidson would be clearly in brand. Right, then you
have like Pfizer or like a m D clearly in IP.
And then you know Goldman might be in might maybe
be a non financial by the human capital, right, And
when you do that, the balance sheet is you know, yes,
(19:03):
you know IP, that pillar ends up being about forty percent,
but it's closely followed by human capital, brand and then
tangible being the least important. So it is a kind
of relatively diversity portfolio across you know, a variety of
different pillars.
Speaker 2 (19:16):
Okay, so if we've got your model and it's this
heat seeking missile to find intangible value out there. How
do you weight this in a portfolio? How do you
look at Wells Fargo or Ge and go like, I'm
we're gonna uh with the exposure to them?
Speaker 3 (19:32):
Wells Fargo has a one point five percent weight and
Ge is a one percent, but Apple's a four percent?
Speaker 2 (19:37):
Yeah? Or Amazon or Meta? Like how you know if
your robots gets to do what it does? Like, how
do you decide who gets what percentage?
Speaker 7 (19:43):
Yeah?
Speaker 1 (19:44):
Look it's it's and how much does it change over time?
Speaker 4 (19:46):
So the methodology is consistent through time that does not change. Currently.
Speaker 5 (19:50):
What we're doing is there's always a trade off in
a quant world, as you know, Chris, which is you
know you have too few stocks and it ends up
beingcoming like all driven by idiosyncratic risk. Oh you have
an own you know, Twitter and and elon texts, something
weird out and then you know you're done right, Like,
so you want to have a certain amount of diversification
to protect against that, but you don't want to be
too many stocks. If you have a thousand of it,
of a thousand stocks, it's basically just the index one
(20:12):
at that point, right, So for us, we pick one
hundred and fifty as our cut off. So it's like,
you know, trying to strike a balance between being you know,
concentrated enough around this factor, but also having diversification on
the name sense. And then in terms of the waiting
amongst those stocks, there's kind of two things that drive that.
So the first is just the score, right, higher scores.
Speaker 4 (20:30):
Get more weight, that's obvious.
Speaker 5 (20:32):
The second thing we do, though, is this modified market
cap waiting, right, And again this is to deal with
a trade off. So imagine I were to create a
you know, market cap weighted version of the strategy to say,
all right, well, like Apple has ten x the market
cap of stock you know two, so therefore it gets
next to weight. Well, then you end up with like
very little breath because you know, especially these megacaps have
become so large and in the seas, it's you don't
(20:53):
have much ability to kind of over underweight. On the
other hand, if you do equal weight instead, you end
up creating this huge bias towards the factor, right, where like, yes,
you have a lot of active share, but it's all
just kind of like junk food, right, It's all just like, oh,
you know, I just have a small cap and so
you know, for better or worse, your clients are gonna
judge against the MP. And if you know, as it
has played out the past two years, right equal weighted
RSP for example, has underperformed SPY, you.
Speaker 4 (21:17):
Know you're going to look really bad.
Speaker 5 (21:18):
So we ended up doing this this middle ground where
we basically half marketapp weight the stocks so that we
can kind of like thread the needle between these two
these two challenges.
Speaker 2 (21:26):
Okay, so obviously there's a product in the one fifty,
but if you have this data, there's the other side
of the spectrum with the companies that aren't doing so
good at this. Have you thought about building a product
that combines the two.
Speaker 4 (21:38):
Yeah.
Speaker 5 (21:38):
Look, I mean we've looked at short side as well, right,
And if you look at like the so looking at
the top fifteen percent and you short the bottom fifteen percent,
that actually works well. Right Historically in back test the
short side, these things do underperform, right, So in theory
there is a product around that. Of course, like if
we're in the ETF space, it's a little challenging to
do long short, especially on single names, because it's transparent
(21:58):
and people can kind of pick you off. So that
hasn't been our starting points. But you know, I come
from an institution in a world where I used to
run you know, large hedge funds, and so that's totally
like a product that could be available to the right client.
But as it turns out, most of our investor base,
they like the beta. They like you know, being you
know long stock to stocks go up over time.
Speaker 3 (22:15):
Yeah, this is really fascinating, this idea of how to
make a factor strategy, because the academics do long short
because you're trying to isolate the factor. But when you
do long short, you get a lot of offsetting, so
your volatility goes down. So it's a nice easy ride.
But it never has like a shiny object moment. It
never has like breakout performance. This is the problem with
the Jim Kramer ETF. It goes long short, and in
(22:36):
the advisor world, I think, unlike institutions, they need a
little shiny object moment. And Chris, you deal with this
all the time. You do make long short in disease,
but clearly, when you're actually trying to package some of
what you do into an ETF marketplace, decisions have to
be made.
Speaker 7 (22:53):
Sure. Yeah, I mean you know, kay, You know when
you do long only.
Speaker 6 (22:56):
Obviously you have that equity beta, and I think a
lot of advisors want that equity beta.
Speaker 7 (23:01):
You know, to me, with long short, you know your real.
Speaker 6 (23:03):
Value add there is a lower correlation, significantly lower correlation
to traditional stocks and bonds. So if you're a traditional
investor that has that already, I think that's really where
long short shines. But long only factor investing is certainly,
you know, a good approach as well.
Speaker 3 (23:19):
Also listening to Kai and going over the design of
the ETF and all these decisions that are made, I
would say you probably made twenty five decisions somewhere not
to mention all the research. So we're talking like potentially
one hundred things that you could tweak that would make
the returns different. That's why I think smart beta is active.
It's just it's just all the active is done in
(23:42):
the design. It's like you're designing an active robot. Once
you close the door and like, you know, screw in
the bolts, it's now a robot, but all of the
decisions you made before you close the door are active.
Speaker 1 (23:53):
Would you agree with that?
Speaker 3 (23:54):
Yeah, on hundred percent, Even though you don't do any
you have no more control over it.
Speaker 1 (23:58):
It's like you are too.
Speaker 4 (24:00):
Now right, Yep.
Speaker 5 (24:02):
All the active decisions is upfront in the construction of
the model. But then once you kind of finish that
process and as you say, you you know, turn the
key and you throw it away, then you know, it
kind of runs on his own.
Speaker 1 (24:13):
And quants like the fact that the way just to
clear are two D two active? Is that what you're saying?
Speaker 2 (24:18):
Very active?
Speaker 7 (24:19):
Yes?
Speaker 3 (24:19):
Not, well you heard him. He's coaching Luke and stuff.
I mean he's pretty active.
Speaker 2 (24:22):
Yeah.
Speaker 1 (24:23):
Yeah, it's not like a dishwasher. That's like like an index.
That's so there's other ones that were on the on
the rig. Yeah, I don't know what C three PO is.
That's a whole different thing there. But our changes okay,
so droll.
Speaker 3 (24:38):
You know, these quants they love the idea that the
humans don't get involved. So like there's traditional active like
the sort of fidelity active manager that you're supposed to
trust with your money. They're a five star manager. They're
just good at picking stocks. Like Peter Lynch, I went
to the mall, I saw these kids lined up. I
bought Nike. These quants think that's all like BS no,
(24:59):
they're like.
Speaker 1 (24:59):
Give me the data.
Speaker 3 (25:00):
Yeah, yeah, and then let's get the humans that hell
out of this because we're only gonna screw it up.
Speaker 1 (25:04):
Yeah, but it's active and I'll be on the golf
course checking out at the end of the quarter.
Speaker 4 (25:09):
Quan, don't golf, come on?
Speaker 1 (25:10):
Oh yeah, no, they might ski ball. Yeah.
Speaker 2 (25:21):
I'm curious Kai just about performance, because it's been you
launched in twenty twenty one, you're below share prices below,
then went way down, and then you've had a good
year so far. Like when you try and make sense
of it, what's been happening?
Speaker 5 (25:37):
Yeah, So the way we think about the strategy is
against an internal benchmark of you know, factor neutralized you know,
stock stock performance, and you know, on that on that metric,
like we're actually quite happy with how things that have
unfolded so far. Like obviously you can't control the exact
timeing of launched, and like who we'll unfold you know,
in a subsequent year or two, Like we launched June
twenty one right right.
Speaker 4 (25:57):
Before you know a lot of tech stocks sold off.
Speaker 5 (25:59):
We actually you know, did better than you know, a
lot of innovation focused ones you might you might say,
and then you know, we've also enjoyed the ride op,
but again, like it's a pretty short period, so we
don't want to like over index on any particular regime
that we happened to have come into.
Speaker 3 (26:14):
I'll give them a shout. It's out performing the Value
Factory TF for my shares and the SMP, although losing
to growth, but if you consider yourself somewhere in between,
that's I think it was up eighteen percent. But you're right,
the timing is crucial with these launches. You launch right
before a market downturn, it takes some take, it takes
a little while to come back, but it's all about
relative performance as well.
Speaker 5 (26:33):
Yeah, and look, we're we're in this for the long run.
Like I think, just intellectually we view this as the
way that you're the way forward for value investors, and
so we want to have products in the market. But
ultimately the this is like a long game we're playing.
Speaker 2 (26:44):
When you when you were working on this and like
doing the back testing everything, what was the what was
the thing that from a performance standpoint that really jumped
out to you and we're like we're onto something here.
Speaker 5 (26:53):
Well, I think it's quite interesting how you know the
the individual pillars of this strategy kind of interact together.
You think about you know, IP is kind of the
most obvious, right, it tends to be technology names. It
tends to be you know, some communications media companies, and
you have like your consumer brands, and you have you know,
human capital tends to be very financial services oriented as
well as technology, network effects, more communication. But it's just
(27:15):
interesting they tend to be uncorrelated. They kind of play
well together and you know, contribute to an overall you
know basket in a nice way. Right, Like you can
have a company with like really strong IP, but if
they have no marketing, like that's not going to succeed
and vice versa. So you kind of need, you know,
the collection of all these intellgible assets to really be
to really thrive in the modern day.
Speaker 7 (27:34):
Sure, very very logical. One thing I wanted to ask
you real fast.
Speaker 6 (27:37):
Uh, you know this kind of goes with you know,
is intangible value a different factor or how's it interact
with other factors.
Speaker 7 (27:42):
You have a great quote, I'm just going to quote you.
Speaker 6 (27:43):
You say, well, the quality factor seeks firms that are
profitable today. In tangible value seeks firms that are profitable
tomorrow and you have this fantastic graph that shows you
know that, I believe it's like the difference in ROE
is predicted by your intangible value factors. So can you
talk about some of like the interactions there and and
how how that relationship is is possible.
Speaker 4 (28:05):
Yeah, So if you step back, like what is what
is quality today? It's what is the modern moat?
Speaker 5 (28:09):
It's an intangible asset, Like why can you know, no,
don't notice charge so much money for Wigovi? Right, it's
because they have a patent. Why can Urmas or LBMA
charge so much for their handbags? Because they have these
really strong like brands that they've built. But how do
you actually get those things? They don't come for free.
You have to invest upfront in eventually getting those assets.
Speaker 4 (28:29):
So you know, what is.
Speaker 5 (28:30):
Profitably what is quality is looking for companies with those
moats today, right, But oftentimes the problem being that those
things already priced by the market because it's pretty obvious.
Whereas what's interesting about intangible value is you know, we're
looking for names that are kind of making the investments
today in advertising or in R and.
Speaker 4 (28:45):
D that will hopefully lead to that sort.
Speaker 5 (28:49):
Of moat down the line and hence the U the
Roe upgrade that that comes in line with that, which
is why, which is quite interesting, and I'm surprised to
find this that the correlation between the quality factor and
the intangible value factor or also zero. So it's not
just with traditional value and intangible value, it's also intangible
value with quality, which makes sense, and it kind of
you as you think more about it, and kind of
(29:09):
justifies why, you know, in a portfolio context, you'd want
to have it slotted in there alongside the other you know,
more traditional.
Speaker 7 (29:15):
So it was like for looking profitability factor exactly.
Speaker 4 (29:18):
Yeah, it's quality of the future.
Speaker 7 (29:19):
Very cool, very cool.
Speaker 2 (29:20):
Okay, So in the intro, Eric teased that you had
this conversation with Cliff Fastness. I'm curious what did you
What did you say that set him off?
Speaker 5 (29:32):
So, so, first of all, I have a ton of
respect for Cliff and for AQR.
Speaker 4 (29:35):
He is a legend.
Speaker 5 (29:37):
So but basically the discussion was this, right, which was Cliff,
you know, made the argument that the spread between the
basket of stocks that are value stocks as opposed to
growth stocks so expensive price to book or kind of
a generational wides and then as a result of that,
he said, therefore we should expect outperformance of value stocks
relative to growth stocks. It was kind of a two
(29:58):
phase argument, and he did a lot of really interesting
robustness checks to like adjust for various factors, like excluding
the magnificent seven, like things like that. You know, my
you know, my argument was kind of twofold. So first
I said, well, you know, on the definition of value, right,
this goes back to your dark matter point, which is,
you know, a lot of the phenomena we've seen in
(30:19):
the world can be explained by this by intangible assets.
So for example, the fact that the US has help
performed international stocks, well, the US has invested in more
intangible assets. We have the best universities, we have the
best global brands, we have you know, so on and
so forth. That kind of makes sense, right, It explains
just the general absolute overvaluation of the market on traditional metrics. Well,
if you don't adjust for all the investment we've made
(30:39):
in these intangible assets, then yeah, of course the markets
always going to seem expensive. And so I basically use
that line of reasoning, you know, with some data of course,
to kind of show that, Yeah, when you adjust, I
think what Cliff showed was that the spread between value
and growth stocks, you know, just headline number was like
a two standard deviation, like really wide number. But once
what I show was that once you adjust for intangible,
(31:00):
it comes down just still being expensive, but maybe that
point five so within the range of noise. And that
was kind of the second point, which was, you know,
Cliff was arguing that you know, a widespread should mean
you know, high perspective returns, and you know, I actually
looked at one of the papers that he wrote, Cliff
and his co authors a few years ago, where we
actually showed that, you know, yes, at extremes it matters,
but really within this middle band it's kind of not statistically.
Speaker 4 (31:23):
You know, meaningful.
Speaker 5 (31:24):
Right, So the conclusion being that, all right, well it's
not that wide, then, you know, should we be really
kind of pounding the table today?
Speaker 3 (31:30):
This is fascinating because what quants do is they take
what's work for active where they found alpha, and they
turn it into beta. So like values said, oh, over
the years, this person just outperformed because they just went
to cheap stocks. So they're like, oh, we'll just make
an index out of that. Bam, now that's done. They
did it with quality they did it with we'll say
momentum they did it with size. Intentional value does seem
(31:51):
like that latest thing, like what have the people been
leaning on to get that out performance in mojo? Like
how do you explain the cues being the S and
P all the time you take intangible value, it probably
does go in line a little more and explain it.
It makes you think, if this is a true factor
and you've now captured it and turn it into beta,
is there any alpha left?
Speaker 1 (32:12):
What else can you do?
Speaker 4 (32:13):
There's always going to be more alpha out there.
Speaker 5 (32:16):
Look, I mean, what we're trying to do is, as
you point out, just like trying to capture what is
it that a smart invester would do, like a smart
fundamental guy at like a top edge fund, what sorts
of things where they look at when they evaluate a
copy of like Disney or in the video or these
are just like kind of common sense things that to
the extent where we can use AI, we can use
all the new data available to make it into beta,
to make it into a systematic factor. That's good, But
(32:38):
then you know, the the smart guys, once it is
table stakes will find the next thing to lean on,
right and I have.
Speaker 1 (32:43):
What's the next thing?
Speaker 4 (32:44):
I don't know. I mean if I knew, then you
know it would.
Speaker 1 (32:46):
Be you wouldn't be here. Yeah, exactly whatever, yea, exactly.
Speaker 2 (32:52):
All right, we're gonna leave it there.
Speaker 7 (32:53):
Kai.
Speaker 2 (32:54):
One final question. Uh, it's a question we ask everyone
on the on the program. Uh, what is your favorite
ETF ticker other than your own?
Speaker 4 (33:01):
Oh?
Speaker 1 (33:03):
I know what he's gonna pick. I just know, go ahead.
Speaker 5 (33:06):
Well, I don't think I don't I don't think what
my opinion is matters. I think what matters is what
the market would say, and the market would say, M
E T A meadow It's like an eight figure ticker.
Speaker 3 (33:16):
Right, answered, like a true quant Well, meta is the
is the ticker that was sold to Martin. So yeah,
you're right, that is the most valuable ticker.
Speaker 4 (33:25):
Right.
Speaker 1 (33:25):
So I don't know why will hersh she is still
working around him. I don't understand that.
Speaker 3 (33:29):
Yeah, that was that was talking about a guy whohould
be on an island somewhere. Yeah, your that's a very
smart answer, by the way.
Speaker 4 (33:34):
So he So here's my thing.
Speaker 5 (33:36):
If if Tim Cook wants a rebrand Apple as Itan, Yeah.
Speaker 2 (33:43):
All right, Uh, Kai, Chris, thanks for joining us in trillion.
Thank you, Thank you, thanks for listening to Trillions. Until
next time. You can find us on the Bloomberg Terminal,
Bloomberg dot com, Apple Podcast, Spotify, or wherever else you'd
like to listen. We'd love to hear from you. We're
(34:05):
on Twitter, I'm at Joel Webber Show. He's at Eric Balcuna's.
This episode of Trillions was produced by Magnus Hendrickson. Bye
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