Welcomer trains. I'm Joel Webber and I'm Eric Belchunas. Eric, this was your idea.
I'm really curious about where this conversation goes today because all of this stuff was like candy as I've prepared for it.
Yeah, you know, we're getting back to the basics today. You know, how do you value a stock?
Now?
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
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 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 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 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 is important to know.
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.
Thanks for having me, Thanks for having me.
Okay, Kai, what is intangible value?
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.
Why is value investing not capturing this?
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 value does not actually is not actually.
Required to produce earnings.
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.
Why hadn't this been captured in an ETF until yours?
You know, I don't know.
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 have been attempts to reverse that by saying, all right, you're gonna spend one hundred million dollars building a building.
A factory that gets capitalized.
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,
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.
So it is your is your fund.
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.
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
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.
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.
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 do you?
But what really always helped me back.
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.
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.
Yeah, so interesting.
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?
Yeah?
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 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 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.
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.
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.
Again and again, run away and again.
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.
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 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 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 quality as a factor has just done so well the past two decades.
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.
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.
Critical, Apple, Google.
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.
Are have not really evolved to do that.
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 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.
And what problem did you have to solve in order to make this thing a reality?
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, 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.
It's all just noise.
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.
You know what this reminds me of, Joe. I'm going to go full metaphor here. Dark matter.
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, you have to correctly capture factors, track them. How do you work this in so?
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 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.
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, why isn't this a bigger deal?
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 has a huge real estate portfolio and a huge cash hoard and the other doesn't.
Of course, that company should be worth more than the other one. So you don't want to not use this.
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.
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.
No finger on the scale.
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 new patents, new trademarks, and that kind of feeds into the models and it automatically adjusts the relative rankings of stocks.
And how big of a universe are you able to come through right now? And where do you want to get to?
Well, we'll start with the where I want to get to.
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.
I am.
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.
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.
You know, or feel free to or if you want to.
Yeah, it's probably in the perspective.
But I you know, you mentioned that you use alternative data.
I'm guessing as higher frequency data NLP techniques to to put some you know, context around it.
So do you need to use alternative data?
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.
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 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
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 that that portfolio looks a lot more like the result of having added alternative data than just making this simple accounting based changes.
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.
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, 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, 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.
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?
Wells Fargo has a one point five percent weight and Ge is a one percent, but Apple's a four percent?
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?
Yeah?
Look it's it's and how much does it change over time?
So the methodology is consistent through time that does not change. Currently.
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 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.
Get more weight, that's obvious.
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
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.
Know you're going to look really bad.
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.
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.
Yeah.
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
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.
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 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.
Sure. Yeah, I mean you know, kay, You know when you do long only.
Obviously you have that equity beta, and I think a lot of advisors want that equity beta.
You know, to me, with long short, you know your real.
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.
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
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.
Would you agree with that?
Yeah, on hundred percent, Even though you don't do any you have no more control over it.
It's like you are too.
Now right, Yep.
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.
And quants like the fact that the way just to clear are two D two active? Is that what you're saying?
Very active?
Yes?
Not, well you heard him. He's coaching Luke and stuff. I mean he's pretty active.
Yeah.
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.
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, they're like.
Give me the data.
Yeah, yeah, and then let's get the humans that hell out of this because we're only gonna screw it up.
Yeah, but it's active and I'll be on the golf course checking out at the end of the quarter.
Quan, don't golf, come on?
Oh yeah, no, they might ski ball. Yeah.
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?
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.
Before you know a lot of tech stocks sold off.
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.
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.
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.
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.
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
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.
Sure, very very logical. One thing I wanted to ask you real fast.
Uh, you know this kind of goes with you know, is intangible value a different factor or how's it interact with other factors.
You have a great quote, I'm just going to quote you.
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.
Yeah, So if you step back, like what is what is quality today? It's what is the modern moat?
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.
So you know, what is.
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.
D that will hopefully lead to that sort.
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 justifies why, you know, in a portfolio context, you'd want to have it slotted in there alongside the other you know, more traditional.
So it was like for looking profitability factor exactly.
Yeah, it's quality of the future.
Very cool, very cool.
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?
So, so, first of all, I have a ton of respect for Cliff and for AQR.
He is a legend.
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 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 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 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, 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.
You know, meaningful.
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?
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 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?
What else can you do?
There's always going to be more alpha out there.
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 then you know, the the smart guys, once it is table stakes will find the next thing to lean on, right and I have.
What's the next thing?
I don't know. I mean if I knew, then you know it would.
Be you wouldn't be here. Yeah, exactly whatever, yea, exactly.
All right, we're gonna leave it there.
Kai.
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?
Oh?
I know what he's gonna pick. I just know, go ahead.
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.
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.
Right.
So I don't know why will hersh she is still working around him. I don't understand that.
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.
So he So here's my thing.
If if Tim Cook wants a rebrand Apple as Itan, Yeah.
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 on Twitter, I'm at Joel Webber Show. He's at Eric Balcuna's. This episode of Trillions was produced by Magnus Hendrickson. Bye