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Speaker 1: Welcomer trains. I'm Joel Webber and I'm Eric Belchunas. Eric,

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Speaker 1: this was your idea.

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Speaker 2: I'm really curious about where this conversation goes today because

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Speaker 2: all of this stuff was like candy as I've prepared

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Speaker 2: for it.

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Speaker 3: Yeah, you know, we're getting back to the basics today.

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Speaker 3: You know, how do you value a stock?

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Speaker 2: Now?

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Speaker 3: That's his stock conversation. But stocks make up index funds,

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Speaker 3: which make up ETFs, and a big wing of ETFs

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Speaker 3: are called smart beta, which are ETFs that sort of

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Speaker 3: use metrics that an active manager would use, price to earnings, price,

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Speaker 3: the book, dividends, et cetera. And within there there's all

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Speaker 3: kinds of variations. So in a way it's active even

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Speaker 3: though it uses an index. And so there's an ETF

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Speaker 3: that launched recently called the Sparkline Intangible Value ETF. I

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Speaker 3: TAN is the ticker, and this uses the intangible value,

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Speaker 3: which this person claims is a new factor. The reason

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Speaker 3: to call my attention is I was at the Democratized

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Speaker 3: quant event I don't know six months ago, which Wes

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Speaker 3: Gray from alf Architect puts on, and I saw this

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Speaker 3: guy Kai Wu debate Cliff Asnas who is like giant

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Speaker 3: in the quant world. I mean, he's like heavyweight, a lister, right,

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Speaker 3: and Kai has this ETF. It's indy and it was

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Speaker 3: sort of like a David and Goliath debate, no offense,

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Speaker 3: but Cliff had met his match here. I thought Kai

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Speaker 3: made some very good arguments. I was more on his

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Speaker 3: side by the end of the debate. Cliff was a

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Speaker 3: good sport. It was a great discussion. I love the quants.

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Speaker 3: They do a very academic type of rigorous debate when

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Speaker 3: they have events, and I like that. It was two

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Speaker 3: sides presented, and I thought we got to get this

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Speaker 3: guy on because not only is intangible value interesting and

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Speaker 3: people should know what that means. But when you think

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Speaker 3: of smart baita ETFs, like a value ETF, many of

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Speaker 3: them use price the book. Well what does that mean?

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Speaker 3: What is book value? Well, a lot of the book

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Speaker 3: values are old. They don't use things like the brand.

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Speaker 3: They use like how much actual literal capital goods the

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Speaker 3: company owns, and so they don't use real estate. So

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Speaker 3: there's this huge debate in the quant world on how

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Speaker 3: to actually define price the book, and that is a

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Speaker 3: major pillar of how you define value. So if you're

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Speaker 3: shopping for a value etf or value manager. This stuff

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Speaker 3: is important to know.

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Speaker 2: Joining us Kai Wu who's the founder chief investment officer

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Speaker 2: of Sparkline Capital, as well as Chris Kane, who's a

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Speaker 2: quant analyst at Bloomberg Intelligence, this time on Trillions the intangibles, Kai, Chris,

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Speaker 2: Welcome to trillions.

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Speaker 4: Thanks for having me, Thanks for having me.

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Speaker 1: Okay, Kai, what is intangible value?

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Speaker 5: So an intangible asset is, you know, as Eric was saying,

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Speaker 5: anything that's not your kind of factories, your cash, your property.

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Speaker 5: It's at Sparkline we have four pillars of intangibles. We

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Speaker 5: talk about intellectual property, brand equity, human capital, and network effects.

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Speaker 5: So again IP, human capital, brand, network effects, and these

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Speaker 5: assets are becoming more and more important for companies today.

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Speaker 1: Why is value investing not capturing this?

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Speaker 5: So when you think about value investing, it was really

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Speaker 5: popularized in the nineteen thirties with bang Gram's Security Analysis book.

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Speaker 5: And you go back to the thirties, right, the economy

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Speaker 5: was fully industrial. The big companies were railroads and textile mills,

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Speaker 5: and as a result, you know, these sorts of intangils

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Speaker 5: didn't really matter too much. But today the biggest companies

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Speaker 5: are you know, Apple, Facebook, firms for whom their book

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Speaker 5: value does not actually is not actually.

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Speaker 4: Required to produce earnings.

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Speaker 5: And so you know, think about you know Apple for example, right,

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Speaker 5: it's their brand, it's their the network effects around the

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Speaker 5: iPhone iOS ecosystem, it's the human capital and IP around

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Speaker 5: you know, their internal processors and such that allow them

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Speaker 5: to earn such fat profit margins.

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Speaker 2: Why hadn't this been captured in an ETF until yours?

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Speaker 4: You know, I don't know.

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Speaker 5: There have been attempts to to kind of capture some

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Speaker 5: of the intangible assets using accounting based metrics. So what

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Speaker 5: the weird anomalies within the way accounting works that gap

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Speaker 5: accounting is that they allow for the capitalization of physical

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Speaker 5: capex but not intangial assets. Right, so there are they

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Speaker 5: have been attempts to reverse that by saying, all right,

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Speaker 5: you're gonna spend one hundred million dollars building a building.

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Speaker 4: A factory that gets capitalized.

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Speaker 5: You're gonna spende hundred million dollars doing R and D

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Speaker 5: to develop a patent that gets capitalized, and doing so

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Speaker 5: you can kind of create a more holistic version of

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Speaker 5: book value is a little bit better. But what we

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Speaker 5: found here at sparkline is that you know that only

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Speaker 5: takes you so far, because again, like the link between

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Speaker 5: the money you put into R and D and what

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Speaker 5: you get out is super wide. So what we like

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Speaker 5: to do here is to focus instead on the actual products,

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Speaker 5: the actual outputs. You know, what patents do you actually get,

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Speaker 5: how strong is the brand you actually build through your

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Speaker 5: advertising efforts? And I think that's kind of a novel

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Speaker 5: and unique approach that really only became available with the

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Speaker 5: advent of you know, instructured data and natural language processing,

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Speaker 5: which we'll get into in a bit.

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Speaker 1: So it is your is your fund.

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Speaker 3: It isn't a value ETF that actually just uses this

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Speaker 3: one tweaks this one part. It's more of let's go

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Speaker 3: after the companies with the highest in tangible value. Again,

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Speaker 3: that's different then let's do a traditional value ETF. But

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Speaker 3: let's correct how we define price the book, right yours

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Speaker 3: is let's go after these stocks that are high and

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Speaker 3: intangible value, right yeah.

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Speaker 5: And I think one important thing to mention is that

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Speaker 5: it's not like we're going to go after the companies

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Speaker 5: with simply the most total overall innovative patents, let's say,

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Speaker 5: because then that just map do large cap names. Right,

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Speaker 5: what we carry about is how much as a shareholder

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Speaker 5: you get per dollar invested. Right, So it's very similar

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Speaker 5: like a dividend yield or like an earning yield. So

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Speaker 5: for each dollar invest how many you know PhDs do

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Speaker 5: I get? As an investor? How many you know Twitter

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Speaker 5: followers do I get? And so these are kind of

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Speaker 5: proxies for intangible assets. But again the key just being

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Speaker 5: that they're price based, very similar to price to book,

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Speaker 5: but you know, using kind of a more expansive set

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Speaker 5: of variables.

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Speaker 3: And let's bring in Chris Kane here, because Chris spends

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Speaker 3: all day looking at this quantitative data he has builds indices,

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Speaker 3: And I was curious, Chris, you know, sort of your

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Speaker 3: take on this. And you have to have a price

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Speaker 3: to book in your metrics and how you define that,

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Speaker 3: and I'm just curious to get your you know, how

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Speaker 3: you've considered this for your work.

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Speaker 6: Sure, I mean, I you know, I love this concept.

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Speaker 6: You know when I go to you know, customers and

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Speaker 6: speak to them about value investing, you know a lot

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Speaker 6: of the feedback I get it as well. These are

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Speaker 6: old companies. This is an old way to do it.

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Speaker 6: You know, this is kind of like anti innovation, and

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Speaker 6: you know, I don't have to tell everyone, you know

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Speaker 6: that we're kind of living through a gold native innovation

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Speaker 6: in many ways. You look at the cues, you look

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Speaker 6: at r KK, et cetera. So you know, do you

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Speaker 6: do you?

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Speaker 7: But what really always helped me back.

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Speaker 6: From those type of funds is like they're anti factor, right,

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Speaker 6: they're very high vaulved, they're very expensive. So do you

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Speaker 6: view you know, your your fund more like a value

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Speaker 6: tilt or you know, an innovation tilt, but without those

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Speaker 6: like bad factor waitings.

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Speaker 5: Yeah, I think that's a very fair way of characterizing

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Speaker 5: the strategy, right, It's it's in an innovation fund without

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Speaker 5: the kind of baggage, where as an investor, you don't

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Speaker 5: have to sacrifice your value exposure or your quality exposure

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Speaker 5: by going into it.

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Speaker 7: Yeah, so interesting.

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Speaker 6: So would you consider this like, I mean, would you

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Speaker 6: consider more like a growth fund or like a traditional

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Speaker 6: value fund, or would you consider it completely different and

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Speaker 6: separate in the stins?

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Speaker 4: Yeah?

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Speaker 5: Look, I mean I don't love the whole value versus

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Speaker 5: growth economy. I don't think it's it's fair to say

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Speaker 5: you have to be either one or the other. You know,

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Speaker 5: Warren Buffett has talked about this as well, as you know,

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Speaker 5: this is kind of being a false construction. The way

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Speaker 5: I would think about it is a traditional value ETF. Right,

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Speaker 5: what are they trying to do. They're trying to look

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Speaker 5: for stocks with low price to book ratios. In other words,

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Speaker 5: book value is a proxy for tangible capital. So they're

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Speaker 5: going to look within the tangible economy, the old economy

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Speaker 5: as you point out, industrials, banks, energy materials, and find

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Speaker 5: the cheapest names, which is a totally valid thing to do.

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Speaker 5: But you know, obviously, you know, as we move forward

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Speaker 5: in time with innovation, AI, et cetera, this is becoming

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Speaker 5: a vanishingly small part of the stock market. So what

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Speaker 5: we're trying to do with the intangible value ETF is

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Speaker 5: the same exact thing. We're looking for cheap stocks, but

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Speaker 5: relive not to tangible but intangible capital, which ends up

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Speaker 5: mapping to consumer brands to you know, tech platforms, you

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Speaker 5: know life sciences companies, and you know services businesses. So

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Speaker 5: it's kind of the same concept, but apply to the

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Speaker 5: other half, so to speak, of the stock market.

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Speaker 3: So is what you're saying part of the reason that

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Speaker 3: traditional value investing just sort of gets punched in the

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Speaker 3: face all the time and just lags for with fifteen

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Speaker 3: years at this point. I had a nice little run

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Speaker 3: in twenty twenty two, I believe, but now it's kind

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Speaker 3: of back in the gutter. Is that why traditional value

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Speaker 3: doesn't ever seem to have like a true regime takeover.

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Speaker 3: And at the same time, every time you think something

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Speaker 3: is coming back, like small caps or international, the CUES

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Speaker 3: just wakes up and says, uh uh, I'm going to

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Speaker 3: run over you.

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Speaker 1: Again and again, run away and again.

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Speaker 3: Like Marshall Lynch when we was talking about running people over,

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Speaker 3: He's like, I'm to smash you in the mouth again

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Speaker 3: and again and again and again and then you finally

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Speaker 3: am gonna run over you and then like you just

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Speaker 3: talk about how we scores touch downs anyway, piece mode.

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Speaker 3: The Cues is in constant bast mode mode. But is

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Speaker 3: that is intangible value? The reason that that phenomenon exists

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Speaker 3: again and again.

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Speaker 5: Yeah, We've actually done some analysis on both the CUES

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Speaker 5: and on ARKK and what we did was we said,

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Speaker 5: let's look at a factory based framework, right, think about

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Speaker 5: the Fama French model, which is, you know, there's the market,

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Speaker 5: there's a small cap there's value so on and so forth.

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Speaker 5: And we added a sixth factor, which is the intangible

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Speaker 5: value factor. And we looked at the holdings of both

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Speaker 5: of these funds and then decompose the return, say, can

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Speaker 5: we retrospectively explain its performance by allocating to the six

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Speaker 5: factors and then idiosyncretic risk their alpha right, And what

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Speaker 5: was quite interesting was both of these funds actually had

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Speaker 5: a very positive loading on intangible value and in fact,

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Speaker 5: a lot of their outperformance relative to Yes and people

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Speaker 5: one hundred the traditional stock market has been due to this,

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Speaker 5: you know, this this exposure to innovative companies. That being said,

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Speaker 5: there's also a lot of volatility around that, as you

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Speaker 5: point out, Chris, due to say, exposure to you know,

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Speaker 5: cheap press to bookstocks, which you know did really well

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Speaker 5: and then did really poorly, and you know kind of

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Speaker 5: cycles in these really wide gyrations. And also, you know,

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Speaker 5: especially in the case of the ar KAK, the exposures

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Speaker 5: earlier stage unprofitable tech companies has been you know, kind

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Speaker 5: of a negative contributor to their returns. Just given that

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Speaker 5: quality as a factor has just done so well the

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Speaker 5: past two decades.

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Speaker 2: Curious where the idea for the for for it came

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Speaker 2: from was did you have the idea for the ETF

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Speaker 2: or did you see a company? And we're like, that

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Speaker 2: is the poster child for intangible value. I'm going to

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Speaker 2: build a product around it.

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Speaker 5: Well kind of both, right, I mean you look at

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Speaker 5: the stock market, you look at companies like you know,

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Speaker 5: McDonald's or Coca Cola, you know, for whom brands are obviously.

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Speaker 4: Critical, Apple, Google.

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Speaker 5: Right, and it just kind of makes sense that these

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Speaker 5: are the things that should matter today. And it's shocking that,

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Speaker 5: you know, the quantitative metrics that we've used for many

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Speaker 5: many years.

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Speaker 4: Are have not really evolved to do that.

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Speaker 5: You know, I used to work for GMO Jeremy Grantham,

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Speaker 5: who was a pioneer in developing a lot of systematic

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Speaker 5: value strategies in the seventies and eighties, and so I've

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Speaker 5: always been thinking about this this problem. And you know,

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Speaker 5: we're talking on an ETF podcast value ets or like

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Speaker 5: a multie hundred billion dollar if not trillion dollar category,

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Speaker 5: if you you know, expand that to also include active

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Speaker 5: managers hollow value strategies. So this is a huge question

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Speaker 5: and one which I feel like up until you know,

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Speaker 5: now you know, just hasn't really been kind of satisfactory,

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Speaker 5: literally like addressed. You know, we need more research, more

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Speaker 5: and more work to understand the valuation of these names.

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Speaker 2: And what problem did you have to solve in order

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Speaker 2: to make this thing a reality?

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Speaker 5: Well, this goes back to your question about like timing,

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Speaker 5: like why now you know, the big problem is that

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Speaker 5: accounting accounting statements don't really contain enough insight into intangible assets,

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Speaker 5: and so you really need to go to unstructured data

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Speaker 5: or alternative data. Right, We're lucky that we live in

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Speaker 5: an air now. It's just been exponential growth in big data.

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Speaker 5: We have everything from we use LinkedIn glassdoor, you know,

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Speaker 5: job postings, patents, mars, all this information you know, obviously

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Speaker 5: just by first principles contains insight into intangible value. The

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Speaker 5: challenge being that, like the information is kind of locked

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Speaker 5: in there because you can't, you know, as a quant

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Speaker 5: just take a linear aggression running over it at twenty

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Speaker 5: thousand more document and get anything meaningful out.

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Speaker 4: It's all just noise.

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Speaker 5: And so that's why the advent of the transformer natural

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Speaker 5: language processing. You know, we were actually talking about this

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Speaker 5: in twenty twenty. We've wrote a paper saying, you know,

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Speaker 5: the killer app of AI within investing is then natural

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Speaker 5: processing language and NLP, you know toolkit, which allows us

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Speaker 5: to take unstructured data and kind of create structured factors

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Speaker 5: which can then be used as inputs into traditional valuation models.

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Speaker 1: You know what this reminds me of, Joe. I'm going

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Speaker 1: to go full metaphor here. Dark matter.

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Speaker 3: You know it's out there, you just can't see it,

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Speaker 3: and it is. It kind of explains some most of

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Speaker 3: the universes comprised of U. Yes, this is why the

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Speaker 3: cues are the cues. It's this dark matter of intangible

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Speaker 3: value because I'm looking at the holdings here. You know, Amazon, Meta, Google, Cisco, Intel,

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Speaker 3: those are some of the firms driving the cues. Chris,

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Speaker 3: you know in your world again this concept of dark matter,

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Speaker 3: you have to correctly capture factors, track them. How do

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Speaker 3: you work this in so?

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Speaker 6: I you know, I read the white paper and a

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Speaker 6: big fan. You know, I do view this as a

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Speaker 6: different type of factor. You know, I don't think as

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Speaker 6: you did with your six factor model. I don't think

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Speaker 6: you throw out per se traditional value as you showed

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Speaker 6: in the paper. You know the correlation between traditional value

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Speaker 6: and tangible value is pretty low. If I remember, actually

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Speaker 6: the correlation was higher to quality with intangible value. So

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Speaker 6: you know, to me, that's a value add I think,

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Speaker 6: you know, it's you know, the economy has changed. I

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Speaker 6: mean no one would say not right. I mean, it's

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Speaker 6: not plants anymore, it's not those tangible things. So this

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Speaker 6: is very logical. I view it as, you know, a

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Speaker 6: separate factor at least somewhat, and it can certainly add

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Speaker 6: value to a multi factor process.

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Speaker 3: Yeah, but why why not just forget traditional value? Like

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Speaker 3: why even use old Price the Book? Why isn't the

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Speaker 3: quant world much more adjusting things for this? Because it

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Speaker 3: does explain so much, and it just seems like if

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Speaker 3: you're doing value investing using Price the Book, it's like

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Speaker 3: using like a rotary phone or something. I don't understand, Like,

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Speaker 3: why isn't this a bigger deal?

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Speaker 5: You know, that's a great, great question, and I ask

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Speaker 5: myself that each day. But no, But look, we're all

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Speaker 5: as researchers kind of building on the edifice of what's

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Speaker 5: what's come before us, and you know, Fama French in

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Speaker 5: the mid nineties and Germany beforehand, you know, popularize this

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Speaker 5: idea of this book to market factor, which is important.

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Speaker 5: It's not that it doesn't matter, right to take the

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Speaker 5: converse to companies with a lot of IP, but one

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Speaker 5: has a huge real estate portfolio and a huge cash

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Speaker 5: hoard and the other doesn't.

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Speaker 4: Of course, that company should be worth more than the

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Speaker 4: other one. So you don't want to not use this.

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Speaker 5: It's just that you know, we can maybe do better

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Speaker 5: by adding additional dimensions of risk and dimensions of corporate

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Speaker 5: performance to our kind of mulo of factors.

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Speaker 2: When you think about this and what you've created is

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Speaker 2: your model just the model, and it finds the companies

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Speaker 2: and then you just you know, balance rebalance quarterly like

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Speaker 2: a smart beta fund or are you are you putting

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Speaker 2: a little bit of finger on the scale.

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Speaker 4: No finger on the scale.

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Speaker 5: So I mean my involvements only as a researcher kind

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Speaker 5: of setting up the parameters the model, figuring out what

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Speaker 5: data sets to look at, and how to build the

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Speaker 5: machine learning uh, you know infrastructure, but you know it's

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Speaker 5: it's all systematic, it's all data driven, right. Every day,

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Speaker 5: you know, new information comes in about you know, employee turnover,

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Speaker 5: about you know, cultures, corporate culture increasing, decreasing, you know,

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Speaker 5: scandals in the media or all all the good stuff

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Speaker 5: new patents, new trademarks, and that kind of feeds into

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Speaker 5: the models and it automatically adjusts the relative rankings of stocks.

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Speaker 2: And how big of a universe are you able to

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Speaker 2: come through right now? And where do you want to

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Speaker 2: get to?

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Speaker 4: Well, we'll start with the where I want to get to.

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Speaker 5: You know, I've actually just been working on a super

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Speaker 5: interesting project expanding the universe of stocks to global so

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Speaker 5: you know, effectively MSCI all country world.

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Speaker 4: I am.

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Speaker 5: I so like the nine thousand stocks or so right

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Speaker 5: now when you know, in terms of launching products, the

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Speaker 5: itn ETF is focused on the top one thousand largest

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Speaker 5: us ST so used larger medcap stocks. But obviously that

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Speaker 5: if it's not there's no kind of technological reason why

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Speaker 5: that was the case. We just wanted to start with

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Speaker 5: a product that you know, most people could kind of

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Speaker 5: get their heads around.

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Speaker 6: You know, one thing I wanted to ask you it It

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Speaker 6: was more kind of like the methodology of intangible value.

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Speaker 6: You know, you don't have to share secret sauce here

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Speaker 6: or anything, but.

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Speaker 7: You know, or feel free to or if you want to.

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Speaker 1: Yeah, it's probably in the perspective.

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Speaker 7: But I you know, you mentioned that you use alternative data.

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Speaker 6: I'm guessing as higher frequency data NLP techniques to to

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Speaker 6: put some you know, context around it.

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Speaker 7: So do you need to use alternative data?

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Speaker 6: Could you you know, substitute more traditional like balance sheet

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Speaker 6: data or financial statement data for that? How far would

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Speaker 6: you get if you did do that? Or is the

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Speaker 6: is there really the value add the NLP and the

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Speaker 6: alternative data.

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Speaker 5: So we use both traditional accounting based information and alternative data,

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Speaker 5: and we actually I can give you a very clear answer.

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Speaker 5: So if you look at like the performance historically of

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Speaker 5: say the MSCI, you know value index right relatively in

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Speaker 5: the markets in pretty bad. You know, as you point

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Speaker 5: out the past fifteen years. If instead you say, well,

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Speaker 5: well let's now allow the capitalization of intangible investment so

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Speaker 5: R and D. You know, as you kind of invest

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Speaker 5: R and D, you build up a balance sheet asset

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Speaker 5: for that and then you appreciate it over time. Same

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Speaker 5: for sales and marketing expenditures. Well you get a line

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Speaker 5: that's a little bit less bad, but still no panacea, right,

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Speaker 5: it still goes down. And then when we said well

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Speaker 5: let's start adding you know, more sources of data like

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Speaker 5: I mentioned patents, I mentioned LinkedIn, you know, to measure

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Speaker 5: each of the pillars using unstructured data. And that's when

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Speaker 5: the line starts to look pretty interesting. Right And if

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Speaker 5: you look at just the name, so put us out

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Speaker 5: even the historical performance, because that's just a back test.

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Speaker 5: Is the names you know, changed dramatically as you kind

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Speaker 5: of continually iterate and add more and more data sources

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Speaker 5: to a portfolio that just looks more like what it

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Speaker 5: should look like. Right Like if you if I said,

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Speaker 5: like first principles, build me a portfolio companies that are

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Speaker 5: you know, attractively valued relative to prodigious and tangibles, right

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Speaker 5: that that portfolio looks a lot more like the result

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Speaker 5: of having added alternative data than just making this simple

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Speaker 5: accounting based changes.

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Speaker 3: It seems to me that you know, most people would

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Speaker 3: hear this and go, I get it. It's kind of

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Speaker 3: like tech stocks, right They they don't have a lot

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Speaker 3: of machinery lying around, they're mostly intangible value. But there

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Speaker 3: are some companies here that aren't tech. Right, So just

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Speaker 3: let's just go over how are these are intangible value?

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Speaker 3: Wells Fargo and General Electric those almost seem more traditional value.

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Speaker 5: Right, Well, I mean ge in particularly, it's it's mainly

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Speaker 5: the brand that's kind of carrying that that company. Wells Fargo,

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Speaker 5: like many banks, has obviously a large balance sheet, but

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Speaker 5: for them, it's probably gonna be human capital. You know

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Speaker 5: that that is its main contributor. And I've actually done

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Speaker 5: this work. It's kind of quite interesting because you know,

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Speaker 5: even if you look at the website for Ian, we

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Speaker 5: do this analysis where we do a balance sheet dot composition,

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Speaker 5: So we take all the stocks in the portfolio and

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Speaker 5: assign it to a single pill pillar. So for example,

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Speaker 5: like a clear example would be like Nike or maybe

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Speaker 5: Harley Davidson would be clearly in brand. Right, then you

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Speaker 5: have like Pfizer or like a m D clearly in IP.

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Speaker 5: And then you know Goldman might be in might maybe

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Speaker 5: be a non financial by the human capital, right, And

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Speaker 5: when you do that, the balance sheet is you know, yes,

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Speaker 5: you know IP, that pillar ends up being about forty percent,

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Speaker 5: but it's closely followed by human capital, brand and then

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Speaker 5: tangible being the least important. So it is a kind

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Speaker 5: of relatively diversity portfolio across you know, a variety of

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Speaker 5: different pillars.

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Speaker 2: Okay, so if we've got your model and it's this

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Speaker 2: heat seeking missile to find intangible value out there. How

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Speaker 2: do you weight this in a portfolio? How do you

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Speaker 2: look at Wells Fargo or Ge and go like, I'm

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Speaker 2: we're gonna uh with the exposure to them?

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Speaker 3: Wells Fargo has a one point five percent weight and

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Speaker 3: Ge is a one percent, but Apple's a four percent?

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Speaker 2: Yeah? Or Amazon or Meta? Like how you know if

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Speaker 2: your robots gets to do what it does? Like, how

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Speaker 2: do you decide who gets what percentage?

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Speaker 7: Yeah?

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Speaker 1: Look it's it's and how much does it change over time?

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Speaker 4: So the methodology is consistent through time that does not change. Currently.

435
00:19:50,040 --> 00:19:52,119
Speaker 5: What we're doing is there's always a trade off in

436
00:19:52,160 --> 00:19:54,080
Speaker 5: a quant world, as you know, Chris, which is you

437
00:19:54,080 --> 00:19:55,840
Speaker 5: know you have too few stocks and it ends up

438
00:19:55,880 --> 00:19:58,600
Speaker 5: beingcoming like all driven by idiosyncratic risk. Oh you have

439
00:19:58,640 --> 00:20:01,000
Speaker 5: an own you know, Twitter and and elon texts, something

440
00:20:01,040 --> 00:20:03,119
Speaker 5: weird out and then you know you're done right, Like,

441
00:20:03,320 --> 00:20:05,240
Speaker 5: so you want to have a certain amount of diversification

442
00:20:05,920 --> 00:20:07,840
Speaker 5: to protect against that, but you don't want to be

443
00:20:07,880 --> 00:20:09,919
Speaker 5: too many stocks. If you have a thousand of it,

444
00:20:09,960 --> 00:20:12,159
Speaker 5: of a thousand stocks, it's basically just the index one

445
00:20:12,160 --> 00:20:14,080
Speaker 5: at that point, right, So for us, we pick one

446
00:20:14,160 --> 00:20:15,919
Speaker 5: hundred and fifty as our cut off. So it's like,

447
00:20:15,960 --> 00:20:19,040
Speaker 5: you know, trying to strike a balance between being you know,

448
00:20:19,080 --> 00:20:23,240
Speaker 5: concentrated enough around this factor, but also having diversification on

449
00:20:23,280 --> 00:20:25,240
Speaker 5: the name sense. And then in terms of the waiting

450
00:20:25,240 --> 00:20:27,560
Speaker 5: amongst those stocks, there's kind of two things that drive that.

451
00:20:27,840 --> 00:20:31,119
Speaker 5: So the first is just the score, right, higher scores.

452
00:20:30,880 --> 00:20:32,120
Speaker 4: Get more weight, that's obvious.

453
00:20:32,720 --> 00:20:35,200
Speaker 5: The second thing we do, though, is this modified market

454
00:20:35,240 --> 00:20:37,399
Speaker 5: cap waiting, right, And again this is to deal with

455
00:20:37,440 --> 00:20:40,159
Speaker 5: a trade off. So imagine I were to create a

456
00:20:41,080 --> 00:20:43,119
Speaker 5: you know, market cap weighted version of the strategy to say,

457
00:20:43,119 --> 00:20:45,040
Speaker 5: all right, well, like Apple has ten x the market

458
00:20:45,080 --> 00:20:47,320
Speaker 5: cap of stock you know two, so therefore it gets

459
00:20:47,520 --> 00:20:49,159
Speaker 5: next to weight. Well, then you end up with like

460
00:20:49,240 --> 00:20:51,800
Speaker 5: very little breath because you know, especially these megacaps have

461
00:20:51,800 --> 00:20:53,640
Speaker 5: become so large and in the seas, it's you don't

462
00:20:53,680 --> 00:20:55,960
Speaker 5: have much ability to kind of over underweight. On the

463
00:20:55,960 --> 00:20:58,000
Speaker 5: other hand, if you do equal weight instead, you end

464
00:20:58,080 --> 00:21:01,520
Speaker 5: up creating this huge bias towards the factor, right, where like, yes,

465
00:21:01,520 --> 00:21:02,800
Speaker 5: you have a lot of active share, but it's all

466
00:21:02,840 --> 00:21:04,479
Speaker 5: just kind of like junk food, right, It's all just like, oh,

467
00:21:04,520 --> 00:21:06,240
Speaker 5: you know, I just have a small cap and so

468
00:21:06,600 --> 00:21:08,600
Speaker 5: you know, for better or worse, your clients are gonna

469
00:21:08,640 --> 00:21:11,159
Speaker 5: judge against the MP. And if you know, as it

470
00:21:11,200 --> 00:21:13,359
Speaker 5: has played out the past two years, right equal weighted

471
00:21:13,600 --> 00:21:17,160
Speaker 5: RSP for example, has underperformed SPY, you.

472
00:21:17,080 --> 00:21:18,240
Speaker 4: Know you're going to look really bad.

473
00:21:18,359 --> 00:21:21,000
Speaker 5: So we ended up doing this this middle ground where

474
00:21:21,000 --> 00:21:23,639
Speaker 5: we basically half marketapp weight the stocks so that we

475
00:21:23,640 --> 00:21:25,960
Speaker 5: can kind of like thread the needle between these two

476
00:21:26,080 --> 00:21:26,880
Speaker 5: these two challenges.

477
00:21:26,920 --> 00:21:29,879
Speaker 2: Okay, so obviously there's a product in the one fifty,

478
00:21:30,080 --> 00:21:32,360
Speaker 2: but if you have this data, there's the other side

479
00:21:32,400 --> 00:21:34,560
Speaker 2: of the spectrum with the companies that aren't doing so

480
00:21:34,640 --> 00:21:36,520
Speaker 2: good at this. Have you thought about building a product

481
00:21:36,880 --> 00:21:37,840
Speaker 2: that combines the two.

482
00:21:38,240 --> 00:21:38,480
Speaker 4: Yeah.

483
00:21:38,480 --> 00:21:41,520
Speaker 5: Look, I mean we've looked at short side as well, right,

484
00:21:41,560 --> 00:21:42,840
Speaker 5: And if you look at like the so looking at

485
00:21:42,880 --> 00:21:45,520
Speaker 5: the top fifteen percent and you short the bottom fifteen percent,

486
00:21:45,680 --> 00:21:48,600
Speaker 5: that actually works well. Right Historically in back test the

487
00:21:48,640 --> 00:21:52,000
Speaker 5: short side, these things do underperform, right, So in theory

488
00:21:52,000 --> 00:21:54,120
Speaker 5: there is a product around that. Of course, like if

489
00:21:54,160 --> 00:21:55,919
Speaker 5: we're in the ETF space, it's a little challenging to

490
00:21:55,960 --> 00:21:58,760
Speaker 5: do long short, especially on single names, because it's transparent

491
00:21:58,800 --> 00:22:00,639
Speaker 5: and people can kind of pick you off. So that

492
00:22:00,680 --> 00:22:02,440
Speaker 5: hasn't been our starting points. But you know, I come

493
00:22:02,440 --> 00:22:03,800
Speaker 5: from an institution in a world where I used to

494
00:22:03,880 --> 00:22:06,560
Speaker 5: run you know, large hedge funds, and so that's totally

495
00:22:06,560 --> 00:22:09,199
Speaker 5: like a product that could be available to the right client.

496
00:22:09,600 --> 00:22:11,400
Speaker 5: But as it turns out, most of our investor base,

497
00:22:11,560 --> 00:22:13,720
Speaker 5: they like the beta. They like you know, being you

498
00:22:13,760 --> 00:22:15,640
Speaker 5: know long stock to stocks go up over time.

499
00:22:15,760 --> 00:22:17,840
Speaker 3: Yeah, this is really fascinating, this idea of how to

500
00:22:17,880 --> 00:22:21,560
Speaker 3: make a factor strategy, because the academics do long short

501
00:22:21,800 --> 00:22:24,159
Speaker 3: because you're trying to isolate the factor. But when you

502
00:22:24,200 --> 00:22:26,199
Speaker 3: do long short, you get a lot of offsetting, so

503
00:22:26,240 --> 00:22:29,480
Speaker 3: your volatility goes down. So it's a nice easy ride.

504
00:22:29,880 --> 00:22:31,919
Speaker 3: But it never has like a shiny object moment. It

505
00:22:31,960 --> 00:22:33,879
Speaker 3: never has like breakout performance. This is the problem with

506
00:22:33,880 --> 00:22:36,840
Speaker 3: the Jim Kramer ETF. It goes long short, and in

507
00:22:36,840 --> 00:22:39,440
Speaker 3: the advisor world, I think, unlike institutions, they need a

508
00:22:39,480 --> 00:22:42,639
Speaker 3: little shiny object moment. And Chris, you deal with this

509
00:22:42,680 --> 00:22:45,560
Speaker 3: all the time. You do make long short in disease,

510
00:22:45,640 --> 00:22:48,960
Speaker 3: but clearly, when you're actually trying to package some of

511
00:22:49,000 --> 00:22:52,960
Speaker 3: what you do into an ETF marketplace, decisions have to

512
00:22:53,000 --> 00:22:53,399
Speaker 3: be made.

513
00:22:53,600 --> 00:22:55,680
Speaker 7: Sure. Yeah, I mean you know, kay, You know when

514
00:22:55,680 --> 00:22:56,680
Speaker 7: you do long only.

515
00:22:56,520 --> 00:22:58,480
Speaker 6: Obviously you have that equity beta, and I think a

516
00:22:58,520 --> 00:23:01,280
Speaker 6: lot of advisors want that equity beta.

517
00:23:01,359 --> 00:23:03,760
Speaker 7: You know, to me, with long short, you know your real.

518
00:23:03,680 --> 00:23:07,760
Speaker 6: Value add there is a lower correlation, significantly lower correlation

519
00:23:07,880 --> 00:23:10,240
Speaker 6: to traditional stocks and bonds. So if you're a traditional

520
00:23:10,280 --> 00:23:12,560
Speaker 6: investor that has that already, I think that's really where

521
00:23:12,640 --> 00:23:16,400
Speaker 6: long short shines. But long only factor investing is certainly,

522
00:23:16,880 --> 00:23:18,720
Speaker 6: you know, a good approach as well.

523
00:23:19,160 --> 00:23:21,879
Speaker 3: Also listening to Kai and going over the design of

524
00:23:21,920 --> 00:23:24,960
Speaker 3: the ETF and all these decisions that are made, I

525
00:23:25,000 --> 00:23:27,879
Speaker 3: would say you probably made twenty five decisions somewhere not

526
00:23:27,960 --> 00:23:31,600
Speaker 3: to mention all the research. So we're talking like potentially

527
00:23:32,080 --> 00:23:34,080
Speaker 3: one hundred things that you could tweak that would make

528
00:23:34,119 --> 00:23:38,080
Speaker 3: the returns different. That's why I think smart beta is active.

529
00:23:39,000 --> 00:23:42,320
Speaker 3: It's just it's just all the active is done in

530
00:23:42,359 --> 00:23:45,080
Speaker 3: the design. It's like you're designing an active robot. Once

531
00:23:45,119 --> 00:23:46,960
Speaker 3: you close the door and like, you know, screw in

532
00:23:47,000 --> 00:23:50,280
Speaker 3: the bolts, it's now a robot, but all of the

533
00:23:50,320 --> 00:23:52,679
Speaker 3: decisions you made before you close the door are active.

534
00:23:53,040 --> 00:23:53,919
Speaker 1: Would you agree with that?

535
00:23:54,080 --> 00:23:56,560
Speaker 3: Yeah, on hundred percent, Even though you don't do any

536
00:23:56,840 --> 00:23:58,640
Speaker 3: you have no more control over it.

537
00:23:58,640 --> 00:24:00,000
Speaker 1: It's like you are too.

538
00:24:00,359 --> 00:24:02,040
Speaker 4: Now right, Yep.

539
00:24:02,800 --> 00:24:05,919
Speaker 5: All the active decisions is upfront in the construction of

540
00:24:06,040 --> 00:24:08,560
Speaker 5: the model. But then once you kind of finish that

541
00:24:08,600 --> 00:24:10,960
Speaker 5: process and as you say, you you know, turn the

542
00:24:11,000 --> 00:24:12,480
Speaker 5: key and you throw it away, then you know, it

543
00:24:12,560 --> 00:24:13,560
Speaker 5: kind of runs on his own.

544
00:24:13,840 --> 00:24:16,080
Speaker 1: And quants like the fact that the way just to

545
00:24:16,080 --> 00:24:18,400
Speaker 1: clear are two D two active? Is that what you're saying?

546
00:24:18,800 --> 00:24:19,240
Speaker 2: Very active?

547
00:24:19,400 --> 00:24:19,560
Speaker 7: Yes?

548
00:24:19,680 --> 00:24:21,639
Speaker 3: Not, well you heard him. He's coaching Luke and stuff.

549
00:24:21,640 --> 00:24:22,760
Speaker 3: I mean he's pretty active.

550
00:24:22,920 --> 00:24:23,080
Speaker 2: Yeah.

551
00:24:23,720 --> 00:24:26,520
Speaker 1: Yeah, it's not like a dishwasher. That's like like an index.

552
00:24:26,600 --> 00:24:29,000
Speaker 1: That's so there's other ones that were on the on

553
00:24:29,040 --> 00:24:33,159
Speaker 1: the rig. Yeah, I don't know what C three PO is.

554
00:24:33,200 --> 00:24:35,920
Speaker 1: That's a whole different thing there. But our changes okay,

555
00:24:36,640 --> 00:24:37,680
Speaker 1: so droll.

556
00:24:38,080 --> 00:24:41,639
Speaker 3: You know, these quants they love the idea that the

557
00:24:41,720 --> 00:24:45,479
Speaker 3: humans don't get involved. So like there's traditional active like

558
00:24:45,560 --> 00:24:49,320
Speaker 3: the sort of fidelity active manager that you're supposed to

559
00:24:49,359 --> 00:24:51,480
Speaker 3: trust with your money. They're a five star manager. They're

560
00:24:51,520 --> 00:24:53,399
Speaker 3: just good at picking stocks. Like Peter Lynch, I went

561
00:24:53,400 --> 00:24:55,439
Speaker 3: to the mall, I saw these kids lined up. I

562
00:24:55,440 --> 00:24:59,040
Speaker 3: bought Nike. These quants think that's all like BS no,

563
00:24:59,119 --> 00:24:59,560
Speaker 3: they're like.

564
00:24:59,600 --> 00:25:00,320
Speaker 1: Give me the data.

565
00:25:00,520 --> 00:25:02,520
Speaker 3: Yeah, yeah, and then let's get the humans that hell

566
00:25:02,560 --> 00:25:04,159
Speaker 3: out of this because we're only gonna screw it up.

567
00:25:04,240 --> 00:25:06,040
Speaker 1: Yeah, but it's active and I'll be on the golf

568
00:25:06,119 --> 00:25:08,440
Speaker 1: course checking out at the end of the quarter.

569
00:25:09,000 --> 00:25:09,919
Speaker 4: Quan, don't golf, come on?

570
00:25:10,760 --> 00:25:21,639
Speaker 1: Oh yeah, no, they might ski ball. Yeah.

571
00:25:21,720 --> 00:25:26,879
Speaker 2: I'm curious Kai just about performance, because it's been you

572
00:25:26,960 --> 00:25:31,240
Speaker 2: launched in twenty twenty one, you're below share prices below,

573
00:25:31,280 --> 00:25:34,120
Speaker 2: then went way down, and then you've had a good

574
00:25:34,160 --> 00:25:36,000
Speaker 2: year so far. Like when you try and make sense

575
00:25:36,000 --> 00:25:37,000
Speaker 2: of it, what's been happening?

576
00:25:37,280 --> 00:25:39,280
Speaker 5: Yeah, So the way we think about the strategy is

577
00:25:39,320 --> 00:25:43,480
Speaker 5: against an internal benchmark of you know, factor neutralized you know,

578
00:25:43,720 --> 00:25:46,240
Speaker 5: stock stock performance, and you know, on that on that metric,

579
00:25:46,280 --> 00:25:48,000
Speaker 5: like we're actually quite happy with how things that have

580
00:25:48,119 --> 00:25:50,600
Speaker 5: unfolded so far. Like obviously you can't control the exact

581
00:25:50,640 --> 00:25:52,960
Speaker 5: timeing of launched, and like who we'll unfold you know,

582
00:25:52,960 --> 00:25:55,920
Speaker 5: in a subsequent year or two, Like we launched June

583
00:25:55,960 --> 00:25:57,360
Speaker 5: twenty one right right.

584
00:25:57,200 --> 00:25:59,160
Speaker 4: Before you know a lot of tech stocks sold off.

585
00:25:59,440 --> 00:26:01,879
Speaker 5: We actually you know, did better than you know, a

586
00:26:01,920 --> 00:26:05,400
Speaker 5: lot of innovation focused ones you might you might say,

587
00:26:05,440 --> 00:26:07,120
Speaker 5: and then you know, we've also enjoyed the ride op,

588
00:26:07,800 --> 00:26:09,280
Speaker 5: but again, like it's a pretty short period, so we

589
00:26:09,320 --> 00:26:12,320
Speaker 5: don't want to like over index on any particular regime

590
00:26:12,359 --> 00:26:13,800
Speaker 5: that we happened to have come into.

591
00:26:14,040 --> 00:26:16,640
Speaker 3: I'll give them a shout. It's out performing the Value

592
00:26:16,680 --> 00:26:19,639
Speaker 3: Factory TF for my shares and the SMP, although losing

593
00:26:19,680 --> 00:26:22,240
Speaker 3: to growth, but if you consider yourself somewhere in between,

594
00:26:22,359 --> 00:26:25,400
Speaker 3: that's I think it was up eighteen percent. But you're right,

595
00:26:25,720 --> 00:26:29,560
Speaker 3: the timing is crucial with these launches. You launch right

596
00:26:29,600 --> 00:26:31,440
Speaker 3: before a market downturn, it takes some take, it takes

597
00:26:31,440 --> 00:26:32,760
Speaker 3: a little while to come back, but it's all about

598
00:26:32,800 --> 00:26:33,840
Speaker 3: relative performance as well.

599
00:26:33,920 --> 00:26:35,680
Speaker 5: Yeah, and look, we're we're in this for the long run.

600
00:26:35,720 --> 00:26:38,119
Speaker 5: Like I think, just intellectually we view this as the

601
00:26:38,160 --> 00:26:40,439
Speaker 5: way that you're the way forward for value investors, and

602
00:26:40,480 --> 00:26:41,879
Speaker 5: so we want to have products in the market. But

603
00:26:42,240 --> 00:26:44,600
Speaker 5: ultimately the this is like a long game we're playing.

604
00:26:44,600 --> 00:26:46,080
Speaker 2: When you when you were working on this and like

605
00:26:46,160 --> 00:26:48,199
Speaker 2: doing the back testing everything, what was the what was

606
00:26:48,240 --> 00:26:51,439
Speaker 2: the thing that from a performance standpoint that really jumped

607
00:26:51,440 --> 00:26:53,680
Speaker 2: out to you and we're like we're onto something here.

608
00:26:53,800 --> 00:26:55,879
Speaker 5: Well, I think it's quite interesting how you know the

609
00:26:55,880 --> 00:26:59,560
Speaker 5: the individual pillars of this strategy kind of interact together.

610
00:27:00,119 --> 00:27:01,399
Speaker 5: You think about you know, IP is kind of the

611
00:27:01,400 --> 00:27:03,520
Speaker 5: most obvious, right, it tends to be technology names. It

612
00:27:03,560 --> 00:27:07,040
Speaker 5: tends to be you know, some communications media companies, and

613
00:27:07,160 --> 00:27:10,159
Speaker 5: you have like your consumer brands, and you have you know,

614
00:27:10,200 --> 00:27:12,520
Speaker 5: human capital tends to be very financial services oriented as

615
00:27:12,520 --> 00:27:15,359
Speaker 5: well as technology, network effects, more communication. But it's just

616
00:27:15,359 --> 00:27:17,840
Speaker 5: interesting they tend to be uncorrelated. They kind of play

617
00:27:17,840 --> 00:27:20,800
Speaker 5: well together and you know, contribute to an overall you

618
00:27:20,840 --> 00:27:23,320
Speaker 5: know basket in a nice way. Right, Like you can

619
00:27:23,400 --> 00:27:25,239
Speaker 5: have a company with like really strong IP, but if

620
00:27:25,240 --> 00:27:27,119
Speaker 5: they have no marketing, like that's not going to succeed

621
00:27:27,359 --> 00:27:30,119
Speaker 5: and vice versa. So you kind of need, you know,

622
00:27:30,160 --> 00:27:32,159
Speaker 5: the collection of all these intellgible assets to really be

623
00:27:32,640 --> 00:27:34,320
Speaker 5: to really thrive in the modern day.

624
00:27:34,480 --> 00:27:37,040
Speaker 7: Sure, very very logical. One thing I wanted to ask

625
00:27:37,040 --> 00:27:37,560
Speaker 7: you real fast.

626
00:27:37,640 --> 00:27:39,199
Speaker 6: Uh, you know this kind of goes with you know,

627
00:27:39,359 --> 00:27:41,840
Speaker 6: is intangible value a different factor or how's it interact

628
00:27:41,880 --> 00:27:42,480
Speaker 6: with other factors.

629
00:27:42,520 --> 00:27:44,080
Speaker 7: You have a great quote, I'm just going to quote you.

630
00:27:43,960 --> 00:27:46,800
Speaker 6: You say, well, the quality factor seeks firms that are

631
00:27:46,840 --> 00:27:51,080
Speaker 6: profitable today. In tangible value seeks firms that are profitable

632
00:27:51,119 --> 00:27:54,119
Speaker 6: tomorrow and you have this fantastic graph that shows you

633
00:27:54,119 --> 00:27:56,440
Speaker 6: know that, I believe it's like the difference in ROE

634
00:27:56,680 --> 00:27:59,359
Speaker 6: is predicted by your intangible value factors. So can you

635
00:27:59,400 --> 00:28:02,359
Speaker 6: talk about some of like the interactions there and and

636
00:28:02,600 --> 00:28:05,480
Speaker 6: how how that relationship is is possible.

637
00:28:05,560 --> 00:28:07,679
Speaker 4: Yeah, So if you step back, like what is what

638
00:28:07,760 --> 00:28:09,760
Speaker 4: is quality today? It's what is the modern moat?

639
00:28:09,800 --> 00:28:13,359
Speaker 5: It's an intangible asset, Like why can you know, no,

640
00:28:13,640 --> 00:28:15,960
Speaker 5: don't notice charge so much money for Wigovi? Right, it's

641
00:28:15,960 --> 00:28:19,320
Speaker 5: because they have a patent. Why can Urmas or LBMA

642
00:28:19,480 --> 00:28:21,640
Speaker 5: charge so much for their handbags? Because they have these

643
00:28:21,720 --> 00:28:23,960
Speaker 5: really strong like brands that they've built. But how do

644
00:28:24,000 --> 00:28:25,680
Speaker 5: you actually get those things? They don't come for free.

645
00:28:25,720 --> 00:28:29,160
Speaker 5: You have to invest upfront in eventually getting those assets.

646
00:28:29,480 --> 00:28:30,680
Speaker 4: So you know, what is.

647
00:28:30,680 --> 00:28:33,560
Speaker 5: Profitably what is quality is looking for companies with those

648
00:28:33,600 --> 00:28:36,280
Speaker 5: moats today, right, But oftentimes the problem being that those

649
00:28:36,280 --> 00:28:38,320
Speaker 5: things already priced by the market because it's pretty obvious.

650
00:28:38,520 --> 00:28:41,320
Speaker 5: Whereas what's interesting about intangible value is you know, we're

651
00:28:41,320 --> 00:28:43,480
Speaker 5: looking for names that are kind of making the investments

652
00:28:43,480 --> 00:28:45,920
Speaker 5: today in advertising or in R and.

653
00:28:45,960 --> 00:28:49,240
Speaker 4: D that will hopefully lead to that sort.

654
00:28:49,000 --> 00:28:51,600
Speaker 5: Of moat down the line and hence the U the

655
00:28:51,720 --> 00:28:54,720
Speaker 5: Roe upgrade that that comes in line with that, which

656
00:28:54,760 --> 00:28:56,640
Speaker 5: is why, which is quite interesting, and I'm surprised to

657
00:28:56,640 --> 00:28:59,280
Speaker 5: find this that the correlation between the quality factor and

658
00:28:59,320 --> 00:29:02,040
Speaker 5: the intangible value factor or also zero. So it's not

659
00:29:02,080 --> 00:29:04,760
Speaker 5: just with traditional value and intangible value, it's also intangible

660
00:29:04,840 --> 00:29:07,200
Speaker 5: value with quality, which makes sense, and it kind of

661
00:29:07,440 --> 00:29:09,120
Speaker 5: you as you think more about it, and kind of

662
00:29:09,200 --> 00:29:11,880
Speaker 5: justifies why, you know, in a portfolio context, you'd want

663
00:29:11,880 --> 00:29:14,520
Speaker 5: to have it slotted in there alongside the other you know,

664
00:29:14,520 --> 00:29:15,400
Speaker 5: more traditional.

665
00:29:15,160 --> 00:29:18,120
Speaker 7: So it was like for looking profitability factor exactly.

666
00:29:18,200 --> 00:29:19,360
Speaker 4: Yeah, it's quality of the future.

667
00:29:19,480 --> 00:29:20,560
Speaker 7: Very cool, very cool.

668
00:29:20,720 --> 00:29:23,520
Speaker 2: Okay, So in the intro, Eric teased that you had

669
00:29:23,560 --> 00:29:28,560
Speaker 2: this conversation with Cliff Fastness. I'm curious what did you

670
00:29:28,640 --> 00:29:30,160
Speaker 2: What did you say that set him off?

671
00:29:32,680 --> 00:29:33,840
Speaker 5: So, so, first of all, I have a ton of

672
00:29:33,840 --> 00:29:35,520
Speaker 5: respect for Cliff and for AQR.

673
00:29:35,760 --> 00:29:36,520
Speaker 4: He is a legend.

674
00:29:37,400 --> 00:29:40,400
Speaker 5: So but basically the discussion was this, right, which was Cliff,

675
00:29:41,040 --> 00:29:44,120
Speaker 5: you know, made the argument that the spread between the

676
00:29:44,160 --> 00:29:46,360
Speaker 5: basket of stocks that are value stocks as opposed to

677
00:29:46,880 --> 00:29:49,720
Speaker 5: growth stocks so expensive price to book or kind of

678
00:29:49,760 --> 00:29:52,560
Speaker 5: a generational wides and then as a result of that,

679
00:29:52,920 --> 00:29:57,160
Speaker 5: he said, therefore we should expect outperformance of value stocks

680
00:29:57,160 --> 00:29:58,600
Speaker 5: relative to growth stocks. It was kind of a two

681
00:29:58,640 --> 00:30:01,960
Speaker 5: phase argument, and he did a lot of really interesting

682
00:30:02,000 --> 00:30:05,440
Speaker 5: robustness checks to like adjust for various factors, like excluding

683
00:30:05,440 --> 00:30:09,080
Speaker 5: the magnificent seven, like things like that. You know, my

684
00:30:09,440 --> 00:30:11,360
Speaker 5: you know, my argument was kind of twofold. So first

685
00:30:11,400 --> 00:30:14,280
Speaker 5: I said, well, you know, on the definition of value, right,

686
00:30:14,280 --> 00:30:16,600
Speaker 5: this goes back to your dark matter point, which is,

687
00:30:16,640 --> 00:30:19,080
Speaker 5: you know, a lot of the phenomena we've seen in

688
00:30:19,120 --> 00:30:21,840
Speaker 5: the world can be explained by this by intangible assets.

689
00:30:21,840 --> 00:30:23,640
Speaker 5: So for example, the fact that the US has help

690
00:30:23,680 --> 00:30:26,080
Speaker 5: performed international stocks, well, the US has invested in more

691
00:30:26,120 --> 00:30:28,120
Speaker 5: intangible assets. We have the best universities, we have the

692
00:30:28,120 --> 00:30:30,320
Speaker 5: best global brands, we have you know, so on and

693
00:30:30,400 --> 00:30:32,920
Speaker 5: so forth. That kind of makes sense, right, It explains

694
00:30:32,960 --> 00:30:37,440
Speaker 5: just the general absolute overvaluation of the market on traditional metrics. Well,

695
00:30:37,480 --> 00:30:39,360
Speaker 5: if you don't adjust for all the investment we've made

696
00:30:39,360 --> 00:30:41,040
Speaker 5: in these intangible assets, then yeah, of course the markets

697
00:30:41,040 --> 00:30:44,239
Speaker 5: always going to seem expensive. And so I basically use

698
00:30:44,320 --> 00:30:46,600
Speaker 5: that line of reasoning, you know, with some data of course,

699
00:30:46,640 --> 00:30:49,480
Speaker 5: to kind of show that, Yeah, when you adjust, I

700
00:30:49,520 --> 00:30:52,320
Speaker 5: think what Cliff showed was that the spread between value

701
00:30:52,320 --> 00:30:55,240
Speaker 5: and growth stocks, you know, just headline number was like

702
00:30:55,240 --> 00:30:58,040
Speaker 5: a two standard deviation, like really wide number. But once

703
00:30:58,120 --> 00:30:59,960
Speaker 5: what I show was that once you adjust for intangible,

704
00:31:00,280 --> 00:31:02,040
Speaker 5: it comes down just still being expensive, but maybe that

705
00:31:02,120 --> 00:31:04,600
Speaker 5: point five so within the range of noise. And that

706
00:31:04,640 --> 00:31:05,880
Speaker 5: was kind of the second point, which was, you know,

707
00:31:05,960 --> 00:31:09,440
Speaker 5: Cliff was arguing that you know, a widespread should mean

708
00:31:09,680 --> 00:31:12,440
Speaker 5: you know, high perspective returns, and you know, I actually

709
00:31:12,520 --> 00:31:14,400
Speaker 5: looked at one of the papers that he wrote, Cliff

710
00:31:14,400 --> 00:31:16,720
Speaker 5: and his co authors a few years ago, where we

711
00:31:16,760 --> 00:31:19,040
Speaker 5: actually showed that, you know, yes, at extremes it matters,

712
00:31:19,080 --> 00:31:23,960
Speaker 5: but really within this middle band it's kind of not statistically.

713
00:31:23,240 --> 00:31:24,040
Speaker 4: You know, meaningful.

714
00:31:24,400 --> 00:31:26,800
Speaker 5: Right, So the conclusion being that, all right, well it's

715
00:31:26,880 --> 00:31:28,680
Speaker 5: not that wide, then, you know, should we be really

716
00:31:28,720 --> 00:31:30,000
Speaker 5: kind of pounding the table today?

717
00:31:30,240 --> 00:31:33,320
Speaker 3: This is fascinating because what quants do is they take

718
00:31:33,360 --> 00:31:36,640
Speaker 3: what's work for active where they found alpha, and they

719
00:31:36,680 --> 00:31:39,440
Speaker 3: turn it into beta. So like values said, oh, over

720
00:31:39,480 --> 00:31:41,520
Speaker 3: the years, this person just outperformed because they just went

721
00:31:41,560 --> 00:31:43,400
Speaker 3: to cheap stocks. So they're like, oh, we'll just make

722
00:31:43,400 --> 00:31:45,640
Speaker 3: an index out of that. Bam, now that's done. They

723
00:31:45,640 --> 00:31:48,080
Speaker 3: did it with quality they did it with we'll say

724
00:31:48,120 --> 00:31:51,480
Speaker 3: momentum they did it with size. Intentional value does seem

725
00:31:51,520 --> 00:31:54,000
Speaker 3: like that latest thing, like what have the people been

726
00:31:54,080 --> 00:31:56,640
Speaker 3: leaning on to get that out performance in mojo? Like

727
00:31:56,680 --> 00:31:58,320
Speaker 3: how do you explain the cues being the S and

728
00:31:58,400 --> 00:32:01,520
Speaker 3: P all the time you take intangible value, it probably

729
00:32:01,560 --> 00:32:04,440
Speaker 3: does go in line a little more and explain it.

730
00:32:04,440 --> 00:32:07,040
Speaker 3: It makes you think, if this is a true factor

731
00:32:07,080 --> 00:32:09,320
Speaker 3: and you've now captured it and turn it into beta,

732
00:32:10,360 --> 00:32:11,440
Speaker 3: is there any alpha left?

733
00:32:12,960 --> 00:32:13,640
Speaker 1: What else can you do?

734
00:32:13,760 --> 00:32:15,360
Speaker 4: There's always going to be more alpha out there.

735
00:32:16,080 --> 00:32:17,680
Speaker 5: Look, I mean, what we're trying to do is, as

736
00:32:17,720 --> 00:32:19,760
Speaker 5: you point out, just like trying to capture what is

737
00:32:19,760 --> 00:32:21,840
Speaker 5: it that a smart invester would do, like a smart

738
00:32:21,880 --> 00:32:24,600
Speaker 5: fundamental guy at like a top edge fund, what sorts

739
00:32:24,600 --> 00:32:26,080
Speaker 5: of things where they look at when they evaluate a

740
00:32:26,080 --> 00:32:27,760
Speaker 5: copy of like Disney or in the video or these

741
00:32:27,760 --> 00:32:30,240
Speaker 5: are just like kind of common sense things that to

742
00:32:30,320 --> 00:32:32,760
Speaker 5: the extent where we can use AI, we can use

743
00:32:32,840 --> 00:32:35,600
Speaker 5: all the new data available to make it into beta,

744
00:32:35,640 --> 00:32:38,320
Speaker 5: to make it into a systematic factor. That's good, But

745
00:32:38,360 --> 00:32:40,640
Speaker 5: then you know, the the smart guys, once it is

746
00:32:40,680 --> 00:32:42,600
Speaker 5: table stakes will find the next thing to lean on,

747
00:32:42,760 --> 00:32:43,160
Speaker 5: right and I have.

748
00:32:43,400 --> 00:32:44,080
Speaker 1: What's the next thing?

749
00:32:44,200 --> 00:32:45,800
Speaker 4: I don't know. I mean if I knew, then you

750
00:32:45,840 --> 00:32:46,400
Speaker 4: know it would.

751
00:32:46,240 --> 00:32:51,200
Speaker 1: Be you wouldn't be here. Yeah, exactly whatever, yea, exactly.

752
00:32:52,080 --> 00:32:53,160
Speaker 2: All right, we're gonna leave it there.

753
00:32:53,200 --> 00:32:53,400
Speaker 7: Kai.

754
00:32:54,120 --> 00:32:57,280
Speaker 2: One final question. Uh, it's a question we ask everyone

755
00:32:57,320 --> 00:32:59,520
Speaker 2: on the on the program. Uh, what is your favorite

756
00:32:59,560 --> 00:33:01,160
Speaker 2: ETF ticker other than your own?

757
00:33:01,720 --> 00:33:03,320
Speaker 4: Oh?

758
00:33:03,360 --> 00:33:06,760
Speaker 1: I know what he's gonna pick. I just know, go ahead.

759
00:33:06,960 --> 00:33:08,400
Speaker 5: Well, I don't think I don't I don't think what

760
00:33:08,520 --> 00:33:10,480
Speaker 5: my opinion is matters. I think what matters is what

761
00:33:10,520 --> 00:33:13,600
Speaker 5: the market would say, and the market would say, M

762
00:33:13,640 --> 00:33:16,280
Speaker 5: E T A meadow It's like an eight figure ticker.

763
00:33:16,160 --> 00:33:20,479
Speaker 3: Right, answered, like a true quant Well, meta is the

764
00:33:20,760 --> 00:33:23,520
Speaker 3: is the ticker that was sold to Martin. So yeah,

765
00:33:23,560 --> 00:33:25,400
Speaker 3: you're right, that is the most valuable ticker.

766
00:33:25,560 --> 00:33:25,760
Speaker 4: Right.

767
00:33:25,800 --> 00:33:27,920
Speaker 1: So I don't know why will hersh she is still

768
00:33:27,920 --> 00:33:29,520
Speaker 1: working around him. I don't understand that.

769
00:33:29,640 --> 00:33:31,440
Speaker 3: Yeah, that was that was talking about a guy whohould

770
00:33:31,440 --> 00:33:33,800
Speaker 3: be on an island somewhere. Yeah, your that's a very

771
00:33:33,840 --> 00:33:35,040
Speaker 3: smart answer, by the way.

772
00:33:34,920 --> 00:33:35,920
Speaker 4: So he So here's my thing.

773
00:33:36,200 --> 00:33:45,120
Speaker 5: If if Tim Cook wants a rebrand Apple as Itan, Yeah.

774
00:33:43,600 --> 00:33:46,560
Speaker 2: All right, Uh, Kai, Chris, thanks for joining us in trillion.

775
00:33:46,800 --> 00:33:55,560
Speaker 2: Thank you, Thank you, thanks for listening to Trillions. Until

776
00:33:55,600 --> 00:33:57,760
Speaker 2: next time. You can find us on the Bloomberg Terminal,

777
00:33:58,080 --> 00:34:02,760
Speaker 2: Bloomberg dot com, Apple Podcast, Spotify, or wherever else you'd

778
00:34:02,800 --> 00:34:05,400
Speaker 2: like to listen. We'd love to hear from you. We're

779
00:34:05,440 --> 00:34:09,839
Speaker 2: on Twitter, I'm at Joel Webber Show. He's at Eric Balcuna's.

780
00:34:11,000 --> 00:34:14,920
Speaker 2: This episode of Trillions was produced by Magnus Hendrickson. Bye