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Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast.

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Speaker 1: I'm Joe Wisn't Thal and I'm Tracy Hallaway. Tracy, you know,

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Speaker 1: one of the things that we discussed a fair number

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Speaker 1: of times but also maybe before. It's just sort of

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Speaker 1: the I guess you would say permanent state of crisis

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Speaker 1: that you might call it in so called value investors. Yes,

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Speaker 1: it's been. It's certainly been a long running theme, hasn't it.

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Speaker 1: And I feel like we've done quite a few episodes

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Speaker 1: on it at this point, but didn't exactly turn things

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Speaker 1: around for value investors either. No, definitely not. And I think,

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Speaker 1: like you know, there's really two strands that tend to

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Speaker 1: emerge in our conversations, and I would characterize them is

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Speaker 1: this one is that there is some like cyclical element

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Speaker 1: to value investing in this cycle, it just hasn't turned

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Speaker 1: around yet. So it's like we have this sort of

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Speaker 1: long multiple expansion. It's kind of low growth fed driven

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Speaker 1: economy that sort of creates this permanent bid for growth

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Speaker 1: companies at the expensive value week GDP growth, etcetera. Just

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Speaker 1: like certain industries aren't going to thrive in that environment.

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Speaker 1: And then I would say the other half is that no,

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Speaker 1: the problem is that we are not good at defining

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Speaker 1: value in their people sort of take a more accounting

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Speaker 1: approach and say traditional measures of the stocks cheapness, like

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Speaker 1: say price to book value. It just these measures don't

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Speaker 1: work in a world where so much value is intangible. Right.

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Speaker 1: This is the idea that as companies, well the sort

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Speaker 1: of successful companies nowadays, are investing a lot, they're investing

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Speaker 1: a lot in things like their brands and other intangible

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Speaker 1: assets that just aren't captured by price to book value.

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Speaker 1: And so a company that's positioning itself for a really

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Speaker 1: good future performance might not actually look that way if

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Speaker 1: you're just glancing at, you know, something like price to

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Speaker 1: book you know, like it's sort of a joke, and

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Speaker 1: I think we've talked about it with Michael Mobison last

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Speaker 1: year Jared Witdard. But it's like you can rescue value

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Speaker 1: investing if you call Netflix of value stock, or if

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Speaker 1: you call Facebook a value stode, and someone would say, Okay,

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Speaker 1: these companies don't have like big like factories that can

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Speaker 1: easily be measured. But if you could somehow like put

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Speaker 1: a number on the value of the Facebook network as

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Speaker 1: an asset, then you couldn't, then you could theoretically imagine

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Speaker 1: a world in which had some traditional value screen Facebook

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Speaker 1: comes up right. I mean, the thing that still makes

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Speaker 1: me uncomfortable about value stocks is that you're you're still

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Speaker 1: investing in something on the basis that the market has

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Speaker 1: some how mischaracterized its future, which I think you touched

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Speaker 1: on this with with your two narratives. But I think

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Speaker 1: in the current environment, where we talk a lot a

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Speaker 1: lot about flows based investing, a lot of momentum trading

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Speaker 1: things like that, it feels like the market is quite

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Speaker 1: consistently directing capital to you know, a few firms, and

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Speaker 1: then it just keeps doing that and those firms get

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Speaker 1: overvalued and overvalued and overvalued. And if you're not in

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Speaker 1: that cycle, I don't know. I just feel like it's

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Speaker 1: unlikely that you're going to get back in it. And

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Speaker 1: the longer it takes you to get in, the more

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Speaker 1: you're sort of losing out in terms of valuations. But anyway, sorry,

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Speaker 1: I'm going on a tangent. No, No, it's great, And

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Speaker 1: I mean, and the other thing, and I think it's

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Speaker 1: sort of related to your point, is you know, it's

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Speaker 1: nice to say like, okay, like there's some intangible value

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Speaker 1: that we if we could only measure it at Netflix

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Speaker 1: or Facebook or whatever it is, but it seems hard

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Speaker 1: to know in advance that is there. And so it's like, okay,

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Speaker 1: if you have some like factory, then you can at

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Speaker 1: least say, okay, this exists, and historically speaking, it will uh,

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Speaker 1: you know, we would project it over the next ten years.

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Speaker 1: It's going to throw up this cash and that's a

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Speaker 1: good value. It feels like with a lot of these

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Speaker 1: sort of like backing into the value approach that it's

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Speaker 1: very much easier to say in retrospect or expose factor, oh,

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Speaker 1: this turned out to be a very valuable asset that

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Speaker 1: they have, which is sort of nice, I guess from

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Speaker 1: maybe an intellectual standpoint, but it doesn't really help you

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Speaker 1: like pick stocks today, which is what people to really

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Speaker 1: care about these discussions. It's great to say, Okay, the

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Speaker 1: Facebook network is worth a lot of money, but you know,

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Speaker 1: I wish you had told me that six years ago. No,

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Speaker 1: but it does help a lot of different investment companies

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Speaker 1: come up with different strategies and factors to come up

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Speaker 1: with different definitions of value that always work when they're

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Speaker 1: back tested against historical data. Right. So I guess the

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Speaker 1: question is that sarcasm. I don't know my sarcasm is

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Speaker 1: coming through on the podcast, but I got it crazy.

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Speaker 1: But I guess the thing we're looking for is the

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Speaker 1: approach that works in advance, that's not just back tested

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Speaker 1: and some you know, some approach that can help us

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Speaker 1: identify the sort of deeply under a company intervalue companies

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Speaker 1: today using some something whether it's a metric, whether it's

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Speaker 1: a screen, whether it's some other intangible measure, something that

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Speaker 1: doesn't just help us rationalize the past, but can you know,

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Speaker 1: help help me retire a few years earlier. Yeah, and

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Speaker 1: also maybe even explain why value investing as it's you know,

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Speaker 1: commonly understood has performed so badly for so long. Absolutely

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Speaker 1: all right, So I'm very excited. We're going to be

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Speaker 1: speaking with someone today who may be able to help

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Speaker 1: move this forward, whose work consists of sort of solving

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Speaker 1: this problem. An investor, so obviously someone who wants to

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Speaker 1: do more than just explain the past but produce good

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Speaker 1: returns going forward. We're going to be speaking with Rafael Rassindez.

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Speaker 1: He is the co founder of Applied Finance Capital Management,

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Speaker 1: which has looked at some of these problems and try

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Speaker 1: to identify where the traditional and value investing framework has

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Speaker 1: gone wrong. So, Rafael, thank you very much for joining us.

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Speaker 1: Great to be here. So I'm curious, I mean, uh,

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Speaker 1: you know, I always like to look for validation after

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Speaker 1: our introest But that general framework of sort of the

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Speaker 1: two stories that people tell about value investing, how does

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Speaker 1: that comport with how you see the world right now

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Speaker 1: and how you see this sort of ongoing debate. Great,

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Speaker 1: great question. So as you, as you know through some

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Speaker 1: of our exchanges, applied finance focuses on valuation. I think

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Speaker 1: to some degree the term value has been hijacked historically

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Speaker 1: with with the advent of the release of the FAMA

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Speaker 1: French three factor model back in ninety two and the

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Speaker 1: creation of this quote value factor, otherwise known as the

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Speaker 1: book to price ratio. I think the term value has

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Speaker 1: really been hijacked because book to price, at the end

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Speaker 1: of the day, is really nothing more than a cheapness metric,

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Speaker 1: as are all the price to something measures. It's measuring

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Speaker 1: the price of a stock in relation to some fundamental variable,

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Speaker 1: whatever that variable is, or even composites of that variable.

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Speaker 1: But it doesn't necessarily relate to the worth of a stock,

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Speaker 1: and I think that fundamentally is where things have really

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Speaker 1: tended to diverge or go off the rails to some degree.

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Speaker 1: In investing finance, the focus has been on cheapness, and

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Speaker 1: there hasn't been enough attention paid to understanding intrinsic value

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Speaker 1: through more complete valuation approaches, which is what we've specialized

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Speaker 1: in since n SO Applied finance has been in existence

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Speaker 1: since nineteen ninety five. But we were chatting before we

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Speaker 1: started recording the podcast, and you mentioned that your current

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Speaker 1: area of focus is something that grabbed your interest because

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Speaker 1: of a big debate that was happening in the nineties seventies.

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Speaker 1: Could you maybe explain that a little bit? Sure? Sure, so,

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Speaker 1: let me just provide a little historic background on how

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Speaker 1: we are where we are today. If you think about

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Speaker 1: how finance has evolved, you know, in the sixties you

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Speaker 1: had a lot of theoretical work on the CAPTEM model,

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Speaker 1: which was really exciting, and then in the early seventies

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Speaker 1: you started to see this basically this brand new field

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Speaker 1: of finance created by Eugene Fama, which is empirical finance,

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Speaker 1: and he did amazing work testing and kind of formulating

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Speaker 1: the efficient market hypothesis and ushering a whole new era

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Speaker 1: of scientific method applied to finance, so to speak. And

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Speaker 1: I think prior to that, for the most part, university

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Speaker 1: level finance focused on net present value and a lot

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Speaker 1: of security analysis. And in a great paper that Farmer wrote,

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Speaker 1: I believe it's titled you know, My Memories and Finance,

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Speaker 1: or something to that extent, he essentially says, you know,

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Speaker 1: when I arrived at Chicago, finance consisted of classes essentially

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Speaker 1: teaching people to pick stocks, and in in an efficient

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Speaker 1: market world, there's not a lot of value to having

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Speaker 1: so many people specialized in that. And what we saw

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Speaker 1: in the seventies was this birth of mean variance finance,

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Speaker 1: where computer methods and data availability allowed for incredible study

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Speaker 1: of properties of stock prices, testing of hypotheses of you know,

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Speaker 1: a lot of the mainstream thoughts back then we're biased

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Speaker 1: stock just because it's going up. So a lot of

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Speaker 1: efficient market tests focused on momentum investing, which is kind

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Speaker 1: of ironic given the crave of momentum investing now you know,

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Speaker 1: thirty years later, fifty years later, um. And ultimately it

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Speaker 1: drove security analysis to a large degree out of finance,

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Speaker 1: and you see a lot of security analysis of courses

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Speaker 1: now being taught in accounting departments. And what we tried

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Speaker 1: to do had applied finance when we started in was

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Speaker 1: essentially try to link this notion of mean variance finance

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Speaker 1: and security analysis. And we did that through systematically constructing

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Speaker 1: security analysis rules to process data on companies and then

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Speaker 1: ultimately linking that the output of that those reformulated income

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Speaker 1: statements and balance sheets from accounting data into more of

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Speaker 1: an economic framework through the calculation of an economic margin,

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Speaker 1: which essentially is a firm's return on investment less it's

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Speaker 1: cost a capital. We linked that with expected capital growth, risk,

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Speaker 1: and competition to value a company. And so we did

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Speaker 1: this work from the seventies to ninety five, kind of

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Speaker 1: a twenty five year what I'll call an observation window.

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Speaker 1: And I think, Tracy, going back to one of your

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Speaker 1: statements you made earlier about back testing, it'd be fun

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Speaker 1: to get into that. It'll a little bit more depth later,

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Speaker 1: but so I'll call it an observation window rather than

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Speaker 1: an evidence window. I think there's a big distinction between

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Speaker 1: the two. And beginning in nineteen we began calculating intrinsic

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Speaker 1: value estimates. First for US companies on an ad hoc basis,

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Speaker 1: i'd say monthly and quarterly. When we were early on

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Speaker 1: in our company development, we were doing a lot of

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Speaker 1: corporate consulting work. We didn't have the full uh infrastructure

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Speaker 1: of personnel to handle calculating what i'll call productions of

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Speaker 1: intrinsic value consistently. By the company had grown, and we

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Speaker 1: began calculating these intrinsic values monthly, and from through today

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Speaker 1: we continue doing that. Now we do it globally on

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Speaker 1: twenty thousand companies every week. But what's interesting is back

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Speaker 1: test observations versus evidence. Since ninety eight, these have all

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Speaker 1: been live out of sample estimates of intrinsic value. And

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Speaker 1: I think one of the one of the interesting things

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Speaker 1: about our data set and what we used to prepare

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Speaker 1: this paper called Valuation Beta, is that a lot of finances, well,

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Speaker 1: let's go back to nineteen sixty three, and someone will

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Speaker 1: say no, let's go back to nineteen thirty and someone

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Speaker 1: will say no, I have a data series going back

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Speaker 1: to the seventeen hundreds. Well, at some point this data

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Speaker 1: is really irrelevant because the world has changed so much.

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Speaker 1: Maybe there are these quote fundamental truths, but if you

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Speaker 1: look at the factor work. Oftentimes these factors are discovered

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Speaker 1: in a back test setting and then when they go

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Speaker 1: live they don't work. And certainly that's been an important

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Speaker 1: part of the experience with Book to Price since it's

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Speaker 1: had prior to ninety two. From six to ninety one,

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Speaker 1: it was basically a money making machine. The return attributes

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Speaker 1: of that variable or extraordinary. Since ninety two it's had

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Speaker 1: a much more spotty record, and the same as as

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Speaker 1: if we dig down a little deeper on some of

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Speaker 1: these other factor metrics, such as the profitability factor, the

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Speaker 1: investment factor. Since they were really Eastern publicly, they've had

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Speaker 1: a spotty or record as well. So I think it's important,

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Speaker 1: as you said, to differentiate between back testing, which is

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Speaker 1: when you're kind of formulating your your ideas, versus evidence,

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Speaker 1: which happens after you've released your ideas and you you

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Speaker 1: let the let the world run. As Mike Tyson likes

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Speaker 1: to say, everyone has a plan to you're punched in

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Speaker 1: the face, and for Book to Price and value investing,

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Speaker 1: that punch in the face has happened over the last

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Speaker 1: decade when it basically hasn't worked. H So you make

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Speaker 1: this distinction between value valuation versus cheapness, and as you

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Speaker 1: characterize that measures of price to book or really any

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Speaker 1: other sort of ratio based valuation, it's not to measure

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Speaker 1: of value per se, it's just a measure of cheapness.

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Speaker 1: So can you explain a little bit further, like what

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Speaker 1: the differences between what your measure of valuation and cheapness

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Speaker 1: and why it is in your view that some of

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Speaker 1: these traditional metrics measures of cheapness that at one point

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Speaker 1: did produce returns have failed to do. So let me

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Speaker 1: focus first on explaining our worldview and kind of how

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Speaker 1: we get we get to an answer. And there's there's

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Speaker 1: probably lots of reasons to speculate about why something doesn't work,

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Speaker 1: but I'll offer our view of it as well. So, first,

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Speaker 1: as opposed to beginning with some metric of cheapness, what

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Speaker 1: we begin our discussion with is an estimate of intrinsic value.

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Speaker 1: So how do we go from accounting data to an

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Speaker 1: estimate of affirms intrinsic values. So we begin we process

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Speaker 1: the data, and there's a lot of around the world,

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Speaker 1: there's a lot of accounting issues that we've dealt with

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Speaker 1: and tried to incorporate so dramatically in our analysis, but

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Speaker 1: I'll just talk about a few that I think make

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Speaker 1: for for a quick, interesting discussion. Since since the beginning

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Speaker 1: of our of our company, we've viewed research and development

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Speaker 1: as an investment rather than an expense. So since we've

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Speaker 1: capitalized R and D, and our view is this is

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Speaker 1: a piece of operations based cash flow the company is

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Speaker 1: generating currently. Another item is the use of operating leases.

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Speaker 1: You know, the accountants historically have treated that as an

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Speaker 1: expense rather than anything having to do with the balance sheet. Systematically,

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Speaker 1: in the last couple of years, fast he has kind

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Speaker 1: of come around to our view of thinking and as

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Speaker 1: required companies to start capitalizing operating leases. We want to

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Speaker 1: view companies on a on a capital structure free basis.

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Speaker 1: So we're going to add back interest expense on an

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Speaker 1: after tax basis, We're going to make adjustments to the

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Speaker 1: balance sheet for inflation. It sounds a little silly now,

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Speaker 1: and certainly is silly for technology companies that have very

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Speaker 1: little physical plant, but for industrial firms that have assets

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Speaker 1: on their books that they acquired in the seventies, inflation

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Speaker 1: is a big deal because it's easy to have inflated

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Speaker 1: return on investments because the balance sheets reflecting historic cost

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Speaker 1: the income is respecting is displaying current values, current dollars values.

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Speaker 1: So there's a litany of adjustments we go through. We

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Speaker 1: do all that, and one last thing that we we

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Speaker 1: account for is how long the company's assets last on

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Speaker 1: average to get an asset life. So then we can

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Speaker 1: configure a firm as a project and we can calculate

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Speaker 1: what we call an economic margin. We don't have to

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Speaker 1: get into all the geek speak on that, but essentially,

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Speaker 1: at the end of the day, what we end up

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Speaker 1: with is a corrected return on investment on an inflation

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Speaker 1: adjusted asset based minus acost of capital. That spread or

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Speaker 1: economic margin, starts to tell us a lot of things

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Speaker 1: about the company. First of all, if you have an

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Speaker 1: economic margin that's negative, it tells you the firm is

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Speaker 1: investing in projects below its cost of capital. So the

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Speaker 1: last thing we'd want to see a firm like that

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Speaker 1: do is grow its business. That's what we call wealth destroyers,

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Speaker 1: and it's it's a very key piece of how we

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Speaker 1: think about analyzing companies and what they're doing. If you

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Speaker 1: have a negative spread to your cost of capital. You

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Speaker 1: should be shrinking your business or rationalizing what you have

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Speaker 1: to figure out how to at least get to a

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Speaker 1: zero spread. Companies that have a positive spread, and again

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Speaker 1: this is after accounting for things such as investments in

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Speaker 1: R and D off, balance sheet releases, on and so on.

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Speaker 1: Companies that have a positive spread should grow. And I

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Speaker 1: think Joe in one of your tweets a while back

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Speaker 1: that that initiated our discussions, you asked a question about Monster,

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Speaker 1: and you said, can anyone explain Monster to us? The

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Speaker 1: Monster story is pretty simple. This is a firm that

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Speaker 1: some people know. This is the the energy drink maker

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Speaker 1: with lots of caffeine, that's the best performing stock. Go on,

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Speaker 1: I just wanted to, uh, just for people who are great, great,

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Speaker 1: it's been an incredible wealth compounding company through the years.

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Speaker 1: It's not a it's not a recent thing. You know.

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Speaker 1: This is a firm that's earning ten to fift percent

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Speaker 1: returns above its cost of capital and has been growing

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Speaker 1: its capital based at double digits. So you have This

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Speaker 1: is a classic example of what we call a wealth creator,

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Speaker 1: a compound wealth creator generally reinvesting in high rates of return,

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Speaker 1: creating more and more wealth for the existing shareholders, and

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Speaker 1: the company continues growing into its valuation. I think we

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Speaker 1: sent you a chart that traces out our estimate of

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Speaker 1: Monsters intrinsic value through time, and again these are basically

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Speaker 1: live estimates going back. This is in this case going

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Speaker 1: back to ten years at least, but it traced out

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Speaker 1: our intrinsic value for Monster relative to its traded price.

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Speaker 1: And what's interesting is this thought consistently was trading at

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Speaker 1: or below its intrinsic value, and even recently with the

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Speaker 1: big runs it has, it's not grotesquely overvalued from our perspective,

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Speaker 1: which is what what leads to the tension between the

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Speaker 1: way we view the world from an intrinsic value perspective.

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Speaker 1: Intrinsic values of function of this level of economic profitability

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Speaker 1: how much you're able to reinvest in the business to

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Speaker 1: create more economic profits. Then we discount that back to

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Speaker 1: reflect the firm's risk based on its size and its

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Speaker 1: leverage characteristics. And then lastly we incorporate an overlay. And

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Speaker 1: this is where we diverge quite a bit from traditional

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Speaker 1: DCF methods that say, okay, we're gonna go out five, six,

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Speaker 1: seven years, and then put a terminal value or used

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Speaker 1: to Gordon growth model to assume that the rest of

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Speaker 1: the world is kind of static. We're going to capitalize

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Speaker 1: everything back via perpetuity. We think that's kind of a

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Speaker 1: crazy assumption, and you can just talk to kmart about

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Speaker 1: how valid the world being constant is. And we apply

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Speaker 1: what we call an economic profit horizon, which, again based

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Speaker 1: on the research we did, we assign every firm a

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Speaker 1: factor based on fundamental characteristics that say, how long will

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Speaker 1: this economic profit persist? How long can we expect this

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Speaker 1: firm to have an economic margin greater than zero. Because

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Speaker 1: once the margin, once your returnees, the cost of capital

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Speaker 1: just present value. Math says from that point forward growth

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Speaker 1: is irrelevant because the net present value of future growth

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Speaker 1: of zero. So all of that a lot of different

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Speaker 1: complicated concepts going on at once behind each level, but

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Speaker 1: at its most basic, we're basically we're saying, let's figure

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Speaker 1: out what the true economic return of a firm is.

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Speaker 1: Let's get a handle on how much it's reinvesting in itself.

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Speaker 1: Let's let's get some idea on how risky these cash

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Speaker 1: flows are, so that from a from an a risk

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Speaker 1: adjusted basis, we can compare companies on an apples to

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Speaker 1: Apple's perspective. And then let's say companies are not going

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Speaker 1: to earn economic profits forever because of competition, so we

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Speaker 1: combine those to get an estimate for intrinsic value for

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Speaker 1: twenty companies around the world every week. That becomes the

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Speaker 1: basis for us looking at the world and then sorting

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Speaker 1: out portfolios of companies and Dubai's into cells or creating,

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Speaker 1: you know, slicing and dicing that to create subsets of

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Speaker 1: products for our clients. What do the tech stocks look

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Speaker 1: like under that framework? You know, things like an Apple

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Speaker 1: or Facebook or a Netflix. I think it's always useful

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Speaker 1: when we're talking about differences in how we're measuring intrinsic

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Speaker 1: value to actually speak about concrete examples. And the Monster

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Speaker 1: one was a great example just then. But I'm thinking

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Speaker 1: something like Apple, you have a lot of profits, a

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Speaker 1: lot of investment of very low cost of capital. Does

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Speaker 1: it look good in your method of intrinsic value? Let

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Speaker 1: me go back a little bit in time as well. Uh,

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Speaker 1: and again not in the context of a back test

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Speaker 1: or observation, but in context of kind of our our

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Speaker 1: main strategy portfolio. If we go back to eleven, and

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Speaker 1: this is this is an example that week. We presented

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Speaker 1: to some clients the other day because they're kind of

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Speaker 1: asking the same type of question. In eleven, we purchased three.

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Speaker 1: This is a very low turnover portfolio averages less than

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Speaker 1: ten percent turnover a year. But in twenty eleven we

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Speaker 1: made three purchases in the tech space, Alphabet and Vidia

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Speaker 1: and master Card. Okay, at the time, they were trading

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Speaker 1: on a multiple basis well above the market median, and

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Speaker 1: from a multiple perspective, those multiples only grew more and

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Speaker 1: more expensive relative to the market through time. I think

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Speaker 1: we added Apple to the portfolio. Uh five years ago.

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Speaker 1: I think we added Facebook to the portfolio. So we

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Speaker 1: own all of those stocks and they've all been attractive

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Speaker 1: to us from an intrinsic value perspective. In Video, we

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Speaker 1: bought in twenty eleven and thirteen dollars a share I

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Speaker 1: think in eighteen or at the end of the year

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Speaker 1: crashed from three hundred to one fifty. People thought we

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Speaker 1: were nuts to have owned it at three hundred. I

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Speaker 1: remember having a conversation with a with a really prominent

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Speaker 1: journalist and he's like, you guys are crazy. How can

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Speaker 1: you call that a stock that's undervalued at one fifty

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Speaker 1: after the crash, We're like, we believe it's undervalue, We're

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Speaker 1: gonna own it, and then we just sold that this

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Speaker 1: past August at We continue to own Google, Apple, Facebook, MasterCard,

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Speaker 1: um at the same time, we own what you consider

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Speaker 1: to be some classic value stocks. We own Intel, we

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Speaker 1: own Hewlett Packard, We own Financials, which is a big

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Speaker 1: chunk of the of the value universe, so its valuation.

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Speaker 1: This whole dichotomy of value versus growth is really a

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Speaker 1: false way to think about firms, because every firm is growing,

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Speaker 1: whether it's positive or negative. There's growth taking place for

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Speaker 1: every firm. And you know Warren Buffett obviously super successful,

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Speaker 1: wise guy. Everything is a function of what you're paying

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Speaker 1: for in the market. Please, you can have all the

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Speaker 1: stocks that represent an incredible investment opportunity just as easily

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Speaker 1: as you can have a stock like Monster represent an

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Speaker 1: incredible investment opportunity. It really is a function value. Ultimately,

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Speaker 1: true value becomes the intersection of economic profitability, growth, competition,

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Speaker 1: and risk. The attractiveness of that is a function of

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Speaker 1: what the market's paying for or how psychotic the market

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Speaker 1: is at a point in time for any of these stocks.

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Speaker 1: Does that make sense? Yeah, I mean talk to us

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Speaker 1: further like expanded, pick a pick a name, like okay,

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Speaker 1: in video, you tell the story you got it super cheap,

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Speaker 1: it's soared. It had that brief crash, I remember that,

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Speaker 1: but then it climbed back up in an extraordinary like

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Speaker 1: walk is through like what did you see? What year

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Speaker 1: did you say you first bought it? Okay, So now

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Speaker 1: everyone knows the narrative around and video and video games

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Speaker 1: and AI and automotive chips and stuff like that, Like

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Speaker 1: what was it at that you saw and were able

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Speaker 1: to identify as a you know, it's so great perspective.

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Speaker 1: So in video was basically a graphics chip producer, right,

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Speaker 1: And this is an interesting view of how the world

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Speaker 1: is continually changing around companies and around data. A graphics

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Speaker 1: chip producer that we thought was very attractively priced relative

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Speaker 1: to its underlying fundamentals. And then over time it started

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Speaker 1: to evolve and the at that early in the early

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Speaker 1: stages that we owned it, it was it continued to

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Speaker 1: be a graphics chip producer designer, if you will, because

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Speaker 1: a lot of a lot of these chip makers are

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Speaker 1: fabulous now they don't produce but a graphics chip designer,

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Speaker 1: and that continued to grow and evolve, and then all

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Speaker 1: of a sudden, we started to get little indicators of

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Speaker 1: AI and technology continued blossoming in the story. AI story

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Speaker 1: became sting for us. The reason we were able to

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Speaker 1: stay with it is because as much as it was

432
00:26:05,720 --> 00:26:09,440
Speaker 1: increasing in value because of AI, the company was continually

433
00:26:09,520 --> 00:26:15,639
Speaker 1: delivering on its investments. And even without our analysts modeling

434
00:26:15,720 --> 00:26:19,760
Speaker 1: the company, the company continued to look attractive with our

435
00:26:19,800 --> 00:26:22,560
Speaker 1: systematic valuations that we do for every stock, so just

436
00:26:22,720 --> 00:26:27,920
Speaker 1: on pure as reported data converted into our economic margin

437
00:26:28,040 --> 00:26:30,479
Speaker 1: framework and then an intrinsic value, the company was very

438
00:26:30,520 --> 00:26:33,119
Speaker 1: attractive for years and years and years and years. And

439
00:26:33,160 --> 00:26:37,920
Speaker 1: it wasn't until that these stocks exploded so much after

440
00:26:38,040 --> 00:26:41,520
Speaker 1: that march to client that we painful. We had to

441
00:26:41,680 --> 00:26:43,800
Speaker 1: part ways. You know, we prefer to never sell a

442
00:26:43,880 --> 00:26:47,840
Speaker 1: company a lot of our turnovers because these companies get acquired,

443
00:26:47,880 --> 00:26:49,920
Speaker 1: which is which is kind of like a happy and

444
00:26:50,040 --> 00:26:53,240
Speaker 1: sorrowful moment for us, because we own them. For a reason,

445
00:26:53,280 --> 00:26:55,600
Speaker 1: we only owned fifty stocks in this portfolio, and we

446
00:26:55,680 --> 00:27:00,600
Speaker 1: own across all the sectors, and we're jealous when someone

447
00:27:00,720 --> 00:27:02,760
Speaker 1: buy the stock out below our estimate of it's an

448
00:27:02,880 --> 00:27:06,080
Speaker 1: intrinsic value. It's nice to get the immediate buzz for performance,

449
00:27:06,160 --> 00:27:09,320
Speaker 1: but it really is is kind of annoying that that

450
00:27:09,600 --> 00:27:13,119
Speaker 1: someone's getting such a good deal on our back on

451
00:27:13,280 --> 00:27:16,520
Speaker 1: that note, I'm trying to think how to phrase this question.

452
00:27:16,640 --> 00:27:21,800
Speaker 1: But I guess if you identify a stock that you

453
00:27:22,000 --> 00:27:28,439
Speaker 1: think is undervalued in some way, does that signal something

454
00:27:28,680 --> 00:27:32,840
Speaker 1: about the company, something sort of fundamental to the company,

455
00:27:33,000 --> 00:27:37,920
Speaker 1: that it will perform well in the future, or is

456
00:27:38,000 --> 00:27:43,080
Speaker 1: your measure of intrinsic value maybe correlated with something else

457
00:27:43,200 --> 00:27:47,159
Speaker 1: going on in the market, like a much larger factor.

458
00:27:47,400 --> 00:27:49,800
Speaker 1: I guess I'm trying to get to the flows argument

459
00:27:50,000 --> 00:27:52,800
Speaker 1: um that we briefly talked about in the intro. Does

460
00:27:52,840 --> 00:27:54,960
Speaker 1: any of that make sense? It does? And let me

461
00:27:55,040 --> 00:27:58,399
Speaker 1: take your correlated comment and if I may, let me

462
00:27:58,440 --> 00:28:00,639
Speaker 1: go off on a tangent just for a moment, and

463
00:28:00,720 --> 00:28:03,480
Speaker 1: then we can circle back if if, if, if, the

464
00:28:03,520 --> 00:28:06,760
Speaker 1: conversation returns there, and I'm sure it will. But I

465
00:28:06,840 --> 00:28:10,280
Speaker 1: think the question you're getting at is fundamentally, is intrinsic

466
00:28:10,440 --> 00:28:14,159
Speaker 1: value a causal variable or is it a correlated variable

467
00:28:14,200 --> 00:28:18,200
Speaker 1: with a much larger set of phenomenon. Right, Yes, thank you.

468
00:28:18,520 --> 00:28:20,399
Speaker 1: You put it much better than I did. No, not

469
00:28:20,560 --> 00:28:23,679
Speaker 1: at all, Not at all. The only reason I framed

470
00:28:23,720 --> 00:28:27,040
Speaker 1: it so so quickly is because we think about that

471
00:28:27,160 --> 00:28:30,200
Speaker 1: all the time, and I think it's particularly relevant with

472
00:28:30,400 --> 00:28:33,719
Speaker 1: respect to the cheapness literature. And if if you were

473
00:28:33,760 --> 00:28:37,360
Speaker 1: to think about taking book to price portfolios and intrinsic

474
00:28:37,480 --> 00:28:41,680
Speaker 1: value portfolios, right, and if you were to form groupings

475
00:28:41,760 --> 00:28:46,000
Speaker 1: of them, say the top thirty most undervalued companies, the

476
00:28:46,080 --> 00:28:50,040
Speaker 1: bottom most overvalued companies, and do the same thing with

477
00:28:50,160 --> 00:28:52,600
Speaker 1: a book to price portfolio, and then you have the middle,

478
00:28:53,120 --> 00:28:55,760
Speaker 1: the middle forty, which is some mix of fairly valued,

479
00:28:56,120 --> 00:28:59,880
Speaker 1: somewhat overvalued, somewhat undervalued. We can kind of test to

480
00:29:00,040 --> 00:29:04,880
Speaker 1: see what is driving what by decomposing and deconstructing these returns.

481
00:29:05,080 --> 00:29:07,320
Speaker 1: And if you take that approach and you look at

482
00:29:07,400 --> 00:29:10,920
Speaker 1: book to price, we only are looking at data that's

483
00:29:10,960 --> 00:29:14,160
Speaker 1: live for us. We're not looking at at any simulated

484
00:29:14,240 --> 00:29:17,760
Speaker 1: data or data that we were creating to kind of

485
00:29:17,880 --> 00:29:22,240
Speaker 1: calibrate our estimates of risk or estimates of of economic

486
00:29:22,320 --> 00:29:26,200
Speaker 1: profit horizon, only looking at data that we've produced live

487
00:29:26,320 --> 00:29:32,160
Speaker 1: consistently on a monthly basis. So going back to if

488
00:29:32,200 --> 00:29:34,720
Speaker 1: you look at book to price based stocks and intrinsic

489
00:29:34,840 --> 00:29:38,280
Speaker 1: value based stocks, I would argue that book to price

490
00:29:38,400 --> 00:29:41,560
Speaker 1: is not on a one three five tenure losing street.

491
00:29:41,600 --> 00:29:46,920
Speaker 1: Book to price since has added zero to the investing world.

492
00:29:47,080 --> 00:29:49,040
Speaker 1: And I say that because if you take a look

493
00:29:49,080 --> 00:29:53,200
Speaker 1: at those attractive book to price stocks and you separate

494
00:29:53,280 --> 00:29:56,600
Speaker 1: out the ones that are supported by intrinsic value. In

495
00:29:56,640 --> 00:30:00,320
Speaker 1: other words, those are also attractive intrinsic value stocks, the

496
00:30:00,440 --> 00:30:04,120
Speaker 1: resulting set of book to price stocks generate negative alpha.

497
00:30:05,640 --> 00:30:07,680
Speaker 1: And that's not again, that's not a one three five

498
00:30:07,760 --> 00:30:10,600
Speaker 1: year window, that's a twenty two year window. Book to price,

499
00:30:11,320 --> 00:30:14,960
Speaker 1: if it's not supported by intrinsic value, generates new alpha.

500
00:30:15,240 --> 00:30:18,800
Speaker 1: So then the flip question becomes, okay, well intrinsic value then,

501
00:30:19,440 --> 00:30:20,959
Speaker 1: and a lot of people will say, well, no one

502
00:30:21,080 --> 00:30:24,000
Speaker 1: can trade short against us. No one's going to short

503
00:30:24,080 --> 00:30:26,680
Speaker 1: the high multiple stocks against a value strategy, And that's

504
00:30:26,720 --> 00:30:30,000
Speaker 1: not really true. If you look at high multiple book

505
00:30:30,040 --> 00:30:34,440
Speaker 1: to value stocks that are undervalued, they generate significant positive alpha.

506
00:30:35,280 --> 00:30:39,480
Speaker 1: And so to me, the birth of book to price

507
00:30:40,760 --> 00:30:44,720
Speaker 1: is the result of confusing correlation and causality. Book to

508
00:30:44,800 --> 00:30:47,920
Speaker 1: price has done extraordinarily well. When when that section of

509
00:30:48,000 --> 00:30:52,320
Speaker 1: stocks correlated with intrinsic value do really well and outperform

510
00:30:52,400 --> 00:30:57,600
Speaker 1: the market and carry everyone else along, but by itself unsupported.

511
00:30:57,640 --> 00:30:59,760
Speaker 1: If you strip out the support of intrinsic value, the

512
00:31:00,040 --> 00:31:06,240
Speaker 1: meaning subset of attractive book to price stocks generate negative alpha,

513
00:31:06,360 --> 00:31:09,520
Speaker 1: and the absolute flip happens on the unattractive book to

514
00:31:09,560 --> 00:31:12,680
Speaker 1: price stocks. The only ones with negative alpha are those

515
00:31:12,760 --> 00:31:17,120
Speaker 1: that are really overvalued. The fairly or undervalued high multiple

516
00:31:17,160 --> 00:31:21,080
Speaker 1: stocks generate positive alpha. So this notion of correlation and

517
00:31:21,120 --> 00:31:23,440
Speaker 1: causality is really near and dear to our heart because

518
00:31:23,520 --> 00:31:27,800
Speaker 1: we see an entire industry that was birthed by confusing

519
00:31:27,840 --> 00:31:30,640
Speaker 1: correlation and causality. And that's, you know, one of the

520
00:31:30,720 --> 00:31:34,560
Speaker 1: messages that that we're getting out and we've just started.

521
00:31:34,760 --> 00:31:38,480
Speaker 1: We've just started this message in in earnest recently because

522
00:31:38,520 --> 00:31:43,200
Speaker 1: we just felt we didn't have enough data to construct

523
00:31:43,240 --> 00:31:46,560
Speaker 1: serious arguments. We've been accumulating this data out of sample

524
00:31:46,880 --> 00:31:49,440
Speaker 1: for twenty two years, and then a year and a

525
00:31:49,480 --> 00:31:52,360
Speaker 1: half ago we started to accelerate our efforts to begin

526
00:31:52,800 --> 00:31:55,920
Speaker 1: organizing it to do research the way kind of in

527
00:31:56,000 --> 00:31:58,240
Speaker 1: the FAMA French tradition just so that we have more

528
00:31:58,320 --> 00:32:01,920
Speaker 1: of a of an Apples to Apple comparison, so people

529
00:32:02,000 --> 00:32:04,960
Speaker 1: don't have to unscramble how we're organizing things. And then

530
00:32:05,040 --> 00:32:07,280
Speaker 1: with with the COVID crisis, that gave us a lot

531
00:32:07,320 --> 00:32:09,880
Speaker 1: of time to focus on that, and that's we completed

532
00:32:09,920 --> 00:32:13,000
Speaker 1: this work back in in September and release the paper

533
00:32:13,040 --> 00:32:16,640
Speaker 1: in October. But I think the correlation causality argument is

534
00:32:16,680 --> 00:32:21,000
Speaker 1: really interesting. I think also the quantitative investing world is

535
00:32:21,040 --> 00:32:24,280
Speaker 1: starting to come to grips with some notion of valuation.

536
00:32:24,440 --> 00:32:27,040
Speaker 1: If you look at how that work was extended by

537
00:32:27,120 --> 00:32:32,440
Speaker 1: former French and they motivated their research upfront with a

538
00:32:32,520 --> 00:32:37,160
Speaker 1: dividend discount model, and from that they derived a profitability

539
00:32:37,280 --> 00:32:41,720
Speaker 1: factor saying all all else equel af firms increase their profits,

540
00:32:42,320 --> 00:32:45,320
Speaker 1: they increase their value. And then they have a second

541
00:32:45,400 --> 00:32:47,800
Speaker 1: factor that they added, the investment factor, which I think

542
00:32:47,880 --> 00:32:50,880
Speaker 1: really missed the mark. But I think it's interesting virtually

543
00:32:50,960 --> 00:32:55,280
Speaker 1: everybody in the quantitative value space, any firm that seems

544
00:32:55,320 --> 00:32:58,320
Speaker 1: to be anybody, incorporates this investment factor into their work.

545
00:32:59,000 --> 00:33:01,840
Speaker 1: And what that missed the picture on and it says, okay,

546
00:33:01,920 --> 00:33:04,920
Speaker 1: firms that are investing in their business are expect to

547
00:33:05,000 --> 00:33:09,640
Speaker 1: generate negative future returns. And what that missed from evaluation

548
00:33:09,800 --> 00:33:13,880
Speaker 1: context is you can't separate investment without the return on

549
00:33:13,960 --> 00:33:16,960
Speaker 1: that incremental investment. And so of course, if you only

550
00:33:17,040 --> 00:33:19,040
Speaker 1: make an investment in the firm and it generates no

551
00:33:19,160 --> 00:33:23,800
Speaker 1: future returns, absolutely companies should never invest. But if you

552
00:33:23,880 --> 00:33:28,760
Speaker 1: had the brightest quantitative value investing minds in the world.

553
00:33:29,120 --> 00:33:34,440
Speaker 1: Back in talking to Jeff Bezos and he asked them,

554
00:33:34,720 --> 00:33:37,640
Speaker 1: you know, I'm thinking about these different extensions of my business.

555
00:33:38,160 --> 00:33:41,160
Speaker 1: If they're looking at the profitability factor, they'd say, all

556
00:33:41,200 --> 00:33:43,960
Speaker 1: that's great. But if you're looking at the investment factor,

557
00:33:44,000 --> 00:33:47,680
Speaker 1: they'd say, don't reinvest, don't expand beyond books, because they're

558
00:33:47,680 --> 00:33:50,520
Speaker 1: going to generate negative future returns. And to me, that's

559
00:33:50,520 --> 00:33:54,080
Speaker 1: a worldview that's just missing the wealth creation element that

560
00:33:54,160 --> 00:33:58,520
Speaker 1: comes from a complete valuation framework. Literally, you're biased against

561
00:33:58,640 --> 00:34:02,200
Speaker 1: the greatest in estments of the last sixty years. Not

562
00:34:02,400 --> 00:34:05,840
Speaker 1: tech companies necessarily, of course those are some, but you're

563
00:34:05,880 --> 00:34:09,560
Speaker 1: missing the McDonald's, the Walmarts that the targets, the fisers

564
00:34:10,080 --> 00:34:15,480
Speaker 1: until as a as a manufacturing slash tech company, Apple, Google, Facebook,

565
00:34:15,520 --> 00:34:18,000
Speaker 1: all of these firms have to make huge investments in

566
00:34:18,040 --> 00:34:20,399
Speaker 1: their business. And if you have a worldview that says

567
00:34:21,040 --> 00:34:25,719
Speaker 1: investing is bad, you automatically have at least one aspect

568
00:34:26,480 --> 00:34:31,879
Speaker 1: of your investing world view that's counter to the best

569
00:34:32,040 --> 00:34:34,719
Speaker 1: returning companies in the world. And I think that's just

570
00:34:35,120 --> 00:34:38,239
Speaker 1: a fundamental flaw on that approach. Why don't you know

571
00:34:38,360 --> 00:34:40,800
Speaker 1: when you say that, when you describe it, it sounds

572
00:34:40,880 --> 00:34:45,919
Speaker 1: so obvious that it's kind of weird to penalize these

573
00:34:46,000 --> 00:34:50,440
Speaker 1: companies that are investing in building future technologies and future things.

574
00:34:50,480 --> 00:34:55,200
Speaker 1: We can't conceive of what is their intuition in your view,

575
00:34:55,320 --> 00:34:58,719
Speaker 1: like because it doesn't intuitively, it doesn't seem to make

576
00:34:58,719 --> 00:35:02,080
Speaker 1: any sense at all. But I'm trying understand like accounter argument.

577
00:35:02,200 --> 00:35:05,879
Speaker 1: So the first argument is the data overwhelmingly says it's

578
00:35:05,920 --> 00:35:09,920
Speaker 1: true companies that invest underperformance. That that's an absolute fact

579
00:35:10,080 --> 00:35:14,120
Speaker 1: that I wouldn't argue that with anybody, But I would

580
00:35:14,160 --> 00:35:17,279
Speaker 1: say that's a very naive slice of the data that's

581
00:35:17,320 --> 00:35:22,799
Speaker 1: not accounting for economic profitability and cost of capital. So yes,

582
00:35:23,160 --> 00:35:27,120
Speaker 1: on aggregate, over these big periods of time, say sixty three,

583
00:35:28,640 --> 00:35:31,920
Speaker 1: if you looked at all firms that were reinvesting on aggregate,

584
00:35:32,480 --> 00:35:36,400
Speaker 1: the investment factor turned out to be negative. But I

585
00:35:36,440 --> 00:35:39,600
Speaker 1: don't think that's a really that's a complete view of

586
00:35:39,640 --> 00:35:43,480
Speaker 1: the world because I think it it naively misses the

587
00:35:43,600 --> 00:35:47,000
Speaker 1: key component evaluation, which is that investment gets reinvested to

588
00:35:47,080 --> 00:35:50,840
Speaker 1: generate future returns. So if you then segment companies based

589
00:35:50,920 --> 00:35:55,200
Speaker 1: on their economic margin, positive economic margin firms that are

590
00:35:55,239 --> 00:35:58,840
Speaker 1: growing versus negative economic margin firms that are growing, you

591
00:35:59,040 --> 00:36:03,759
Speaker 1: end up with distinctly different return profiles. We captured this.

592
00:36:04,640 --> 00:36:07,799
Speaker 1: We've been doing this notion of wealth creation and destruction

593
00:36:07,920 --> 00:36:12,240
Speaker 1: through what we call a management quality score for fifteen

594
00:36:12,280 --> 00:36:16,280
Speaker 1: plus years out of sample. Also, when we first created

595
00:36:16,320 --> 00:36:20,920
Speaker 1: this metric back somewhere in the two thousand's, we converted

596
00:36:20,960 --> 00:36:24,719
Speaker 1: this into a financing yield, with the story being, look,

597
00:36:24,800 --> 00:36:27,239
Speaker 1: there's a stewardship aspect to this, and when we look

598
00:36:27,280 --> 00:36:29,480
Speaker 1: at what companies are doing and able to finance their

599
00:36:29,520 --> 00:36:32,480
Speaker 1: business on their own versus having to turn to external capital,

600
00:36:33,760 --> 00:36:36,279
Speaker 1: that captures companies that are growing like an apple, but

601
00:36:36,400 --> 00:36:39,840
Speaker 1: also returning money back to shareholders, and that changes the

602
00:36:39,960 --> 00:36:44,520
Speaker 1: underlying characteristic of just saying growth is bad. Growth is bad,

603
00:36:44,719 --> 00:36:47,480
Speaker 1: but it has to be tempered by what's the underlying

604
00:36:47,560 --> 00:36:51,040
Speaker 1: wealth creation aspect of the company or wealth creation prospects

605
00:36:51,080 --> 00:36:54,520
Speaker 1: of the company to really sign whether growth is a

606
00:36:54,600 --> 00:36:58,800
Speaker 1: positive or negative. The overwhelming data shows the returns are negative.

607
00:36:59,239 --> 00:37:01,600
Speaker 1: And in a fact world agreed, you have a factor

608
00:37:01,680 --> 00:37:04,640
Speaker 1: that's negative, but I think it's a factor motivated by

609
00:37:04,719 --> 00:37:08,320
Speaker 1: really poor theory, which is problematic in my mind. I

610
00:37:08,400 --> 00:37:10,720
Speaker 1: wanted to ask you about that. So we've been focused

611
00:37:10,800 --> 00:37:17,080
Speaker 1: on investment as one path towards growth. We haven't talked

612
00:37:17,239 --> 00:37:21,760
Speaker 1: as much about the capital side, the cost of capital

613
00:37:21,920 --> 00:37:25,600
Speaker 1: and capital funding decisions going into a company, whether to

614
00:37:25,680 --> 00:37:28,920
Speaker 1: buy back shares or whether to borrow from the market

615
00:37:28,960 --> 00:37:33,440
Speaker 1: and add on leverage. How much has that impacted or

616
00:37:33,600 --> 00:37:37,920
Speaker 1: how much has trends in the capital markets impacted the

617
00:37:38,200 --> 00:37:43,160
Speaker 1: performance of traditional value investing in recent years. So that's

618
00:37:43,160 --> 00:37:46,160
Speaker 1: a great question, and it's something we've we've started tackling

619
00:37:46,239 --> 00:37:49,400
Speaker 1: more recently. And that's I think this notion of value

620
00:37:49,400 --> 00:37:51,440
Speaker 1: and growth getting at the heart of this question is

621
00:37:51,480 --> 00:37:54,400
Speaker 1: really more of a duration argument and a duration problem.

622
00:37:55,280 --> 00:37:59,680
Speaker 1: You have from the nineteen sixties to the eighties sort

623
00:37:59,719 --> 00:38:03,080
Speaker 1: of a period of increasing interest rates. Since the late

624
00:38:03,160 --> 00:38:07,400
Speaker 1: eighties to today, we've basically seen decreasing interest rates. I

625
00:38:07,480 --> 00:38:10,520
Speaker 1: think it's just present value math when we when we

626
00:38:10,680 --> 00:38:16,640
Speaker 1: build out economic profit profiles for companies, the higher growth

627
00:38:16,719 --> 00:38:19,960
Speaker 1: companies ultimately end up with much higher durations and a

628
00:38:20,080 --> 00:38:24,040
Speaker 1: much greater sensitivity to what's happening to discount rates than

629
00:38:24,160 --> 00:38:28,880
Speaker 1: what's traditionally viewed as value companies or low duration companies

630
00:38:28,920 --> 00:38:31,200
Speaker 1: that all their assets, all their cash flows are basically

631
00:38:31,280 --> 00:38:34,799
Speaker 1: coming from what they already had in place, and there's

632
00:38:34,840 --> 00:38:38,160
Speaker 1: not much on the on the horizon. So we've had

633
00:38:38,160 --> 00:38:41,960
Speaker 1: a great we've had a great environment for for kind

634
00:38:42,000 --> 00:38:46,799
Speaker 1: of traditionally defined growth stocks since the nineties. Obviously we've

635
00:38:46,800 --> 00:38:51,640
Speaker 1: had periods of increasing and decreasing rates, or many cycles

636
00:38:51,719 --> 00:38:55,359
Speaker 1: within the larger trend of decreasing rates. We're at zero now.

637
00:38:55,640 --> 00:38:59,680
Speaker 1: You know, it wouldn't surprise us to see these value,

638
00:39:00,280 --> 00:39:03,319
Speaker 1: low duration type stocks have their day in the sun

639
00:39:03,400 --> 00:39:05,840
Speaker 1: again for an extended period of time. If we end

640
00:39:05,920 --> 00:39:10,160
Speaker 1: up with some thematic increase in discount rates and cost

641
00:39:10,239 --> 00:39:12,919
Speaker 1: of capital over time, because those further out cash flow

642
00:39:12,920 --> 00:39:15,680
Speaker 1: has become much less valuable to us, doesn't mean that

643
00:39:15,719 --> 00:39:17,719
Speaker 1: they'll be undervalued per se. That we have to see

644
00:39:17,719 --> 00:39:20,560
Speaker 1: how the market, how fast, and how the market digests

645
00:39:20,600 --> 00:39:24,080
Speaker 1: that and prices that if indeed rising interest rates is

646
00:39:24,120 --> 00:39:27,279
Speaker 1: even what happened, I have my my ability you I

647
00:39:27,320 --> 00:39:29,400
Speaker 1: would probably have to pay someone to listen to my

648
00:39:29,520 --> 00:39:32,640
Speaker 1: thoughts on future interest rate increases. So I won't speculate,

649
00:39:32,760 --> 00:39:37,000
Speaker 1: but I can just say from an economic valuation perspective,

650
00:39:37,080 --> 00:39:39,240
Speaker 1: those are the factors that will be at work driving

651
00:39:39,800 --> 00:39:59,840
Speaker 1: you know, longer versus shorter duration investments. So what happened

652
00:40:00,440 --> 00:40:05,080
Speaker 1: going back? You know, since you sort of identify this

653
00:40:05,360 --> 00:40:09,960
Speaker 1: moment in which your sort of intrinsic value framework seems

654
00:40:10,000 --> 00:40:14,600
Speaker 1: to diverge from the cheapness models, is it just about

655
00:40:14,680 --> 00:40:20,360
Speaker 1: the sort of rise of these more intangible based business

656
00:40:20,440 --> 00:40:23,440
Speaker 1: models what we call a tech or software things like that,

657
00:40:23,640 --> 00:40:27,920
Speaker 1: or what sort what explains this diever this period? Since then?

658
00:40:28,400 --> 00:40:32,960
Speaker 1: So I wouldn't say we recognize that our model diverges

659
00:40:33,000 --> 00:40:35,839
Speaker 1: from cheapness? Are we have a very different worldview than

660
00:40:36,200 --> 00:40:40,279
Speaker 1: than what I'd say a typical quantitative value investor is

661
00:40:40,560 --> 00:40:43,280
Speaker 1: a quantitative value investor is generally going to have completely

662
00:40:43,360 --> 00:40:47,440
Speaker 1: signed off on an efficient market hypothesis, because by definition,

663
00:40:47,520 --> 00:40:49,960
Speaker 1: what they're saying is we're not here to create outfit.

664
00:40:50,000 --> 00:40:52,080
Speaker 1: We're just trying to get you a really fair market

665
00:40:52,160 --> 00:40:55,200
Speaker 1: return for all the risk factors you're you're taking on

666
00:40:55,320 --> 00:41:00,480
Speaker 1: any given portfolio. Our view has always been was sismatically

667
00:41:00,560 --> 00:41:05,160
Speaker 1: believe some companies are overvalued, others are undervalued, and probably

668
00:41:05,239 --> 00:41:08,239
Speaker 1: the majority in the middle. There's not enough of the

669
00:41:08,320 --> 00:41:10,279
Speaker 1: difference to really worry about. So it's kind of the

670
00:41:10,880 --> 00:41:14,800
Speaker 1: the plus on each side that really are important to

671
00:41:14,880 --> 00:41:17,400
Speaker 1: construct a portfolio, and with the rest of the stocks

672
00:41:17,840 --> 00:41:22,200
Speaker 1: it's okay, which leads to certain exploiting of specific behavior.

673
00:41:22,200 --> 00:41:25,080
Speaker 1: If you look at passive investing, you know our view

674
00:41:25,200 --> 00:41:29,919
Speaker 1: is their systematically over investing in the overvalued stock systematically

675
00:41:30,000 --> 00:41:34,520
Speaker 1: under investing in the undervalued stocks. So we're we're super

676
00:41:34,600 --> 00:41:38,080
Speaker 1: happy with the rise of passive investing because it's a

677
00:41:38,200 --> 00:41:41,960
Speaker 1: natural segment for us to trade against. Same with growth

678
00:41:42,040 --> 00:41:45,200
Speaker 1: investors and value investors that we believe their views on

679
00:41:45,280 --> 00:41:49,759
Speaker 1: the world systematically lend them themselves to being exploited. The

680
00:41:49,920 --> 00:41:55,200
Speaker 1: rise of intangibles and intangibles are a really interesting topic

681
00:41:55,360 --> 00:41:57,239
Speaker 1: because now you have a lot of people that are

682
00:41:57,400 --> 00:42:00,239
Speaker 1: that are trying to grapple with that and rescue the

683
00:42:00,320 --> 00:42:02,920
Speaker 1: price if you will. I think that a recent paper

684
00:42:03,960 --> 00:42:08,080
Speaker 1: by Campbell, Harvey and Rob ar No saying let's capitalize

685
00:42:08,239 --> 00:42:11,680
Speaker 1: R and D, and let's take of intangibles and of

686
00:42:12,760 --> 00:42:14,840
Speaker 1: s G and a expense and call it intangibles and

687
00:42:14,880 --> 00:42:17,520
Speaker 1: added to book value. And indeed, when they do that,

688
00:42:17,640 --> 00:42:20,600
Speaker 1: what they see is that book value performs a little

689
00:42:20,600 --> 00:42:23,960
Speaker 1: bit better. Going back to that deconstruction of value that

690
00:42:24,040 --> 00:42:28,160
Speaker 1: I mentioned on on as reported book to price, well,

691
00:42:28,160 --> 00:42:30,480
Speaker 1: i'll call I think they called the variable i h

692
00:42:30,640 --> 00:42:33,640
Speaker 1: mL intangible based high minus low book to value book

693
00:42:33,680 --> 00:42:37,720
Speaker 1: to price. It does absolutely nothing relative to intrinsic value.

694
00:42:37,960 --> 00:42:41,719
Speaker 1: All the alpha in that approach is also supported only

695
00:42:41,800 --> 00:42:44,160
Speaker 1: when stocks are undervalued, are overvalued in terms of the

696
00:42:44,239 --> 00:42:48,560
Speaker 1: long short portfolios, and when you capitalize the intangibles. If

697
00:42:48,640 --> 00:42:51,360
Speaker 1: you if you looked at the return profiles they published,

698
00:42:51,400 --> 00:42:53,680
Speaker 1: that was for all the stocks, yes, but if you

699
00:42:53,719 --> 00:42:56,520
Speaker 1: look at the large caps, the large caps really didn't

700
00:42:56,520 --> 00:42:59,920
Speaker 1: do materially better than an unadjusted book to price, and

701
00:43:00,080 --> 00:43:03,160
Speaker 1: certainly on a on an intrinsic value adjusted basis to

702
00:43:03,239 --> 00:43:06,360
Speaker 1: perform poorly. And I think the reason is all of

703
00:43:06,440 --> 00:43:09,400
Speaker 1: these approaches to intangibles are completely missing the picture on

704
00:43:09,480 --> 00:43:13,000
Speaker 1: how to deal with them. They're immediately treating intangibles as

705
00:43:13,040 --> 00:43:18,520
Speaker 1: a valuation issue, and they're saying Google spending a billion

706
00:43:18,600 --> 00:43:22,400
Speaker 1: on intangibles adds to its book value, but so does Macy's.

707
00:43:23,280 --> 00:43:26,120
Speaker 1: And I would tell you the properties of return for

708
00:43:26,280 --> 00:43:30,799
Speaker 1: Google investing a billion dollars versus Macy's and advertising are

709
00:43:30,880 --> 00:43:36,400
Speaker 1: wildly different. And to treat intangibles immediately as a valuation concept,

710
00:43:36,520 --> 00:43:39,719
Speaker 1: I think is completely wrong. What you need to do

711
00:43:39,920 --> 00:43:41,920
Speaker 1: is you first need to treat it as a corporate

712
00:43:42,000 --> 00:43:45,440
Speaker 1: performance concept. You need to answer the question how well

713
00:43:45,560 --> 00:43:49,359
Speaker 1: is the firm performing the reason you're struggling with intangibles

714
00:43:49,360 --> 00:43:53,000
Speaker 1: as you disagree with the accounting convention of conservatism that

715
00:43:53,080 --> 00:43:56,279
Speaker 1: writes it off immediately, and that's fine, Then treat it

716
00:43:56,360 --> 00:43:59,120
Speaker 1: as an investment and figure out what the real rates

717
00:43:59,160 --> 00:44:02,000
Speaker 1: of return the companies generating on investments, just the way

718
00:44:02,000 --> 00:44:04,719
Speaker 1: you would have the factory, assign a life to it,

719
00:44:05,320 --> 00:44:08,560
Speaker 1: put it on the books, and generate and calculating r

720
00:44:08,600 --> 00:44:13,719
Speaker 1: o I. Companies that consistently are generating significantly positive returns

721
00:44:13,760 --> 00:44:16,960
Speaker 1: on that investment will start to see their r o

722
00:44:17,080 --> 00:44:20,120
Speaker 1: I s increase as they continue spending. Companies that don't won't.

723
00:44:20,600 --> 00:44:23,200
Speaker 1: So Macy's can spend a lot on intangibles and R

724
00:44:23,239 --> 00:44:24,920
Speaker 1: and D whatever, R and D would be for them,

725
00:44:25,360 --> 00:44:27,040
Speaker 1: and it's not going to move the needle on their

726
00:44:27,120 --> 00:44:31,200
Speaker 1: valuation much so to just say they're cheaper because they've

727
00:44:31,239 --> 00:44:34,000
Speaker 1: spent this money on advertising, I think it's just it's

728
00:44:34,040 --> 00:44:37,320
Speaker 1: a super naive view of understanding what valuation is all about.

729
00:44:37,719 --> 00:44:40,000
Speaker 1: It's a convenient view because it lends itself to a

730
00:44:40,080 --> 00:44:43,279
Speaker 1: lot of you know, in the classic mean variance factor world,

731
00:44:43,320 --> 00:44:47,120
Speaker 1: that's a convenient view, but I don't think it's anywhere

732
00:44:47,200 --> 00:44:50,640
Speaker 1: near the appropriate view. And the other the other aspect

733
00:44:50,680 --> 00:44:53,440
Speaker 1: of a lot of these studies is going back to

734
00:44:53,520 --> 00:44:56,920
Speaker 1: this notion of back tests. I don't believe you can.

735
00:44:57,160 --> 00:44:59,320
Speaker 1: You can start to look at it a variable and

736
00:44:59,440 --> 00:45:01,480
Speaker 1: go back time and look at it, and even if

737
00:45:01,560 --> 00:45:05,360
Speaker 1: you don't have any mal intent, it's really hard to

738
00:45:05,520 --> 00:45:09,480
Speaker 1: not know. Over the last ten fifteen years technology firms,

739
00:45:09,520 --> 00:45:11,239
Speaker 1: which are the ones spending the most on R and D.

740
00:45:11,600 --> 00:45:13,719
Speaker 1: I've been the ones that have exploded in stock price.

741
00:45:14,200 --> 00:45:16,399
Speaker 1: You don't have to have mal intent, but everybody knows

742
00:45:16,440 --> 00:45:19,080
Speaker 1: that unless you've lived in a shell. So it's hard

743
00:45:19,120 --> 00:45:21,680
Speaker 1: to be an objective researcher and say, well, the solution is,

744
00:45:21,760 --> 00:45:23,400
Speaker 1: let's add back R and D and we get a

745
00:45:23,440 --> 00:45:27,279
Speaker 1: better metric. Any of these finance studies that are looking

746
00:45:27,400 --> 00:45:32,920
Speaker 1: backward in time and consistently drawing back test results and

747
00:45:33,040 --> 00:45:35,800
Speaker 1: claiming they're real. I think you need to put in

748
00:45:35,880 --> 00:45:38,880
Speaker 1: the hard work and effort. Every time you changed your model,

749
00:45:38,960 --> 00:45:41,240
Speaker 1: your track record stops and you need to start tracking

750
00:45:41,760 --> 00:45:44,319
Speaker 1: what's the efficacy of the model going forward. You can't

751
00:45:44,360 --> 00:45:47,320
Speaker 1: say I launched this model ten years ago, I've improved

752
00:45:47,360 --> 00:45:49,640
Speaker 1: it now because of this additional variable that I've backed

753
00:45:49,640 --> 00:45:51,800
Speaker 1: tested and added to the model. I don't think so

754
00:45:52,360 --> 00:45:55,160
Speaker 1: the models reset when you added this new variable to it. Buddy,

755
00:45:55,800 --> 00:45:58,600
Speaker 1: That's just the way it is. We waited twenty two

756
00:45:58,680 --> 00:46:01,640
Speaker 1: years to kind of make this explicitly public because we

757
00:46:01,719 --> 00:46:04,719
Speaker 1: wanted to make sure there was enough data and the

758
00:46:04,840 --> 00:46:07,880
Speaker 1: data is growing to really do the study properly. And

759
00:46:08,000 --> 00:46:10,680
Speaker 1: I think that's a standard everybody needs to do it

760
00:46:10,760 --> 00:46:13,320
Speaker 1: here to. You can't. You can't create a valuation model

761
00:46:13,400 --> 00:46:17,320
Speaker 1: looking at historic data when you know how you're estimating

762
00:46:17,400 --> 00:46:19,880
Speaker 1: these parameters to assign risk and it fits for that

763
00:46:20,040 --> 00:46:22,879
Speaker 1: period and say you have a great model. Yeah. It's

764
00:46:22,960 --> 00:46:26,800
Speaker 1: great to observe, and observations are an important part of science.

765
00:46:27,239 --> 00:46:29,760
Speaker 1: You have to observe, you formulate your theory, you formulate

766
00:46:29,800 --> 00:46:32,760
Speaker 1: your model, then you have to start calculating the data,

767
00:46:33,320 --> 00:46:34,919
Speaker 1: and you have to let the data theri and age

768
00:46:35,000 --> 00:46:39,080
Speaker 1: live out of sample. It's it's it's painful and it's costly,

769
00:46:39,840 --> 00:46:43,000
Speaker 1: but it's really the gold standard. And Harvard Campbell even

770
00:46:43,040 --> 00:46:47,520
Speaker 1: wrote a paper describing how big of a problem this

771
00:46:47,719 --> 00:46:52,400
Speaker 1: potential for forward bias, you know, foresight bias and inadvertent

772
00:46:52,520 --> 00:46:55,160
Speaker 1: data snooping is in financial research, and he said, clearly

773
00:46:55,680 --> 00:46:57,840
Speaker 1: the gold standard is live out of sample data. But

774
00:46:57,880 --> 00:47:00,759
Speaker 1: it's just very difficult to obtain. And that's one of

775
00:47:00,840 --> 00:47:03,040
Speaker 1: the things that I think really sets us apart from

776
00:47:03,080 --> 00:47:06,560
Speaker 1: any other firms. We've made the investment in time, because

777
00:47:06,600 --> 00:47:08,760
Speaker 1: you can't short change the time. We've made the investment

778
00:47:08,800 --> 00:47:11,120
Speaker 1: in time to get that data and to offer our

779
00:47:11,160 --> 00:47:15,120
Speaker 1: results relative to other data that's been developed through backward

780
00:47:15,200 --> 00:47:17,640
Speaker 1: looking approaches, and we still came out on top. If

781
00:47:17,680 --> 00:47:21,680
Speaker 1: you look at in this valuation data research paper we

782
00:47:21,840 --> 00:47:27,160
Speaker 1: rereleased in October, we systematically reconstructed an asset pricing framework

783
00:47:27,280 --> 00:47:33,680
Speaker 1: looking at all the popular value quantitative value factors, profitability,

784
00:47:34,080 --> 00:47:39,560
Speaker 1: book to price, the investment factor, momentum, low volatility. They

785
00:47:39,640 --> 00:47:43,360
Speaker 1: all basically are subsumed by these concepts of intrinsic value,

786
00:47:44,080 --> 00:47:48,680
Speaker 1: wealth creation or stewardship, and leverage. And what is quantitative

787
00:47:48,760 --> 00:47:52,280
Speaker 1: value investing When you start adding factors such as momentum

788
00:47:52,360 --> 00:47:56,080
Speaker 1: and volatility there, what's the identity of that field? What's

789
00:47:56,080 --> 00:48:00,320
Speaker 1: the theory that links price, momentum and volatility to thetrinsic

790
00:48:00,440 --> 00:48:03,360
Speaker 1: value of the stock? I think it started as a

791
00:48:03,880 --> 00:48:08,040
Speaker 1: as a very clearly defined having a very clearly defined identity,

792
00:48:08,160 --> 00:48:10,920
Speaker 1: i e. This book to price is a factor that

793
00:48:11,040 --> 00:48:15,840
Speaker 1: either represents a behavioral problem that people are missing this information,

794
00:48:16,040 --> 00:48:19,279
Speaker 1: or it's some sort of embedded risk factor. But then

795
00:48:19,360 --> 00:48:22,719
Speaker 1: over time is that factor hasn't worked and they've continually

796
00:48:22,760 --> 00:48:26,840
Speaker 1: added on. I think quantitative value investing the value component

797
00:48:26,920 --> 00:48:29,280
Speaker 1: has lost a lot of its identity. What is it exactly?

798
00:48:29,360 --> 00:48:31,880
Speaker 1: It's okay to have to be a quantitative investor, but

799
00:48:31,960 --> 00:48:35,360
Speaker 1: I'm not sure. Again, getting back to our initial discussion

800
00:48:35,400 --> 00:48:38,120
Speaker 1: at the top of the show, value doesn't belong in there.

801
00:48:38,239 --> 00:48:41,080
Speaker 1: That needs to be reclaimed in a more appropriate manner.

802
00:48:41,120 --> 00:48:44,800
Speaker 1: I think. So I'm going to try to squeeze in

803
00:48:45,040 --> 00:48:50,359
Speaker 1: too hopefully interrelated questions here, But how dynamic are your

804
00:48:50,400 --> 00:48:54,440
Speaker 1: own models? Then? I know, you just very much criticize

805
00:48:54,440 --> 00:48:56,920
Speaker 1: people that are constantly changing their models in order to

806
00:48:57,040 --> 00:49:01,279
Speaker 1: fit new data coming in. And secondly, what did you

807
00:49:01,560 --> 00:49:08,120
Speaker 1: learn from how did your funds actually perform and did

808
00:49:08,160 --> 00:49:12,000
Speaker 1: you make any changes off the back of that. How

809
00:49:12,120 --> 00:49:16,560
Speaker 1: dynamic are models? We pretty much locked in our models

810
00:49:16,600 --> 00:49:20,279
Speaker 1: in Nothing has changed structurally to what we do, the

811
00:49:20,320 --> 00:49:24,600
Speaker 1: way we estimate uh the persistence of economic profit, the

812
00:49:24,640 --> 00:49:29,440
Speaker 1: way we calculate cost of capital risk adjust cost of

813
00:49:29,520 --> 00:49:33,359
Speaker 1: capital for a company. There have been along the way

814
00:49:33,480 --> 00:49:37,080
Speaker 1: what we considered to be minor accounting changes. For instance,

815
00:49:37,120 --> 00:49:40,640
Speaker 1: recently Accountant started capitalizing lease's last year and putting them

816
00:49:40,680 --> 00:49:44,520
Speaker 1: on the balance sheet. We have to undo those because

817
00:49:44,560 --> 00:49:46,719
Speaker 1: we think they actually did it incorrectly for a number

818
00:49:46,719 --> 00:49:49,880
Speaker 1: of reasons that are way way more geeky than probably

819
00:49:49,920 --> 00:49:52,120
Speaker 1: the show needs to get into at the moment. But

820
00:49:53,200 --> 00:49:57,600
Speaker 1: there's always little adjustments like that. But the fundamental thrust

821
00:49:57,680 --> 00:50:00,680
Speaker 1: of the model has not changed, since it's a very

822
00:50:00,800 --> 00:50:05,200
Speaker 1: dynamic model because market prices are always changing and the

823
00:50:05,560 --> 00:50:09,120
Speaker 1: performance and strategy of firms are always changing, so that's

824
00:50:09,400 --> 00:50:12,480
Speaker 1: constantly at work. So the answers, we haven't really changed

825
00:50:12,520 --> 00:50:15,800
Speaker 1: the model. I feel comfortable with my criticism because we

826
00:50:15,920 --> 00:50:18,400
Speaker 1: haven't done that. There's been lots of time where our

827
00:50:18,440 --> 00:50:21,360
Speaker 1: performance hasn't been what we wanted it to be. The

828
00:50:21,800 --> 00:50:25,000
Speaker 1: going back to the first part of you know, the

829
00:50:25,120 --> 00:50:28,520
Speaker 1: value guys weren't in this boat alone. We we performed

830
00:50:29,360 --> 00:50:31,880
Speaker 1: poorly relative to the S and P I. We we

831
00:50:32,080 --> 00:50:35,880
Speaker 1: were benchmarked in the value category. Our mutual fund in

832
00:50:35,960 --> 00:50:40,439
Speaker 1: that space significantly outperformed the value indexes last year. Since

833
00:50:41,400 --> 00:50:44,400
Speaker 1: we started today, we've outperformed the S and P with

834
00:50:44,480 --> 00:50:48,760
Speaker 1: this with kind of our core strategy, the valuation fifty strategy.

835
00:50:49,560 --> 00:50:52,080
Speaker 1: What we learned in twenty is that it's it's really

836
00:50:52,760 --> 00:50:57,440
Speaker 1: two interesting points from one. What we learned is the

837
00:50:57,680 --> 00:51:00,480
Speaker 1: same lesson we've learned over and over. It's very difficult

838
00:51:00,560 --> 00:51:04,359
Speaker 1: to be true to your process. Sometimes it's it's extraordinarily

839
00:51:04,480 --> 00:51:08,040
Speaker 1: painful when you when you see the world is trading

840
00:51:08,080 --> 00:51:12,000
Speaker 1: against you every day and you're underperforming. Yet you need

841
00:51:12,080 --> 00:51:14,880
Speaker 1: to hold on because if these are central truths you

842
00:51:14,960 --> 00:51:18,480
Speaker 1: believe in, this is what your investment clients purchased from you,

843
00:51:18,600 --> 00:51:20,279
Speaker 1: and this is what they expect they're getting, and that's

844
00:51:20,280 --> 00:51:23,680
Speaker 1: what we delivered. So in I think we had two

845
00:51:23,760 --> 00:51:27,080
Speaker 1: trades to the portfolio. One was selling in video UH

846
00:51:27,440 --> 00:51:31,160
Speaker 1: at five plus in August after it just run well

847
00:51:31,239 --> 00:51:34,920
Speaker 1: above finally exceeded our estimate of its intrinsic value, and

848
00:51:35,000 --> 00:51:38,320
Speaker 1: we we had to sell as we were crying pushing

849
00:51:38,320 --> 00:51:40,640
Speaker 1: the button. It had become kind of an old friend

850
00:51:40,680 --> 00:51:44,840
Speaker 1: of our since and a great performer. The other interesting

851
00:51:44,920 --> 00:51:48,759
Speaker 1: thing of in our history of twenty five years of firm,

852
00:51:48,800 --> 00:51:51,680
Speaker 1: we've issued four market calls. Basically, one was in two

853
00:51:51,760 --> 00:51:55,279
Speaker 1: thousand when we thought the market was overvalued along with

854
00:51:55,320 --> 00:51:57,800
Speaker 1: everybody else. We don't. We don't think our insight was

855
00:51:57,960 --> 00:52:00,840
Speaker 1: necessarily unique, although the a we went about it I

856
00:52:00,920 --> 00:52:03,799
Speaker 1: think was kind of fun. With Cisco, we showed kind

857
00:52:03,840 --> 00:52:06,800
Speaker 1: of what the expectations built into Cisco's price were in

858
00:52:06,840 --> 00:52:09,879
Speaker 1: two thousand of the process we called we created back

859
00:52:09,920 --> 00:52:12,600
Speaker 1: in ninety eight called value Expectations, where we take our

860
00:52:12,680 --> 00:52:16,759
Speaker 1: model and we reversed out the performance implications of a

861
00:52:17,000 --> 00:52:20,560
Speaker 1: of a stock price and we showed Cisco basically had

862
00:52:20,640 --> 00:52:24,479
Speaker 1: to grow it plus sales for the next five years.

863
00:52:25,280 --> 00:52:28,120
Speaker 1: In twenty the end of two thousand and eight, two

864
00:52:28,160 --> 00:52:31,120
Speaker 1: thousand of nine, we came out with another market call

865
00:52:31,200 --> 00:52:35,719
Speaker 1: that we said the market was just extraordinarily undervalued. That

866
00:52:36,040 --> 00:52:38,120
Speaker 1: that was fun because I happened to be on CNBC

867
00:52:38,440 --> 00:52:40,479
Speaker 1: mentioning that at the time, and they thought I was nuts.

868
00:52:41,520 --> 00:52:44,080
Speaker 1: And then this year, with all the volatility, we made

869
00:52:44,120 --> 00:52:48,640
Speaker 1: to market, called one in March saying the market's really undervalued.

870
00:52:48,680 --> 00:52:51,480
Speaker 1: It reminded us of two thousand and eight, and then

871
00:52:51,560 --> 00:52:55,239
Speaker 1: to us finally the market became overvalued in August of

872
00:52:55,320 --> 00:52:59,000
Speaker 1: this year. Now, obviously we've been way wrong. We didn't

873
00:52:59,080 --> 00:53:02,120
Speaker 1: in August. We didn't say, uh, this is an absolute

874
00:53:02,600 --> 00:53:04,759
Speaker 1: time to get out, but we did say statistically, we

875
00:53:04,840 --> 00:53:07,920
Speaker 1: think the market is expensive. We'd be very cautious here.

876
00:53:08,080 --> 00:53:09,840
Speaker 1: You know, who knows whether it'll be right or not

877
00:53:10,040 --> 00:53:12,400
Speaker 1: with that, But you know, that's just what our models

878
00:53:12,480 --> 00:53:15,280
Speaker 1: indicated to us. So that's that's the way we're approaching

879
00:53:15,320 --> 00:53:18,200
Speaker 1: the world. I think it's important to stick to your discipline,

880
00:53:18,880 --> 00:53:23,399
Speaker 1: and that's what stick to your discipline. The market isn't

881
00:53:23,400 --> 00:53:27,279
Speaker 1: always efficient, but it sure is generally rational through time.

882
00:53:27,800 --> 00:53:29,480
Speaker 1: And like I said, a lot of stocks that that

883
00:53:29,560 --> 00:53:33,520
Speaker 1: it underperformed continued to do really well after March, and

884
00:53:33,840 --> 00:53:37,360
Speaker 1: if we looked at our returns last year, we underperformed

885
00:53:37,400 --> 00:53:40,279
Speaker 1: the SMP year to date this year, that fund is

886
00:53:40,280 --> 00:53:43,360
Speaker 1: outperformed the SMP by more so from the start of

887
00:53:44,040 --> 00:53:48,120
Speaker 1: we're up this process. We launched this strategy in two

888
00:53:48,200 --> 00:53:52,040
Speaker 1: thousand and four. It's significantly up on the SMP since

889
00:53:52,120 --> 00:53:55,160
Speaker 1: inception over the last ten years. As I mentioned, over

890
00:53:55,239 --> 00:53:58,200
Speaker 1: the last year plus, I think over three years. It's

891
00:53:58,239 --> 00:54:00,880
Speaker 1: probably down over five years. It's probab we are I

892
00:54:00,920 --> 00:54:04,560
Speaker 1: don't really I don't really track all the return profiles

893
00:54:04,560 --> 00:54:06,880
Speaker 1: because we don't. You know, the turnover on this portfolio

894
00:54:06,960 --> 00:54:08,960
Speaker 1: is about ten percent a year for to five stocks

895
00:54:09,000 --> 00:54:12,320
Speaker 1: a year, unless we have companies that are acquired. So

896
00:54:12,440 --> 00:54:14,560
Speaker 1: what do you do? I mean you you said you

897
00:54:14,840 --> 00:54:20,440
Speaker 1: sold in video. You said in August you identified, uh,

898
00:54:20,760 --> 00:54:24,279
Speaker 1: the S and P or the market overall is being overpriced.

899
00:54:24,880 --> 00:54:28,319
Speaker 1: What do you do from a portfolio perspective? What kind

900
00:54:28,360 --> 00:54:29,839
Speaker 1: of shifts do you make or what are what are

901
00:54:29,920 --> 00:54:33,600
Speaker 1: the implications for you when you make an overall market

902
00:54:33,680 --> 00:54:37,479
Speaker 1: valuation call. In this particular portfolio, it's a long only fund,

903
00:54:37,680 --> 00:54:40,239
Speaker 1: so we aren't going to do anything and the charter

904
00:54:40,440 --> 00:54:43,520
Speaker 1: is to be fully invested. So we sold in video,

905
00:54:43,640 --> 00:54:46,359
Speaker 1: we replaced it um and again this is this kind

906
00:54:46,400 --> 00:54:49,640
Speaker 1: of speaks to why valuation is a little quirky. Let

907
00:54:49,680 --> 00:54:51,640
Speaker 1: me just give you a little a little background prior

908
00:54:51,680 --> 00:54:55,040
Speaker 1: to this. The direct answer to a video we the

909
00:54:55,120 --> 00:54:56,920
Speaker 1: direct answer to the video, we sold the video. We

910
00:54:57,000 --> 00:55:02,080
Speaker 1: bought k l A, the semi conductor equipment maker. But

911
00:55:02,239 --> 00:55:05,120
Speaker 1: the background of that that makes that particular purchase interesting.

912
00:55:05,160 --> 00:55:08,640
Speaker 1: And if you go back to the composition of this

913
00:55:08,760 --> 00:55:12,600
Speaker 1: portfolio is approximately twenty five stocks that would be classified

914
00:55:12,640 --> 00:55:15,480
Speaker 1: as sort of value or core, twenty five stocks that

915
00:55:15,520 --> 00:55:20,680
Speaker 1: would be classified as sort of core or growth. Over time,

916
00:55:21,040 --> 00:55:25,160
Speaker 1: as the market has kind of pushed up the valuations

917
00:55:25,200 --> 00:55:29,040
Speaker 1: of growth stocks, the portfolio is naturally then shifting its

918
00:55:29,120 --> 00:55:33,279
Speaker 1: marginal trades away from shading growth, getting more and more

919
00:55:33,360 --> 00:55:37,560
Speaker 1: into value. By August of this year, the composition of

920
00:55:37,640 --> 00:55:43,120
Speaker 1: the portfolio was approximately seventy seventy of the portfolio was

921
00:55:43,160 --> 00:55:50,080
Speaker 1: in value core stocks. In core value stocks. KLA Corp.

922
00:55:50,560 --> 00:55:53,120
Speaker 1: Happens to be at growth stock. So it's it's it's

923
00:55:53,160 --> 00:55:57,600
Speaker 1: hard to just pigeonhole what we're what valuation says, because

924
00:55:57,680 --> 00:56:00,200
Speaker 1: it doesn't necessarily just have to put you in a

925
00:56:00,960 --> 00:56:04,279
Speaker 1: in a quote non growth value stock or or a

926
00:56:04,360 --> 00:56:07,680
Speaker 1: high flying growth stock. It really is constantly shifting between

927
00:56:08,239 --> 00:56:10,200
Speaker 1: what the market is giving us, and so would we

928
00:56:10,840 --> 00:56:13,279
Speaker 1: When we pulled the trigger on video, we bought kl

929
00:56:13,320 --> 00:56:17,200
Speaker 1: A Corps and immediately KLO Court dropped ten percent, which

930
00:56:17,280 --> 00:56:20,319
Speaker 1: is always the case when we big a trade, which

931
00:56:20,440 --> 00:56:22,360
Speaker 1: is really annoying. But since then it's it's done. A

932
00:56:22,400 --> 00:56:26,440
Speaker 1: really nice job. Routy that Ruffael, that was. That was

933
00:56:26,480 --> 00:56:28,960
Speaker 1: a great conversation. Any other sort of last thoughts or

934
00:56:29,040 --> 00:56:31,880
Speaker 1: key things that you think we should, uh our listeners

935
00:56:31,880 --> 00:56:33,640
Speaker 1: should think about. No, that was. That was a lot

936
00:56:33,680 --> 00:56:35,680
Speaker 1: of fun. I hope we can do again sometimes. That

937
00:56:35,800 --> 00:56:37,960
Speaker 1: was a lot of fun. Well, yeah, let's definitely do

938
00:56:38,040 --> 00:56:40,320
Speaker 1: it again sometime. And I really appreciate you joining it.

939
00:56:41,080 --> 00:56:59,560
Speaker 1: Thank you both, Thanks so much so Trazy, I thought

940
00:56:59,600 --> 00:57:02,200
Speaker 1: that was that was super interesting. I mean, obviously we've

941
00:57:02,280 --> 00:57:08,280
Speaker 1: talked about a lot of these themes before, resuscitating value,

942
00:57:08,360 --> 00:57:11,799
Speaker 1: reviving value, intrinsic trying to come up with some new

943
00:57:11,960 --> 00:57:16,120
Speaker 1: concept of intrinsic worth based on intangible assets, and I

944
00:57:16,240 --> 00:57:19,520
Speaker 1: like that. It feels like their work tries to just

945
00:57:19,600 --> 00:57:23,000
Speaker 1: go about the problem differently, rather than starting from this

946
00:57:23,200 --> 00:57:27,000
Speaker 1: premise that there are ratios or book that book value

947
00:57:27,040 --> 00:57:30,840
Speaker 1: is a useful idea sort of defined value, but not

948
00:57:31,120 --> 00:57:35,960
Speaker 1: within not within the old constraints, right, although it does

949
00:57:36,080 --> 00:57:39,320
Speaker 1: get me thinking if whether you know, one of the

950
00:57:39,560 --> 00:57:44,440
Speaker 1: enduring mysteries of our investment age is why value investing

951
00:57:44,520 --> 00:57:48,080
Speaker 1: hasn't performed better, it makes me wonder whether or not

952
00:57:48,280 --> 00:57:53,600
Speaker 1: like it all just comes down to the definition and semantics.

953
00:57:53,880 --> 00:57:56,440
Speaker 1: And you know, I guess we kind of touched on

954
00:57:56,520 --> 00:57:59,120
Speaker 1: this in the intro, but if you have a completely

955
00:57:59,360 --> 00:58:03,920
Speaker 1: different definition of value investing, then hey, it actually works.

956
00:58:05,160 --> 00:58:07,520
Speaker 1: I guess maybe we the way, and I'm sure this

957
00:58:07,560 --> 00:58:10,280
Speaker 1: won't be our last conversation on the topic, But I

958
00:58:10,360 --> 00:58:14,000
Speaker 1: wonder if the better question is why don't cheap stocks

959
00:58:14,040 --> 00:58:16,280
Speaker 1: do better? Because I sort of like, I mean, ultimately,

960
00:58:16,560 --> 00:58:19,600
Speaker 1: or why don't or why don't the sort of the

961
00:58:19,760 --> 00:58:24,480
Speaker 1: traditional value factors do better? Because it seems like um

962
00:58:24,840 --> 00:58:29,120
Speaker 1: Raphael's criticism isn't that what the concept of value value

963
00:58:29,120 --> 00:58:31,800
Speaker 1: investing per se obviously because he is sort of in

964
00:58:31,880 --> 00:58:34,960
Speaker 1: that category, but just in this idea that the sort

965
00:58:34,960 --> 00:58:39,560
Speaker 1: of like the traditional like farmer French factors that one

966
00:58:39,720 --> 00:58:44,120
Speaker 1: at one point seemed to point to outside returns no

967
00:58:44,320 --> 00:58:46,680
Speaker 1: longer do well, is you gonna say? Then we can

968
00:58:46,720 --> 00:58:49,439
Speaker 1: at least sort of define what we're talking about better?

969
00:58:49,560 --> 00:58:54,520
Speaker 1: Because you know, again, if anyone can sort of redefine value,

970
00:58:54,600 --> 00:58:57,360
Speaker 1: then you can never really prove that it's working or

971
00:58:57,440 --> 00:59:00,440
Speaker 1: not working. But if we could start our convert station

972
00:59:00,600 --> 00:59:03,640
Speaker 1: with why don't these traditional ratios work the way they

973
00:59:03,760 --> 00:59:05,480
Speaker 1: used to? Why does it price to book, why does

974
00:59:05,520 --> 00:59:08,400
Speaker 1: it price to earnings work the way we used to,

975
00:59:08,800 --> 00:59:12,200
Speaker 1: then at least we can define the debate. Yeah. I

976
00:59:12,280 --> 00:59:15,120
Speaker 1: think that's a good point. Um. I also liked how

977
00:59:16,040 --> 00:59:20,000
Speaker 1: strongly he feels about back testing, uh and sort of

978
00:59:20,080 --> 00:59:24,000
Speaker 1: like fitting your thesis to the data. And also this

979
00:59:24,160 --> 00:59:29,400
Speaker 1: idea of if you're making substantial, substantial changes to your

980
00:59:29,480 --> 00:59:33,440
Speaker 1: model every month or every year or whatever, you're basically

981
00:59:33,840 --> 00:59:36,880
Speaker 1: starting from scratch. I don't think you hear that very

982
00:59:36,960 --> 00:59:40,560
Speaker 1: often among a systematic investors. So that was fun. He

983
00:59:40,680 --> 00:59:44,480
Speaker 1: also had some like pretty like strong negative words towards

984
00:59:45,080 --> 00:59:47,640
Speaker 1: you know, what we called quant investing, and so you

985
00:59:47,880 --> 00:59:51,040
Speaker 1: you know, the idea that you could have one person

986
00:59:51,120 --> 00:59:53,720
Speaker 1: who's a sort of quant investor who looks at things

987
00:59:53,840 --> 00:59:56,920
Speaker 1: like price to book, another person who's a quant that

988
00:59:57,000 --> 01:00:00,920
Speaker 1: looks like at momentum factors, they're not out really can

989
01:00:01,360 --> 01:00:04,040
Speaker 1: as he put it, you know, maybe some people argue

990
01:00:04,040 --> 01:00:07,760
Speaker 1: they would they're not. Really, They're not intuitively connected. It's

991
01:00:07,800 --> 01:00:11,480
Speaker 1: not obvious why they should be under the same family

992
01:00:11,960 --> 01:00:16,640
Speaker 1: of thought about investing or about stock picking. Sort of

993
01:00:17,120 --> 01:00:22,400
Speaker 1: ratio based investing is obviously intuitive to the company. Momentum

994
01:00:22,480 --> 01:00:25,040
Speaker 1: factors are intuitive to the price of the stock itself

995
01:00:25,160 --> 01:00:27,440
Speaker 1: or related to the price of the stock itself. It's

996
01:00:27,480 --> 01:00:31,040
Speaker 1: pretty different stuff that ends up getting lumped into one

997
01:00:31,360 --> 01:00:36,000
Speaker 1: broader category that we call quant Yeah. I think that's

998
01:00:36,000 --> 01:00:38,720
Speaker 1: a good point. Should we leave it there? Yeah, let's

999
01:00:38,760 --> 01:00:42,880
Speaker 1: leave it there. Okay. This has been another episode of

1000
01:00:43,000 --> 01:00:46,160
Speaker 1: the All Thoughts Podcast. I'm Tracy Alloway. You can follow

1001
01:00:46,240 --> 01:00:49,560
Speaker 1: me on Twitter at Tracy Alloway and I'm Joe Why

1002
01:00:49,600 --> 01:00:52,240
Speaker 1: Isn't Though? You can follow me on Twitter at the Stalwart.

1003
01:00:52,560 --> 01:00:55,960
Speaker 1: Follow our guests on Twitter. Raphael Ascendez He's at our

1004
01:00:56,040 --> 01:00:59,320
Speaker 1: Ascendas and check out check out their paper that he

1005
01:00:59,520 --> 01:01:04,040
Speaker 1: co off Third Valuation Beta, addressing inadequacy inadequacies of book

1006
01:01:04,080 --> 01:01:08,280
Speaker 1: to price with intrinsic value, stewardship and leverage that's available

1007
01:01:08,360 --> 01:01:12,480
Speaker 1: for download online. Follow our producer Laura Carlson, She's at

1008
01:01:12,600 --> 01:01:16,560
Speaker 1: Laura M. Carlson. Followed the Bloomberg head of podcast, Francesca Levie,

1009
01:01:16,680 --> 01:01:20,000
Speaker 1: at Francesca Today and check out all of our podcasts

1010
01:01:20,680 --> 01:01:23,360
Speaker 1: under the handle at podcasts. Thanks for listening.