Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast.
Speaker 1: I'm Joe Wisn't Thal and I'm Tracy Hallaway. Tracy, you know,
Speaker 1: one of the things that we discussed a fair number
Speaker 1: of times but also maybe before. It's just sort of
Speaker 1: the I guess you would say permanent state of crisis
Speaker 1: that you might call it in so called value investors. Yes,
Speaker 1: it's been. It's certainly been a long running theme, hasn't it.
Speaker 1: And I feel like we've done quite a few episodes
Speaker 1: on it at this point, but didn't exactly turn things
Speaker 1: around for value investors either. No, definitely not. And I think,
Speaker 1: like you know, there's really two strands that tend to
Speaker 1: emerge in our conversations, and I would characterize them is
Speaker 1: this one is that there is some like cyclical element
Speaker 1: to value investing in this cycle, it just hasn't turned
Speaker 1: around yet. So it's like we have this sort of
Speaker 1: long multiple expansion. It's kind of low growth fed driven
Speaker 1: economy that sort of creates this permanent bid for growth
Speaker 1: companies at the expensive value week GDP growth, etcetera. Just
Speaker 1: like certain industries aren't going to thrive in that environment.
Speaker 1: And then I would say the other half is that no,
Speaker 1: the problem is that we are not good at defining
Speaker 1: value in their people sort of take a more accounting
Speaker 1: approach and say traditional measures of the stocks cheapness, like
Speaker 1: say price to book value. It just these measures don't
Speaker 1: work in a world where so much value is intangible. Right.
Speaker 1: This is the idea that as companies, well the sort
Speaker 1: of successful companies nowadays, are investing a lot, they're investing
Speaker 1: a lot in things like their brands and other intangible
Speaker 1: assets that just aren't captured by price to book value.
Speaker 1: And so a company that's positioning itself for a really
Speaker 1: good future performance might not actually look that way if
Speaker 1: you're just glancing at, you know, something like price to
Speaker 1: book you know, like it's sort of a joke, and
Speaker 1: I think we've talked about it with Michael Mobison last
Speaker 1: year Jared Witdard. But it's like you can rescue value
Speaker 1: investing if you call Netflix of value stock, or if
Speaker 1: you call Facebook a value stode, and someone would say, Okay,
Speaker 1: these companies don't have like big like factories that can
Speaker 1: easily be measured. But if you could somehow like put
Speaker 1: a number on the value of the Facebook network as
Speaker 1: an asset, then you couldn't, then you could theoretically imagine
Speaker 1: a world in which had some traditional value screen Facebook
Speaker 1: comes up right. I mean, the thing that still makes
Speaker 1: me uncomfortable about value stocks is that you're you're still
Speaker 1: investing in something on the basis that the market has
Speaker 1: some how mischaracterized its future, which I think you touched
Speaker 1: on this with with your two narratives. But I think
Speaker 1: in the current environment, where we talk a lot a
Speaker 1: lot about flows based investing, a lot of momentum trading
Speaker 1: things like that, it feels like the market is quite
Speaker 1: consistently directing capital to you know, a few firms, and
Speaker 1: then it just keeps doing that and those firms get
Speaker 1: overvalued and overvalued and overvalued. And if you're not in
Speaker 1: that cycle, I don't know. I just feel like it's
Speaker 1: unlikely that you're going to get back in it. And
Speaker 1: the longer it takes you to get in, the more
Speaker 1: you're sort of losing out in terms of valuations. But anyway, sorry,
Speaker 1: I'm going on a tangent. No, No, it's great, And
Speaker 1: I mean, and the other thing, and I think it's
Speaker 1: sort of related to your point, is you know, it's
Speaker 1: nice to say like, okay, like there's some intangible value
Speaker 1: that we if we could only measure it at Netflix
Speaker 1: or Facebook or whatever it is, but it seems hard
Speaker 1: to know in advance that is there. And so it's like, okay,
Speaker 1: if you have some like factory, then you can at
Speaker 1: least say, okay, this exists, and historically speaking, it will uh,
Speaker 1: you know, we would project it over the next ten years.
Speaker 1: It's going to throw up this cash and that's a
Speaker 1: good value. It feels like with a lot of these
Speaker 1: sort of like backing into the value approach that it's
Speaker 1: very much easier to say in retrospect or expose factor, oh,
Speaker 1: this turned out to be a very valuable asset that
Speaker 1: they have, which is sort of nice, I guess from
Speaker 1: maybe an intellectual standpoint, but it doesn't really help you
Speaker 1: like pick stocks today, which is what people to really
Speaker 1: care about these discussions. It's great to say, Okay, the
Speaker 1: Facebook network is worth a lot of money, but you know,
Speaker 1: I wish you had told me that six years ago. No,
Speaker 1: but it does help a lot of different investment companies
Speaker 1: come up with different strategies and factors to come up
Speaker 1: with different definitions of value that always work when they're
Speaker 1: back tested against historical data. Right. So I guess the
Speaker 1: question is that sarcasm. I don't know my sarcasm is
Speaker 1: coming through on the podcast, but I got it crazy.
Speaker 1: But I guess the thing we're looking for is the
Speaker 1: approach that works in advance, that's not just back tested
Speaker 1: and some you know, some approach that can help us
Speaker 1: identify the sort of deeply under a company intervalue companies
Speaker 1: today using some something whether it's a metric, whether it's
Speaker 1: a screen, whether it's some other intangible measure, something that
Speaker 1: doesn't just help us rationalize the past, but can you know,
Speaker 1: help help me retire a few years earlier. Yeah, and
Speaker 1: also maybe even explain why value investing as it's you know,
Speaker 1: commonly understood has performed so badly for so long. Absolutely
Speaker 1: all right, So I'm very excited. We're going to be
Speaker 1: speaking with someone today who may be able to help
Speaker 1: move this forward, whose work consists of sort of solving
Speaker 1: this problem. An investor, so obviously someone who wants to
Speaker 1: do more than just explain the past but produce good
Speaker 1: returns going forward. We're going to be speaking with Rafael Rassindez.
Speaker 1: He is the co founder of Applied Finance Capital Management,
Speaker 1: which has looked at some of these problems and try
Speaker 1: to identify where the traditional and value investing framework has
Speaker 1: gone wrong. So, Rafael, thank you very much for joining us.
Speaker 1: Great to be here. So I'm curious, I mean, uh,
Speaker 1: you know, I always like to look for validation after
Speaker 1: our introest But that general framework of sort of the
Speaker 1: two stories that people tell about value investing, how does
Speaker 1: that comport with how you see the world right now
Speaker 1: and how you see this sort of ongoing debate. Great,
Speaker 1: great question. So as you, as you know through some
Speaker 1: of our exchanges, applied finance focuses on valuation. I think
Speaker 1: to some degree the term value has been hijacked historically
Speaker 1: with with the advent of the release of the FAMA
Speaker 1: French three factor model back in ninety two and the
Speaker 1: creation of this quote value factor, otherwise known as the
Speaker 1: book to price ratio. I think the term value has
Speaker 1: really been hijacked because book to price, at the end
Speaker 1: of the day, is really nothing more than a cheapness metric,
Speaker 1: as are all the price to something measures. It's measuring
Speaker 1: the price of a stock in relation to some fundamental variable,
Speaker 1: whatever that variable is, or even composites of that variable.
Speaker 1: But it doesn't necessarily relate to the worth of a stock,
Speaker 1: and I think that fundamentally is where things have really
Speaker 1: tended to diverge or go off the rails to some degree.
Speaker 1: In investing finance, the focus has been on cheapness, and
Speaker 1: there hasn't been enough attention paid to understanding intrinsic value
Speaker 1: through more complete valuation approaches, which is what we've specialized
Speaker 1: in since n SO Applied finance has been in existence
Speaker 1: since nineteen ninety five. But we were chatting before we
Speaker 1: started recording the podcast, and you mentioned that your current
Speaker 1: area of focus is something that grabbed your interest because
Speaker 1: of a big debate that was happening in the nineties seventies.
Speaker 1: Could you maybe explain that a little bit? Sure? Sure, so,
Speaker 1: let me just provide a little historic background on how
Speaker 1: we are where we are today. If you think about
Speaker 1: how finance has evolved, you know, in the sixties you
Speaker 1: had a lot of theoretical work on the CAPTEM model,
Speaker 1: which was really exciting, and then in the early seventies
Speaker 1: you started to see this basically this brand new field
Speaker 1: of finance created by Eugene Fama, which is empirical finance,
Speaker 1: and he did amazing work testing and kind of formulating
Speaker 1: the efficient market hypothesis and ushering a whole new era
Speaker 1: of scientific method applied to finance, so to speak. And
Speaker 1: I think prior to that, for the most part, university
Speaker 1: level finance focused on net present value and a lot
Speaker 1: of security analysis. And in a great paper that Farmer wrote,
Speaker 1: I believe it's titled you know, My Memories and Finance,
Speaker 1: or something to that extent, he essentially says, you know,
Speaker 1: when I arrived at Chicago, finance consisted of classes essentially
Speaker 1: teaching people to pick stocks, and in in an efficient
Speaker 1: market world, there's not a lot of value to having
Speaker 1: so many people specialized in that. And what we saw
Speaker 1: in the seventies was this birth of mean variance finance,
Speaker 1: where computer methods and data availability allowed for incredible study
Speaker 1: of properties of stock prices, testing of hypotheses of you know,
Speaker 1: a lot of the mainstream thoughts back then we're biased
Speaker 1: stock just because it's going up. So a lot of
Speaker 1: efficient market tests focused on momentum investing, which is kind
Speaker 1: of ironic given the crave of momentum investing now you know,
Speaker 1: thirty years later, fifty years later, um. And ultimately it
Speaker 1: drove security analysis to a large degree out of finance,
Speaker 1: and you see a lot of security analysis of courses
Speaker 1: now being taught in accounting departments. And what we tried
Speaker 1: to do had applied finance when we started in was
Speaker 1: essentially try to link this notion of mean variance finance
Speaker 1: and security analysis. And we did that through systematically constructing
Speaker 1: security analysis rules to process data on companies and then
Speaker 1: ultimately linking that the output of that those reformulated income
Speaker 1: statements and balance sheets from accounting data into more of
Speaker 1: an economic framework through the calculation of an economic margin,
Speaker 1: which essentially is a firm's return on investment less it's
Speaker 1: cost a capital. We linked that with expected capital growth, risk,
Speaker 1: and competition to value a company. And so we did
Speaker 1: this work from the seventies to ninety five, kind of
Speaker 1: a twenty five year what I'll call an observation window.
Speaker 1: And I think, Tracy, going back to one of your
Speaker 1: statements you made earlier about back testing, it'd be fun
Speaker 1: to get into that. It'll a little bit more depth later,
Speaker 1: but so I'll call it an observation window rather than
Speaker 1: an evidence window. I think there's a big distinction between
Speaker 1: the two. And beginning in nineteen we began calculating intrinsic
Speaker 1: value estimates. First for US companies on an ad hoc basis,
Speaker 1: i'd say monthly and quarterly. When we were early on
Speaker 1: in our company development, we were doing a lot of
Speaker 1: corporate consulting work. We didn't have the full uh infrastructure
Speaker 1: of personnel to handle calculating what i'll call productions of
Speaker 1: intrinsic value consistently. By the company had grown, and we
Speaker 1: began calculating these intrinsic values monthly, and from through today
Speaker 1: we continue doing that. Now we do it globally on
Speaker 1: twenty thousand companies every week. But what's interesting is back
Speaker 1: test observations versus evidence. Since ninety eight, these have all
Speaker 1: been live out of sample estimates of intrinsic value. And
Speaker 1: I think one of the one of the interesting things
Speaker 1: about our data set and what we used to prepare
Speaker 1: this paper called Valuation Beta, is that a lot of finances, well,
Speaker 1: let's go back to nineteen sixty three, and someone will
Speaker 1: say no, let's go back to nineteen thirty and someone
Speaker 1: will say no, I have a data series going back
Speaker 1: to the seventeen hundreds. Well, at some point this data
Speaker 1: is really irrelevant because the world has changed so much.
Speaker 1: Maybe there are these quote fundamental truths, but if you
Speaker 1: look at the factor work. Oftentimes these factors are discovered
Speaker 1: in a back test setting and then when they go
Speaker 1: live they don't work. And certainly that's been an important
Speaker 1: part of the experience with Book to Price since it's
Speaker 1: had prior to ninety two. From six to ninety one,
Speaker 1: it was basically a money making machine. The return attributes
Speaker 1: of that variable or extraordinary. Since ninety two it's had
Speaker 1: a much more spotty record, and the same as as
Speaker 1: if we dig down a little deeper on some of
Speaker 1: these other factor metrics, such as the profitability factor, the
Speaker 1: investment factor. Since they were really Eastern publicly, they've had
Speaker 1: a spotty or record as well. So I think it's important,
Speaker 1: as you said, to differentiate between back testing, which is
Speaker 1: when you're kind of formulating your your ideas, versus evidence,
Speaker 1: which happens after you've released your ideas and you you
Speaker 1: let the let the world run. As Mike Tyson likes
Speaker 1: to say, everyone has a plan to you're punched in
Speaker 1: the face, and for Book to Price and value investing,
Speaker 1: that punch in the face has happened over the last
Speaker 1: decade when it basically hasn't worked. H So you make
Speaker 1: this distinction between value valuation versus cheapness, and as you
Speaker 1: characterize that measures of price to book or really any
Speaker 1: other sort of ratio based valuation, it's not to measure
Speaker 1: of value per se, it's just a measure of cheapness.
Speaker 1: So can you explain a little bit further, like what
Speaker 1: the differences between what your measure of valuation and cheapness
Speaker 1: and why it is in your view that some of
Speaker 1: these traditional metrics measures of cheapness that at one point
Speaker 1: did produce returns have failed to do. So let me
Speaker 1: focus first on explaining our worldview and kind of how
Speaker 1: we get we get to an answer. And there's there's
Speaker 1: probably lots of reasons to speculate about why something doesn't work,
Speaker 1: but I'll offer our view of it as well. So, first,
Speaker 1: as opposed to beginning with some metric of cheapness, what
Speaker 1: we begin our discussion with is an estimate of intrinsic value.
Speaker 1: So how do we go from accounting data to an
Speaker 1: estimate of affirms intrinsic values. So we begin we process
Speaker 1: the data, and there's a lot of around the world,
Speaker 1: there's a lot of accounting issues that we've dealt with
Speaker 1: and tried to incorporate so dramatically in our analysis, but
Speaker 1: I'll just talk about a few that I think make
Speaker 1: for for a quick, interesting discussion. Since since the beginning
Speaker 1: of our of our company, we've viewed research and development
Speaker 1: as an investment rather than an expense. So since we've
Speaker 1: capitalized R and D, and our view is this is
Speaker 1: a piece of operations based cash flow the company is
Speaker 1: generating currently. Another item is the use of operating leases.
Speaker 1: You know, the accountants historically have treated that as an
Speaker 1: expense rather than anything having to do with the balance sheet. Systematically,
Speaker 1: in the last couple of years, fast he has kind
Speaker 1: of come around to our view of thinking and as
Speaker 1: required companies to start capitalizing operating leases. We want to
Speaker 1: view companies on a on a capital structure free basis.
Speaker 1: So we're going to add back interest expense on an
Speaker 1: after tax basis, We're going to make adjustments to the
Speaker 1: balance sheet for inflation. It sounds a little silly now,
Speaker 1: and certainly is silly for technology companies that have very
Speaker 1: little physical plant, but for industrial firms that have assets
Speaker 1: on their books that they acquired in the seventies, inflation
Speaker 1: is a big deal because it's easy to have inflated
Speaker 1: return on investments because the balance sheets reflecting historic cost
Speaker 1: the income is respecting is displaying current values, current dollars values.
Speaker 1: So there's a litany of adjustments we go through. We
Speaker 1: do all that, and one last thing that we we
Speaker 1: account for is how long the company's assets last on
Speaker 1: average to get an asset life. So then we can
Speaker 1: configure a firm as a project and we can calculate
Speaker 1: what we call an economic margin. We don't have to
Speaker 1: get into all the geek speak on that, but essentially,
Speaker 1: at the end of the day, what we end up
Speaker 1: with is a corrected return on investment on an inflation
Speaker 1: adjusted asset based minus acost of capital. That spread or
Speaker 1: economic margin, starts to tell us a lot of things
Speaker 1: about the company. First of all, if you have an
Speaker 1: economic margin that's negative, it tells you the firm is
Speaker 1: investing in projects below its cost of capital. So the
Speaker 1: last thing we'd want to see a firm like that
Speaker 1: do is grow its business. That's what we call wealth destroyers,
Speaker 1: and it's it's a very key piece of how we
Speaker 1: think about analyzing companies and what they're doing. If you
Speaker 1: have a negative spread to your cost of capital. You
Speaker 1: should be shrinking your business or rationalizing what you have
Speaker 1: to figure out how to at least get to a
Speaker 1: zero spread. Companies that have a positive spread, and again
Speaker 1: this is after accounting for things such as investments in
Speaker 1: R and D off, balance sheet releases, on and so on.
Speaker 1: Companies that have a positive spread should grow. And I
Speaker 1: think Joe in one of your tweets a while back
Speaker 1: that that initiated our discussions, you asked a question about Monster,
Speaker 1: and you said, can anyone explain Monster to us? The
Speaker 1: Monster story is pretty simple. This is a firm that
Speaker 1: some people know. This is the the energy drink maker
Speaker 1: with lots of caffeine, that's the best performing stock. Go on,
Speaker 1: I just wanted to, uh, just for people who are great, great,
Speaker 1: it's been an incredible wealth compounding company through the years.
Speaker 1: It's not a it's not a recent thing. You know.
Speaker 1: This is a firm that's earning ten to fift percent
Speaker 1: returns above its cost of capital and has been growing
Speaker 1: its capital based at double digits. So you have This
Speaker 1: is a classic example of what we call a wealth creator,
Speaker 1: a compound wealth creator generally reinvesting in high rates of return,
Speaker 1: creating more and more wealth for the existing shareholders, and
Speaker 1: the company continues growing into its valuation. I think we
Speaker 1: sent you a chart that traces out our estimate of
Speaker 1: Monsters intrinsic value through time, and again these are basically
Speaker 1: live estimates going back. This is in this case going
Speaker 1: back to ten years at least, but it traced out
Speaker 1: our intrinsic value for Monster relative to its traded price.
Speaker 1: And what's interesting is this thought consistently was trading at
Speaker 1: or below its intrinsic value, and even recently with the
Speaker 1: big runs it has, it's not grotesquely overvalued from our perspective,
Speaker 1: which is what what leads to the tension between the
Speaker 1: way we view the world from an intrinsic value perspective.
Speaker 1: Intrinsic values of function of this level of economic profitability
Speaker 1: how much you're able to reinvest in the business to
Speaker 1: create more economic profits. Then we discount that back to
Speaker 1: reflect the firm's risk based on its size and its
Speaker 1: leverage characteristics. And then lastly we incorporate an overlay. And
Speaker 1: this is where we diverge quite a bit from traditional
Speaker 1: DCF methods that say, okay, we're gonna go out five, six,
Speaker 1: seven years, and then put a terminal value or used
Speaker 1: to Gordon growth model to assume that the rest of
Speaker 1: the world is kind of static. We're going to capitalize
Speaker 1: everything back via perpetuity. We think that's kind of a
Speaker 1: crazy assumption, and you can just talk to kmart about
Speaker 1: how valid the world being constant is. And we apply
Speaker 1: what we call an economic profit horizon, which, again based
Speaker 1: on the research we did, we assign every firm a
Speaker 1: factor based on fundamental characteristics that say, how long will
Speaker 1: this economic profit persist? How long can we expect this
Speaker 1: firm to have an economic margin greater than zero. Because
Speaker 1: once the margin, once your returnees, the cost of capital
Speaker 1: just present value. Math says from that point forward growth
Speaker 1: is irrelevant because the net present value of future growth
Speaker 1: of zero. So all of that a lot of different
Speaker 1: complicated concepts going on at once behind each level, but
Speaker 1: at its most basic, we're basically we're saying, let's figure
Speaker 1: out what the true economic return of a firm is.
Speaker 1: Let's get a handle on how much it's reinvesting in itself.
Speaker 1: Let's let's get some idea on how risky these cash
Speaker 1: flows are, so that from a from an a risk
Speaker 1: adjusted basis, we can compare companies on an apples to
Speaker 1: Apple's perspective. And then let's say companies are not going
Speaker 1: to earn economic profits forever because of competition, so we
Speaker 1: combine those to get an estimate for intrinsic value for
Speaker 1: twenty companies around the world every week. That becomes the
Speaker 1: basis for us looking at the world and then sorting
Speaker 1: out portfolios of companies and Dubai's into cells or creating,
Speaker 1: you know, slicing and dicing that to create subsets of
Speaker 1: products for our clients. What do the tech stocks look
Speaker 1: like under that framework? You know, things like an Apple
Speaker 1: or Facebook or a Netflix. I think it's always useful
Speaker 1: when we're talking about differences in how we're measuring intrinsic
Speaker 1: value to actually speak about concrete examples. And the Monster
Speaker 1: one was a great example just then. But I'm thinking
Speaker 1: something like Apple, you have a lot of profits, a
Speaker 1: lot of investment of very low cost of capital. Does
Speaker 1: it look good in your method of intrinsic value? Let
Speaker 1: me go back a little bit in time as well. Uh,
Speaker 1: and again not in the context of a back test
Speaker 1: or observation, but in context of kind of our our
Speaker 1: main strategy portfolio. If we go back to eleven, and
Speaker 1: this is this is an example that week. We presented
Speaker 1: to some clients the other day because they're kind of
Speaker 1: asking the same type of question. In eleven, we purchased three.
Speaker 1: This is a very low turnover portfolio averages less than
Speaker 1: ten percent turnover a year. But in twenty eleven we
Speaker 1: made three purchases in the tech space, Alphabet and Vidia
Speaker 1: and master Card. Okay, at the time, they were trading
Speaker 1: on a multiple basis well above the market median, and
Speaker 1: from a multiple perspective, those multiples only grew more and
Speaker 1: more expensive relative to the market through time. I think
Speaker 1: we added Apple to the portfolio. Uh five years ago.
Speaker 1: I think we added Facebook to the portfolio. So we
Speaker 1: own all of those stocks and they've all been attractive
Speaker 1: to us from an intrinsic value perspective. In Video, we
Speaker 1: bought in twenty eleven and thirteen dollars a share I
Speaker 1: think in eighteen or at the end of the year
Speaker 1: crashed from three hundred to one fifty. People thought we
Speaker 1: were nuts to have owned it at three hundred. I
Speaker 1: remember having a conversation with a with a really prominent
Speaker 1: journalist and he's like, you guys are crazy. How can
Speaker 1: you call that a stock that's undervalued at one fifty
Speaker 1: after the crash, We're like, we believe it's undervalue, We're
Speaker 1: gonna own it, and then we just sold that this
Speaker 1: past August at We continue to own Google, Apple, Facebook, MasterCard,
Speaker 1: um at the same time, we own what you consider
Speaker 1: to be some classic value stocks. We own Intel, we
Speaker 1: own Hewlett Packard, We own Financials, which is a big
Speaker 1: chunk of the of the value universe, so its valuation.
Speaker 1: This whole dichotomy of value versus growth is really a
Speaker 1: false way to think about firms, because every firm is growing,
Speaker 1: whether it's positive or negative. There's growth taking place for
Speaker 1: every firm. And you know Warren Buffett obviously super successful,
Speaker 1: wise guy. Everything is a function of what you're paying
Speaker 1: for in the market. Please, you can have all the
Speaker 1: stocks that represent an incredible investment opportunity just as easily
Speaker 1: as you can have a stock like Monster represent an
Speaker 1: incredible investment opportunity. It really is a function value. Ultimately,
Speaker 1: true value becomes the intersection of economic profitability, growth, competition,
Speaker 1: and risk. The attractiveness of that is a function of
Speaker 1: what the market's paying for or how psychotic the market
Speaker 1: is at a point in time for any of these stocks.
Speaker 1: Does that make sense? Yeah, I mean talk to us
Speaker 1: further like expanded, pick a pick a name, like okay,
Speaker 1: in video, you tell the story you got it super cheap,
Speaker 1: it's soared. It had that brief crash, I remember that,
Speaker 1: but then it climbed back up in an extraordinary like
Speaker 1: walk is through like what did you see? What year
Speaker 1: did you say you first bought it? Okay, So now
Speaker 1: everyone knows the narrative around and video and video games
Speaker 1: and AI and automotive chips and stuff like that, Like
Speaker 1: what was it at that you saw and were able
Speaker 1: to identify as a you know, it's so great perspective.
Speaker 1: So in video was basically a graphics chip producer, right,
Speaker 1: And this is an interesting view of how the world
Speaker 1: is continually changing around companies and around data. A graphics
Speaker 1: chip producer that we thought was very attractively priced relative
Speaker 1: to its underlying fundamentals. And then over time it started
Speaker 1: to evolve and the at that early in the early
Speaker 1: stages that we owned it, it was it continued to
Speaker 1: be a graphics chip producer designer, if you will, because
Speaker 1: a lot of a lot of these chip makers are
Speaker 1: fabulous now they don't produce but a graphics chip designer,
Speaker 1: and that continued to grow and evolve, and then all
Speaker 1: of a sudden, we started to get little indicators of
Speaker 1: AI and technology continued blossoming in the story. AI story
Speaker 1: became sting for us. The reason we were able to
Speaker 1: stay with it is because as much as it was
Speaker 1: increasing in value because of AI, the company was continually
Speaker 1: delivering on its investments. And even without our analysts modeling
Speaker 1: the company, the company continued to look attractive with our
Speaker 1: systematic valuations that we do for every stock, so just
Speaker 1: on pure as reported data converted into our economic margin
Speaker 1: framework and then an intrinsic value, the company was very
Speaker 1: attractive for years and years and years and years. And
Speaker 1: it wasn't until that these stocks exploded so much after
Speaker 1: that march to client that we painful. We had to
Speaker 1: part ways. You know, we prefer to never sell a
Speaker 1: company a lot of our turnovers because these companies get acquired,
Speaker 1: which is which is kind of like a happy and
Speaker 1: sorrowful moment for us, because we own them. For a reason,
Speaker 1: we only owned fifty stocks in this portfolio, and we
Speaker 1: own across all the sectors, and we're jealous when someone
Speaker 1: buy the stock out below our estimate of it's an
Speaker 1: intrinsic value. It's nice to get the immediate buzz for performance,
Speaker 1: but it really is is kind of annoying that that
Speaker 1: someone's getting such a good deal on our back on
Speaker 1: that note, I'm trying to think how to phrase this question.
Speaker 1: But I guess if you identify a stock that you
Speaker 1: think is undervalued in some way, does that signal something
Speaker 1: about the company, something sort of fundamental to the company,
Speaker 1: that it will perform well in the future, or is
Speaker 1: your measure of intrinsic value maybe correlated with something else
Speaker 1: going on in the market, like a much larger factor.
Speaker 1: I guess I'm trying to get to the flows argument
Speaker 1: um that we briefly talked about in the intro. Does
Speaker 1: any of that make sense? It does? And let me
Speaker 1: take your correlated comment and if I may, let me
Speaker 1: go off on a tangent just for a moment, and
Speaker 1: then we can circle back if if, if, if, the
Speaker 1: conversation returns there, and I'm sure it will. But I
Speaker 1: think the question you're getting at is fundamentally, is intrinsic
Speaker 1: value a causal variable or is it a correlated variable
Speaker 1: with a much larger set of phenomenon. Right, Yes, thank you.
Speaker 1: You put it much better than I did. No, not
Speaker 1: at all, Not at all. The only reason I framed
Speaker 1: it so so quickly is because we think about that
Speaker 1: all the time, and I think it's particularly relevant with
Speaker 1: respect to the cheapness literature. And if if you were
Speaker 1: to think about taking book to price portfolios and intrinsic
Speaker 1: value portfolios, right, and if you were to form groupings
Speaker 1: of them, say the top thirty most undervalued companies, the
Speaker 1: bottom most overvalued companies, and do the same thing with
Speaker 1: a book to price portfolio, and then you have the middle,
Speaker 1: the middle forty, which is some mix of fairly valued,
Speaker 1: somewhat overvalued, somewhat undervalued. We can kind of test to
Speaker 1: see what is driving what by decomposing and deconstructing these returns.
Speaker 1: And if you take that approach and you look at
Speaker 1: book to price, we only are looking at data that's
Speaker 1: live for us. We're not looking at at any simulated
Speaker 1: data or data that we were creating to kind of
Speaker 1: calibrate our estimates of risk or estimates of of economic
Speaker 1: profit horizon, only looking at data that we've produced live
Speaker 1: consistently on a monthly basis. So going back to if
Speaker 1: you look at book to price based stocks and intrinsic
Speaker 1: value based stocks, I would argue that book to price
Speaker 1: is not on a one three five tenure losing street.
Speaker 1: Book to price since has added zero to the investing world.
Speaker 1: And I say that because if you take a look
Speaker 1: at those attractive book to price stocks and you separate
Speaker 1: out the ones that are supported by intrinsic value. In
Speaker 1: other words, those are also attractive intrinsic value stocks, the
Speaker 1: resulting set of book to price stocks generate negative alpha.
Speaker 1: And that's not again, that's not a one three five
Speaker 1: year window, that's a twenty two year window. Book to price,
Speaker 1: if it's not supported by intrinsic value, generates new alpha.
Speaker 1: So then the flip question becomes, okay, well intrinsic value then,
Speaker 1: and a lot of people will say, well, no one
Speaker 1: can trade short against us. No one's going to short
Speaker 1: the high multiple stocks against a value strategy, And that's
Speaker 1: not really true. If you look at high multiple book
Speaker 1: to value stocks that are undervalued, they generate significant positive alpha.
Speaker 1: And so to me, the birth of book to price
Speaker 1: is the result of confusing correlation and causality. Book to
Speaker 1: price has done extraordinarily well. When when that section of
Speaker 1: stocks correlated with intrinsic value do really well and outperform
Speaker 1: the market and carry everyone else along, but by itself unsupported.
Speaker 1: If you strip out the support of intrinsic value, the
Speaker 1: meaning subset of attractive book to price stocks generate negative alpha,
Speaker 1: and the absolute flip happens on the unattractive book to
Speaker 1: price stocks. The only ones with negative alpha are those
Speaker 1: that are really overvalued. The fairly or undervalued high multiple
Speaker 1: stocks generate positive alpha. So this notion of correlation and
Speaker 1: causality is really near and dear to our heart because
Speaker 1: we see an entire industry that was birthed by confusing
Speaker 1: correlation and causality. And that's, you know, one of the
Speaker 1: messages that that we're getting out and we've just started.
Speaker 1: We've just started this message in in earnest recently because
Speaker 1: we just felt we didn't have enough data to construct
Speaker 1: serious arguments. We've been accumulating this data out of sample
Speaker 1: for twenty two years, and then a year and a
Speaker 1: half ago we started to accelerate our efforts to begin
Speaker 1: organizing it to do research the way kind of in
Speaker 1: the FAMA French tradition just so that we have more
Speaker 1: of a of an Apples to Apple comparison, so people
Speaker 1: don't have to unscramble how we're organizing things. And then
Speaker 1: with with the COVID crisis, that gave us a lot
Speaker 1: of time to focus on that, and that's we completed
Speaker 1: this work back in in September and release the paper
Speaker 1: in October. But I think the correlation causality argument is
Speaker 1: really interesting. I think also the quantitative investing world is
Speaker 1: starting to come to grips with some notion of valuation.
Speaker 1: If you look at how that work was extended by
Speaker 1: former French and they motivated their research upfront with a
Speaker 1: dividend discount model, and from that they derived a profitability
Speaker 1: factor saying all all else equel af firms increase their profits,
Speaker 1: they increase their value. And then they have a second
Speaker 1: factor that they added, the investment factor, which I think
Speaker 1: really missed the mark. But I think it's interesting virtually
Speaker 1: everybody in the quantitative value space, any firm that seems
Speaker 1: to be anybody, incorporates this investment factor into their work.
Speaker 1: And what that missed the picture on and it says, okay,
Speaker 1: firms that are investing in their business are expect to
Speaker 1: generate negative future returns. And what that missed from evaluation
Speaker 1: context is you can't separate investment without the return on
Speaker 1: that incremental investment. And so of course, if you only
Speaker 1: make an investment in the firm and it generates no
Speaker 1: future returns, absolutely companies should never invest. But if you
Speaker 1: had the brightest quantitative value investing minds in the world.
Speaker 1: Back in talking to Jeff Bezos and he asked them,
Speaker 1: you know, I'm thinking about these different extensions of my business.
Speaker 1: If they're looking at the profitability factor, they'd say, all
Speaker 1: that's great. But if you're looking at the investment factor,
Speaker 1: they'd say, don't reinvest, don't expand beyond books, because they're
Speaker 1: going to generate negative future returns. And to me, that's
Speaker 1: a worldview that's just missing the wealth creation element that
Speaker 1: comes from a complete valuation framework. Literally, you're biased against
Speaker 1: the greatest in estments of the last sixty years. Not
Speaker 1: tech companies necessarily, of course those are some, but you're
Speaker 1: missing the McDonald's, the Walmarts that the targets, the fisers
Speaker 1: until as a as a manufacturing slash tech company, Apple, Google, Facebook,
Speaker 1: all of these firms have to make huge investments in
Speaker 1: their business. And if you have a worldview that says
Speaker 1: investing is bad, you automatically have at least one aspect
Speaker 1: of your investing world view that's counter to the best
Speaker 1: returning companies in the world. And I think that's just
Speaker 1: a fundamental flaw on that approach. Why don't you know
Speaker 1: when you say that, when you describe it, it sounds
Speaker 1: so obvious that it's kind of weird to penalize these
Speaker 1: companies that are investing in building future technologies and future things.
Speaker 1: We can't conceive of what is their intuition in your view,
Speaker 1: like because it doesn't intuitively, it doesn't seem to make
Speaker 1: any sense at all. But I'm trying understand like accounter argument.
Speaker 1: So the first argument is the data overwhelmingly says it's
Speaker 1: true companies that invest underperformance. That that's an absolute fact
Speaker 1: that I wouldn't argue that with anybody, But I would
Speaker 1: say that's a very naive slice of the data that's
Speaker 1: not accounting for economic profitability and cost of capital. So yes,
Speaker 1: on aggregate, over these big periods of time, say sixty three,
Speaker 1: if you looked at all firms that were reinvesting on aggregate,
Speaker 1: the investment factor turned out to be negative. But I
Speaker 1: don't think that's a really that's a complete view of
Speaker 1: the world because I think it it naively misses the
Speaker 1: key component evaluation, which is that investment gets reinvested to
Speaker 1: generate future returns. So if you then segment companies based
Speaker 1: on their economic margin, positive economic margin firms that are
Speaker 1: growing versus negative economic margin firms that are growing, you
Speaker 1: end up with distinctly different return profiles. We captured this.
Speaker 1: We've been doing this notion of wealth creation and destruction
Speaker 1: through what we call a management quality score for fifteen
Speaker 1: plus years out of sample. Also, when we first created
Speaker 1: this metric back somewhere in the two thousand's, we converted
Speaker 1: this into a financing yield, with the story being, look,
Speaker 1: there's a stewardship aspect to this, and when we look
Speaker 1: at what companies are doing and able to finance their
Speaker 1: business on their own versus having to turn to external capital,
Speaker 1: that captures companies that are growing like an apple, but
Speaker 1: also returning money back to shareholders, and that changes the
Speaker 1: underlying characteristic of just saying growth is bad. Growth is bad,
Speaker 1: but it has to be tempered by what's the underlying
Speaker 1: wealth creation aspect of the company or wealth creation prospects
Speaker 1: of the company to really sign whether growth is a
Speaker 1: positive or negative. The overwhelming data shows the returns are negative.
Speaker 1: And in a fact world agreed, you have a factor
Speaker 1: that's negative, but I think it's a factor motivated by
Speaker 1: really poor theory, which is problematic in my mind. I
Speaker 1: wanted to ask you about that. So we've been focused
Speaker 1: on investment as one path towards growth. We haven't talked
Speaker 1: as much about the capital side, the cost of capital
Speaker 1: and capital funding decisions going into a company, whether to
Speaker 1: buy back shares or whether to borrow from the market
Speaker 1: and add on leverage. How much has that impacted or
Speaker 1: how much has trends in the capital markets impacted the
Speaker 1: performance of traditional value investing in recent years. So that's
Speaker 1: a great question, and it's something we've we've started tackling
Speaker 1: more recently. And that's I think this notion of value
Speaker 1: and growth getting at the heart of this question is
Speaker 1: really more of a duration argument and a duration problem.
Speaker 1: You have from the nineteen sixties to the eighties sort
Speaker 1: of a period of increasing interest rates. Since the late
Speaker 1: eighties to today, we've basically seen decreasing interest rates. I
Speaker 1: think it's just present value math when we when we
Speaker 1: build out economic profit profiles for companies, the higher growth
Speaker 1: companies ultimately end up with much higher durations and a
Speaker 1: much greater sensitivity to what's happening to discount rates than
Speaker 1: what's traditionally viewed as value companies or low duration companies
Speaker 1: that all their assets, all their cash flows are basically
Speaker 1: coming from what they already had in place, and there's
Speaker 1: not much on the on the horizon. So we've had
Speaker 1: a great we've had a great environment for for kind
Speaker 1: of traditionally defined growth stocks since the nineties. Obviously we've
Speaker 1: had periods of increasing and decreasing rates, or many cycles
Speaker 1: within the larger trend of decreasing rates. We're at zero now.
Speaker 1: You know, it wouldn't surprise us to see these value,
Speaker 1: low duration type stocks have their day in the sun
Speaker 1: again for an extended period of time. If we end
Speaker 1: up with some thematic increase in discount rates and cost
Speaker 1: of capital over time, because those further out cash flow
Speaker 1: has become much less valuable to us, doesn't mean that
Speaker 1: they'll be undervalued per se. That we have to see
Speaker 1: how the market, how fast, and how the market digests
Speaker 1: that and prices that if indeed rising interest rates is
Speaker 1: even what happened, I have my my ability you I
Speaker 1: would probably have to pay someone to listen to my
Speaker 1: thoughts on future interest rate increases. So I won't speculate,
Speaker 1: but I can just say from an economic valuation perspective,
Speaker 1: those are the factors that will be at work driving
Speaker 1: you know, longer versus shorter duration investments. So what happened
Speaker 1: going back? You know, since you sort of identify this
Speaker 1: moment in which your sort of intrinsic value framework seems
Speaker 1: to diverge from the cheapness models, is it just about
Speaker 1: the sort of rise of these more intangible based business
Speaker 1: models what we call a tech or software things like that,
Speaker 1: or what sort what explains this diever this period? Since then?
Speaker 1: So I wouldn't say we recognize that our model diverges
Speaker 1: from cheapness? Are we have a very different worldview than
Speaker 1: than what I'd say a typical quantitative value investor is
Speaker 1: a quantitative value investor is generally going to have completely
Speaker 1: signed off on an efficient market hypothesis, because by definition,
Speaker 1: what they're saying is we're not here to create outfit.
Speaker 1: We're just trying to get you a really fair market
Speaker 1: return for all the risk factors you're you're taking on
Speaker 1: any given portfolio. Our view has always been was sismatically
Speaker 1: believe some companies are overvalued, others are undervalued, and probably
Speaker 1: the majority in the middle. There's not enough of the
Speaker 1: difference to really worry about. So it's kind of the
Speaker 1: the plus on each side that really are important to
Speaker 1: construct a portfolio, and with the rest of the stocks
Speaker 1: it's okay, which leads to certain exploiting of specific behavior.
Speaker 1: If you look at passive investing, you know our view
Speaker 1: is their systematically over investing in the overvalued stock systematically
Speaker 1: under investing in the undervalued stocks. So we're we're super
Speaker 1: happy with the rise of passive investing because it's a
Speaker 1: natural segment for us to trade against. Same with growth
Speaker 1: investors and value investors that we believe their views on
Speaker 1: the world systematically lend them themselves to being exploited. The
Speaker 1: rise of intangibles and intangibles are a really interesting topic
Speaker 1: because now you have a lot of people that are
Speaker 1: that are trying to grapple with that and rescue the
Speaker 1: price if you will. I think that a recent paper
Speaker 1: by Campbell, Harvey and Rob ar No saying let's capitalize
Speaker 1: R and D, and let's take of intangibles and of
Speaker 1: s G and a expense and call it intangibles and
Speaker 1: added to book value. And indeed, when they do that,
Speaker 1: what they see is that book value performs a little
Speaker 1: bit better. Going back to that deconstruction of value that
Speaker 1: I mentioned on on as reported book to price, well,
Speaker 1: i'll call I think they called the variable i h
Speaker 1: mL intangible based high minus low book to value book
Speaker 1: to price. It does absolutely nothing relative to intrinsic value.
Speaker 1: All the alpha in that approach is also supported only
Speaker 1: when stocks are undervalued, are overvalued in terms of the
Speaker 1: long short portfolios, and when you capitalize the intangibles. If
Speaker 1: you if you looked at the return profiles they published,
Speaker 1: that was for all the stocks, yes, but if you
Speaker 1: look at the large caps, the large caps really didn't
Speaker 1: do materially better than an unadjusted book to price, and
Speaker 1: certainly on a on an intrinsic value adjusted basis to
Speaker 1: perform poorly. And I think the reason is all of
Speaker 1: these approaches to intangibles are completely missing the picture on
Speaker 1: how to deal with them. They're immediately treating intangibles as
Speaker 1: a valuation issue, and they're saying Google spending a billion
Speaker 1: on intangibles adds to its book value, but so does Macy's.
Speaker 1: And I would tell you the properties of return for
Speaker 1: Google investing a billion dollars versus Macy's and advertising are
Speaker 1: wildly different. And to treat intangibles immediately as a valuation concept,
Speaker 1: I think is completely wrong. What you need to do
Speaker 1: is you first need to treat it as a corporate
Speaker 1: performance concept. You need to answer the question how well
Speaker 1: is the firm performing the reason you're struggling with intangibles
Speaker 1: as you disagree with the accounting convention of conservatism that
Speaker 1: writes it off immediately, and that's fine, Then treat it
Speaker 1: as an investment and figure out what the real rates
Speaker 1: of return the companies generating on investments, just the way
Speaker 1: you would have the factory, assign a life to it,
Speaker 1: put it on the books, and generate and calculating r
Speaker 1: o I. Companies that consistently are generating significantly positive returns
Speaker 1: on that investment will start to see their r o
Speaker 1: I s increase as they continue spending. Companies that don't won't.
Speaker 1: So Macy's can spend a lot on intangibles and R
Speaker 1: and D whatever, R and D would be for them,
Speaker 1: and it's not going to move the needle on their
Speaker 1: valuation much so to just say they're cheaper because they've
Speaker 1: spent this money on advertising, I think it's just it's
Speaker 1: a super naive view of understanding what valuation is all about.
Speaker 1: It's a convenient view because it lends itself to a
Speaker 1: lot of you know, in the classic mean variance factor world,
Speaker 1: that's a convenient view, but I don't think it's anywhere
Speaker 1: near the appropriate view. And the other the other aspect
Speaker 1: of a lot of these studies is going back to
Speaker 1: this notion of back tests. I don't believe you can.
Speaker 1: You can start to look at it a variable and
Speaker 1: go back time and look at it, and even if
Speaker 1: you don't have any mal intent, it's really hard to
Speaker 1: not know. Over the last ten fifteen years technology firms,
Speaker 1: which are the ones spending the most on R and D.
Speaker 1: I've been the ones that have exploded in stock price.
Speaker 1: You don't have to have mal intent, but everybody knows
Speaker 1: that unless you've lived in a shell. So it's hard
Speaker 1: to be an objective researcher and say, well, the solution is,
Speaker 1: let's add back R and D and we get a
Speaker 1: better metric. Any of these finance studies that are looking
Speaker 1: backward in time and consistently drawing back test results and
Speaker 1: claiming they're real. I think you need to put in
Speaker 1: the hard work and effort. Every time you changed your model,
Speaker 1: your track record stops and you need to start tracking
Speaker 1: what's the efficacy of the model going forward. You can't
Speaker 1: say I launched this model ten years ago, I've improved
Speaker 1: it now because of this additional variable that I've backed
Speaker 1: tested and added to the model. I don't think so
Speaker 1: the models reset when you added this new variable to it. Buddy,
Speaker 1: That's just the way it is. We waited twenty two
Speaker 1: years to kind of make this explicitly public because we
Speaker 1: wanted to make sure there was enough data and the
Speaker 1: data is growing to really do the study properly. And
Speaker 1: I think that's a standard everybody needs to do it
Speaker 1: here to. You can't. You can't create a valuation model
Speaker 1: looking at historic data when you know how you're estimating
Speaker 1: these parameters to assign risk and it fits for that
Speaker 1: period and say you have a great model. Yeah. It's
Speaker 1: great to observe, and observations are an important part of science.
Speaker 1: You have to observe, you formulate your theory, you formulate
Speaker 1: your model, then you have to start calculating the data,
Speaker 1: and you have to let the data theri and age
Speaker 1: live out of sample. It's it's it's painful and it's costly,
Speaker 1: but it's really the gold standard. And Harvard Campbell even
Speaker 1: wrote a paper describing how big of a problem this
Speaker 1: potential for forward bias, you know, foresight bias and inadvertent
Speaker 1: data snooping is in financial research, and he said, clearly
Speaker 1: the gold standard is live out of sample data. But
Speaker 1: it's just very difficult to obtain. And that's one of
Speaker 1: the things that I think really sets us apart from
Speaker 1: any other firms. We've made the investment in time, because
Speaker 1: you can't short change the time. We've made the investment
Speaker 1: in time to get that data and to offer our
Speaker 1: results relative to other data that's been developed through backward
Speaker 1: looking approaches, and we still came out on top. If
Speaker 1: you look at in this valuation data research paper we
Speaker 1: rereleased in October, we systematically reconstructed an asset pricing framework
Speaker 1: looking at all the popular value quantitative value factors, profitability,
Speaker 1: book to price, the investment factor, momentum, low volatility. They
Speaker 1: all basically are subsumed by these concepts of intrinsic value,
Speaker 1: wealth creation or stewardship, and leverage. And what is quantitative
Speaker 1: value investing When you start adding factors such as momentum
Speaker 1: and volatility there, what's the identity of that field? What's
Speaker 1: the theory that links price, momentum and volatility to thetrinsic
Speaker 1: value of the stock? I think it started as a
Speaker 1: as a very clearly defined having a very clearly defined identity,
Speaker 1: i e. This book to price is a factor that
Speaker 1: either represents a behavioral problem that people are missing this information,
Speaker 1: or it's some sort of embedded risk factor. But then
Speaker 1: over time is that factor hasn't worked and they've continually
Speaker 1: added on. I think quantitative value investing the value component
Speaker 1: has lost a lot of its identity. What is it exactly?
Speaker 1: It's okay to have to be a quantitative investor, but
Speaker 1: I'm not sure. Again, getting back to our initial discussion
Speaker 1: at the top of the show, value doesn't belong in there.
Speaker 1: That needs to be reclaimed in a more appropriate manner.
Speaker 1: I think. So I'm going to try to squeeze in
Speaker 1: too hopefully interrelated questions here, But how dynamic are your
Speaker 1: own models? Then? I know, you just very much criticize
Speaker 1: people that are constantly changing their models in order to
Speaker 1: fit new data coming in. And secondly, what did you
Speaker 1: learn from how did your funds actually perform and did
Speaker 1: you make any changes off the back of that. How
Speaker 1: dynamic are models? We pretty much locked in our models
Speaker 1: in Nothing has changed structurally to what we do, the
Speaker 1: way we estimate uh the persistence of economic profit, the
Speaker 1: way we calculate cost of capital risk adjust cost of
Speaker 1: capital for a company. There have been along the way
Speaker 1: what we considered to be minor accounting changes. For instance,
Speaker 1: recently Accountant started capitalizing lease's last year and putting them
Speaker 1: on the balance sheet. We have to undo those because
Speaker 1: we think they actually did it incorrectly for a number
Speaker 1: of reasons that are way way more geeky than probably
Speaker 1: the show needs to get into at the moment. But
Speaker 1: there's always little adjustments like that. But the fundamental thrust
Speaker 1: of the model has not changed, since it's a very
Speaker 1: dynamic model because market prices are always changing and the
Speaker 1: performance and strategy of firms are always changing, so that's
Speaker 1: constantly at work. So the answers, we haven't really changed
Speaker 1: the model. I feel comfortable with my criticism because we
Speaker 1: haven't done that. There's been lots of time where our
Speaker 1: performance hasn't been what we wanted it to be. The
Speaker 1: going back to the first part of you know, the
Speaker 1: value guys weren't in this boat alone. We we performed
Speaker 1: poorly relative to the S and P I. We we
Speaker 1: were benchmarked in the value category. Our mutual fund in
Speaker 1: that space significantly outperformed the value indexes last year. Since
Speaker 1: we started today, we've outperformed the S and P with
Speaker 1: this with kind of our core strategy, the valuation fifty strategy.
Speaker 1: What we learned in twenty is that it's it's really
Speaker 1: two interesting points from one. What we learned is the
Speaker 1: same lesson we've learned over and over. It's very difficult
Speaker 1: to be true to your process. Sometimes it's it's extraordinarily
Speaker 1: painful when you when you see the world is trading
Speaker 1: against you every day and you're underperforming. Yet you need
Speaker 1: to hold on because if these are central truths you
Speaker 1: believe in, this is what your investment clients purchased from you,
Speaker 1: and this is what they expect they're getting, and that's
Speaker 1: what we delivered. So in I think we had two
Speaker 1: trades to the portfolio. One was selling in video UH
Speaker 1: at five plus in August after it just run well
Speaker 1: above finally exceeded our estimate of its intrinsic value, and
Speaker 1: we we had to sell as we were crying pushing
Speaker 1: the button. It had become kind of an old friend
Speaker 1: of our since and a great performer. The other interesting
Speaker 1: thing of in our history of twenty five years of firm,
Speaker 1: we've issued four market calls. Basically, one was in two
Speaker 1: thousand when we thought the market was overvalued along with
Speaker 1: everybody else. We don't. We don't think our insight was
Speaker 1: necessarily unique, although the a we went about it I
Speaker 1: think was kind of fun. With Cisco, we showed kind
Speaker 1: of what the expectations built into Cisco's price were in
Speaker 1: two thousand of the process we called we created back
Speaker 1: in ninety eight called value Expectations, where we take our
Speaker 1: model and we reversed out the performance implications of a
Speaker 1: of a stock price and we showed Cisco basically had
Speaker 1: to grow it plus sales for the next five years.
Speaker 1: In twenty the end of two thousand and eight, two
Speaker 1: thousand of nine, we came out with another market call
Speaker 1: that we said the market was just extraordinarily undervalued. That
Speaker 1: that was fun because I happened to be on CNBC
Speaker 1: mentioning that at the time, and they thought I was nuts.
Speaker 1: And then this year, with all the volatility, we made
Speaker 1: to market, called one in March saying the market's really undervalued.
Speaker 1: It reminded us of two thousand and eight, and then
Speaker 1: to us finally the market became overvalued in August of
Speaker 1: this year. Now, obviously we've been way wrong. We didn't
Speaker 1: in August. We didn't say, uh, this is an absolute
Speaker 1: time to get out, but we did say statistically, we
Speaker 1: think the market is expensive. We'd be very cautious here.
Speaker 1: You know, who knows whether it'll be right or not
Speaker 1: with that, But you know, that's just what our models
Speaker 1: indicated to us. So that's that's the way we're approaching
Speaker 1: the world. I think it's important to stick to your discipline,
Speaker 1: and that's what stick to your discipline. The market isn't
Speaker 1: always efficient, but it sure is generally rational through time.
Speaker 1: And like I said, a lot of stocks that that
Speaker 1: it underperformed continued to do really well after March, and
Speaker 1: if we looked at our returns last year, we underperformed
Speaker 1: the SMP year to date this year, that fund is
Speaker 1: outperformed the SMP by more so from the start of
Speaker 1: we're up this process. We launched this strategy in two
Speaker 1: thousand and four. It's significantly up on the SMP since
Speaker 1: inception over the last ten years. As I mentioned, over
Speaker 1: the last year plus, I think over three years. It's
Speaker 1: probably down over five years. It's probab we are I
Speaker 1: don't really I don't really track all the return profiles
Speaker 1: because we don't. You know, the turnover on this portfolio
Speaker 1: is about ten percent a year for to five stocks
Speaker 1: a year, unless we have companies that are acquired. So
Speaker 1: what do you do? I mean you you said you
Speaker 1: sold in video. You said in August you identified, uh,
Speaker 1: the S and P or the market overall is being overpriced.
Speaker 1: What do you do from a portfolio perspective? What kind
Speaker 1: of shifts do you make or what are what are
Speaker 1: the implications for you when you make an overall market
Speaker 1: valuation call. In this particular portfolio, it's a long only fund,
Speaker 1: so we aren't going to do anything and the charter
Speaker 1: is to be fully invested. So we sold in video,
Speaker 1: we replaced it um and again this is this kind
Speaker 1: of speaks to why valuation is a little quirky. Let
Speaker 1: me just give you a little a little background prior
Speaker 1: to this. The direct answer to a video we the
Speaker 1: direct answer to the video, we sold the video. We
Speaker 1: bought k l A, the semi conductor equipment maker. But
Speaker 1: the background of that that makes that particular purchase interesting.
Speaker 1: And if you go back to the composition of this
Speaker 1: portfolio is approximately twenty five stocks that would be classified
Speaker 1: as sort of value or core, twenty five stocks that
Speaker 1: would be classified as sort of core or growth. Over time,
Speaker 1: as the market has kind of pushed up the valuations
Speaker 1: of growth stocks, the portfolio is naturally then shifting its
Speaker 1: marginal trades away from shading growth, getting more and more
Speaker 1: into value. By August of this year, the composition of
Speaker 1: the portfolio was approximately seventy seventy of the portfolio was
Speaker 1: in value core stocks. In core value stocks. KLA Corp.
Speaker 1: Happens to be at growth stock. So it's it's it's
Speaker 1: hard to just pigeonhole what we're what valuation says, because
Speaker 1: it doesn't necessarily just have to put you in a
Speaker 1: in a quote non growth value stock or or a
Speaker 1: high flying growth stock. It really is constantly shifting between
Speaker 1: what the market is giving us, and so would we
Speaker 1: When we pulled the trigger on video, we bought kl
Speaker 1: A Corps and immediately KLO Court dropped ten percent, which
Speaker 1: is always the case when we big a trade, which
Speaker 1: is really annoying. But since then it's it's done. A
Speaker 1: really nice job. Routy that Ruffael, that was. That was
Speaker 1: a great conversation. Any other sort of last thoughts or
Speaker 1: key things that you think we should, uh our listeners
Speaker 1: should think about. No, that was. That was a lot
Speaker 1: of fun. I hope we can do again sometimes. That
Speaker 1: was a lot of fun. Well, yeah, let's definitely do
Speaker 1: it again sometime. And I really appreciate you joining it.
Speaker 1: Thank you both, Thanks so much so Trazy, I thought
Speaker 1: that was that was super interesting. I mean, obviously we've
Speaker 1: talked about a lot of these themes before, resuscitating value,
Speaker 1: reviving value, intrinsic trying to come up with some new
Speaker 1: concept of intrinsic worth based on intangible assets, and I
Speaker 1: like that. It feels like their work tries to just
Speaker 1: go about the problem differently, rather than starting from this
Speaker 1: premise that there are ratios or book that book value
Speaker 1: is a useful idea sort of defined value, but not
Speaker 1: within not within the old constraints, right, although it does
Speaker 1: get me thinking if whether you know, one of the
Speaker 1: enduring mysteries of our investment age is why value investing
Speaker 1: hasn't performed better, it makes me wonder whether or not
Speaker 1: like it all just comes down to the definition and semantics.
Speaker 1: And you know, I guess we kind of touched on
Speaker 1: this in the intro, but if you have a completely
Speaker 1: different definition of value investing, then hey, it actually works.
Speaker 1: I guess maybe we the way, and I'm sure this
Speaker 1: won't be our last conversation on the topic, But I
Speaker 1: wonder if the better question is why don't cheap stocks
Speaker 1: do better? Because I sort of like, I mean, ultimately,
Speaker 1: or why don't or why don't the sort of the
Speaker 1: traditional value factors do better? Because it seems like um
Speaker 1: Raphael's criticism isn't that what the concept of value value
Speaker 1: investing per se obviously because he is sort of in
Speaker 1: that category, but just in this idea that the sort
Speaker 1: of like the traditional like farmer French factors that one
Speaker 1: at one point seemed to point to outside returns no
Speaker 1: longer do well, is you gonna say? Then we can
Speaker 1: at least sort of define what we're talking about better?
Speaker 1: Because you know, again, if anyone can sort of redefine value,
Speaker 1: then you can never really prove that it's working or
Speaker 1: not working. But if we could start our convert station
Speaker 1: with why don't these traditional ratios work the way they
Speaker 1: used to? Why does it price to book, why does
Speaker 1: it price to earnings work the way we used to,
Speaker 1: then at least we can define the debate. Yeah. I
Speaker 1: think that's a good point. Um. I also liked how
Speaker 1: strongly he feels about back testing, uh and sort of
Speaker 1: like fitting your thesis to the data. And also this
Speaker 1: idea of if you're making substantial, substantial changes to your
Speaker 1: model every month or every year or whatever, you're basically
Speaker 1: starting from scratch. I don't think you hear that very
Speaker 1: often among a systematic investors. So that was fun. He
Speaker 1: also had some like pretty like strong negative words towards
Speaker 1: you know, what we called quant investing, and so you
Speaker 1: you know, the idea that you could have one person
Speaker 1: who's a sort of quant investor who looks at things
Speaker 1: like price to book, another person who's a quant that
Speaker 1: looks like at momentum factors, they're not out really can
Speaker 1: as he put it, you know, maybe some people argue
Speaker 1: they would they're not. Really, They're not intuitively connected. It's
Speaker 1: not obvious why they should be under the same family
Speaker 1: of thought about investing or about stock picking. Sort of
Speaker 1: ratio based investing is obviously intuitive to the company. Momentum
Speaker 1: factors are intuitive to the price of the stock itself
Speaker 1: or related to the price of the stock itself. It's
Speaker 1: pretty different stuff that ends up getting lumped into one
Speaker 1: broader category that we call quant Yeah. I think that's
Speaker 1: a good point. Should we leave it there? Yeah, let's
Speaker 1: leave it there. Okay. This has been another episode of
Speaker 1: the All Thoughts Podcast. I'm Tracy Alloway. You can follow
Speaker 1: me on Twitter at Tracy Alloway and I'm Joe Why
Speaker 1: Isn't Though? You can follow me on Twitter at the Stalwart.
Speaker 1: Follow our guests on Twitter. Raphael Ascendez He's at our
Speaker 1: Ascendas and check out check out their paper that he
Speaker 1: co off Third Valuation Beta, addressing inadequacy inadequacies of book
Speaker 1: to price with intrinsic value, stewardship and leverage that's available
Speaker 1: for download online. Follow our producer Laura Carlson, She's at
Speaker 1: Laura M. Carlson. Followed the Bloomberg head of podcast, Francesca Levie,
Speaker 1: at Francesca Today and check out all of our podcasts
Speaker 1: under the handle at podcasts. Thanks for listening.