Odd Lots - A Value Manager on How Most Value Managers Are Getting It All Wrong

Episode Date: February 18, 2021

As you might have heard, so-called value investing has not had a good run. At least from a quantitative standpoint, strategies that aim to buy low-valued stocks (based on metrics such as price-to-earnings or price-to-book) are quite out of favor, as fast growing names, loaded up on intangible capital, have outperformed. So is there any way to resuscitate the concept of value, or do investors just need to wait for the tides to change? On the latest Odd Lots, we speak with Rafe Resendes, a portfolio manager and co-founder of the Applied Finance Group, who argues for another way of reconceptualizing value, beyond just cheapness, in a way that works across market environments.What do you love about Odd Lots? What topics do you want to see on upcoming episodes? Share your feedback about the show by completing our first-ever listener survey.See omnystudio.com/listener for privacy information.

Thanks for listening to Odd Lots. Follow the show on Amazon Music for more future episodes or just ask, Alexa, play the Odd Lots podcast on Amazon Music. Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Wisenthal. And I'm Tracy Allaway. Tracy, you know, one of the things that we discussed a fair number of times in 2020, but also maybe before, is just sort of the, I guess you would say, permanent state of crisis that you might call it. in so-called value investor? Yes. It's been, it's certainly been a long-running theme, hasn't it? And I feel like we've done quite a few episodes on it at this point.

But 2020 didn't exactly turn things around for value investors either. No, definitely not. And I think, like, you know, there's really two strands that tend to emerge in our conversations. And I would characterize them as this. One is that there is some like cyclical element to value investing in that this cycle just hasn't turned around yet. So it's like we have this sort of long, multiple expansion. It's kind of low growth, Fed-driven economy that sort of creates this permanent bid for growth companies at the expensive value, weak GDP growth, et cetera, just like certain industries aren't going to thrive in that environment.

And then I would say the other half is that, no, the problem is. that we are not good at defining value. And there are people who sort of take a more accounting approach and say traditional measures of the stock's cheapness, like say price to book value. It just these measures don't work in a world where so much value is intangible. Right. This is the idea that as companies, well, the sort of successful companies nowadays are investing a lot. They're investing a lot in things like their brands and other intangible assets that just aren't captured by price to book value. And so a company that's positioning itself for a really good future performance might not actually

look that way if you're just glancing at, you know, something like price to book. You know, like it's sort of a joke. And I think we've talked about it with Michael Mobison last year, Jared Woodard. But it's like you can rescue value investing if you call Netflix a value stock or if you call Facebook a value stock. And someone would say, okay, these companies don't have like big, like, factories that can easily be measured. But if you could somehow, like, put a number on the value of the Facebook network as an asset, then you could then you could theoretically imagine a world in which on some traditional value screen, Facebook comes up. Right.

I mean, the thing that still makes me uncomfortable about value stocks is that you're still investing in something on the basis. that the market has somehow mischaracterized its future, which I think you touched on this with your two narratives, but I think in the current environment where we talk a lot about flows-based investing, a lot of momentum trading, things like that. It feels like the market is quite consistently directing capital to a few firms, and then it just keeps doing that. and those firms get overvalued and overvalued and overvalued. And if you're not in that cycle, I don't know. I just feel like it's unlikely that you're going to get back in it.

And the longer it takes you to get in, the more you're sort of losing out in terms of valuations. But anyway, sorry, I'm going on a tangent. No, no, it's great. And I mean, the other thing, and I think it's sort of related to your point is, you know, it's nice to say, like, okay, like there's some intangible value that we, if we could only measure it at Netflix or Facebook. Facebook or whatever it is.

But it seems hard to know in advance that it's there. And so it's like, okay, if you have some like factory, then you can at least say, okay, this exists. And historically speaking, it will, you know, we would project it over the next 10 years. It's going to throw up this cash. And that's a good value. It feels like with a lot of these sort of like backing into the value approach that it's very much easier to say in retrospect or ex post facto, oh, this turned out to be a

a very valuable asset that they have, which is sort of nice, I guess, from maybe an intellectual standpoint, but it doesn't really help you pick stocks today, which is what people ultimately care about these discussions. It's great to say, okay, the Facebook network is worth a lot of money, but, you know, I wish you had told me that six years ago. No, but it does help a lot of different investment companies come up with different strategies and factors to come up with different definitions of value that always work when they're back tested against historical data. Right. So I guess the question is...

That's sarcasm. I don't know if my sarcasm is coming through on the podcast, but... I got it, Tracy. But I guess the thing we're looking for is the approach that works in advance that's not just back tested and some approach that can help us identify the sort of deeply under-value companies today using some... something, whether it's a metric, whether it's a screen, whether it's some other intangible measure, something that doesn't just help us rationalize the past, but can, you know, help me retire a few

years earlier. Yeah. And also maybe even explain why value investing, as it's, you know, commonly understood has performed so badly for so long. Absolutely. All right. So I'm very excited. We're going to be speaking with someone today who may be able to help move this forward, whose work consists of sort of solving this problem, an investor. So obviously someone who wants to do more than just explain the past, would produce good returns going forward. We're going to be speaking with Raphael Resendez. He is the co-founder of Applied Finance Capital Management, which has looked at some of these problems and tried to identify where the traditional and value investing framework has gone wrong. So Raphael, thank you very much for joining us. Great to be here. So I'm curious,

I mean, you know, I always like to look for validation after our intros, but that general framework of sort of the two stories that people tell about value investing, how does that comport with how you see the world right now and how you see this sort of ongoing debate? Great, great question. So as you know through some of our exchanges, applied finance focuses on on valuation. I think to some degree, the term value has been hijacked historically with the advent of or the release of the Fama French three-factor model back in 92 and the creation of this quote value factor, otherwise known as the book to price ratio.

I think the term value has really been hijacked because book to price at the end of the day is really nothing more than a cheapness metric, as are all the price to something measures. It's measuring the price of a stock in relation to some fundamental variable, whatever that variable is or even composites of that variable. But it doesn't necessarily relate to the worth of a stock. And I think that fundamentally is where things have really tended to diverge or go off the rails to some degree in investing finance. The focus has been on cheapness and there hasn't been enough attention paid to understanding intrinsic value through more complete valuation approaches, which is what we've specialized in since

1995. So applied finance has been in existence since 1995, but we were chatting before we started recording the podcast. And you mentioned that your current area of focus is something that grabbed your interest because of a big debate that was happening in the 1970s. Could you maybe explain that a little bit? Sure, sure. So let me just provide a little historic background on how we are where we are today. If you think about how finance has evolved, you know, in the 60s, you had a lot of theoretical work on the CAPM model, which was really exciting.

And then in the early 70s, you started to see this, basically this brand new field of finance created by Eugene Fama, which is empirical finance. And he did amazing work testing and kind of formulating the efficient market hypothesis. and ushering a whole new era of scientific method applied to finance, so to speak. And I think prior to that, for the most part, university-level finance focused on net present value and a lot of security analysis. And in a great paper that Fama wrote, I believe it's titled, you know, my memories in finance or something to that extent, he essentially says, you know, when I arrived at Chicago, So finance consisted of classes essentially teaching people to pick stocks.

And in an efficient market world, there's not a lot of value to having so many people specialized in that. And what we saw in the 70s was this birth of mean variance finance, where computer methods and data availability allowed for incredible study of properties of stock prices, testing of hypotheses of, you know, a lot of the mainstream thoughts back then. we're buy a stock just because it's going up. So a lot of efficient market test focused on momentum investing, which is kind of ironic given the crave of momentum investing now,

you know, 30 years later, 50 years later. And ultimately it drove security analysis to a large degree out of finance. And you see a lot of security analysis, of course, is now being taught in accounting departments. And what we tried to do at Applied Finance when we started in 95 was essentially try to link this notion of mean variance, finance, and security analysis. And we did that through systematically constructing security analysis rules to process data on companies, and then ultimately linking that the output of those reformulated income statements and balance sheets from accounting data

into more of an economic framework through the calculation of an economic margin, which essentially is a firm's return on investment less its cost of capital. we linked that with expected capital growth, risk, and competition to value a company. And so we did this work from the 70s to 95, kind of a 25-year, what I'll call an observation window. And I think Tracy, going back to one of your statements you made earlier about back testing, it'd be fun to get into that in a little bit more depth later. So I'll call it an observation window rather than an evidence window. I think there's a big distinction between the two.

And beginning in 1996, we began calculating intrinsic value estimates first for U.S. companies on an ad hoc basis. I'd say monthly and quarterly. When we were early on in our company development, we were doing a lot of corporate consulting work. We didn't have the full infrastructure of personnel to handle calculating what I'll call productions of intrinsic value consistently. By 1998, the company had grown, and we began calculating these intrinsic values monthly. And from 1998 through today, we continue doing that. Now we do it globally on 20,000 companies every week. But what's interesting is backtest observations versus evidence.

Since 98, these have all been live out of sample estimates of intrinsic value. And I think one of the interesting things about our data set and what we use to prepare this paper called valuation beta is that a lot of finance says, well, let's go back to 1963 and someone will say, no, let's go back to 1930 and someone will say, no, I have a data series going back to the 1700s. Well, at some point, this data is really irrelevant because the world has changed so much. Maybe there are these, quote, fundamental truths, but if you look at the factor work, oftentimes these factors are discovered in a back test setting, and then when they go live, they don't work. And certainly that's been an important part of the experience with Book to Price since 1992, it's had prior to 92, from 63 to 91, it was basically a money-making

machine. The return attributes of that variable were extraordinary. Since 92, it's had a much more spotty record. And the same as if we dig down a little deeper on some of these other factor metrics, such as the profitability factor, the investment factor, since they were released in 2015 publicly, they've had a spotty record as well. So I think it's important to, As you said, to differentiate between back testing, which is when you're kind of formulating your ideas versus evidence, which happens after you've released your ideas and you let the world run. As Mike Tyson likes to say, everyone has a plan to your punch in the face. And for book to price and value investing, that punch in the face has happened over the last decade when it basically hasn't worked.

On April 4, 2023, around two in the morning, a man was found stabbed. multiple times on a sidewalk in downtown San Francisco. Hey, we did this to you. What happened next turned the story into a political firestorm. Reports have identified the victim as Bob Lee, the founder of Cash App. From Bloomberg Podcasts, this is Foundering, the Killing of Bob Lee, beginning April 16. So you make this distinction between value, valuation versus cheapness. and as you characterize that measures of price to book or really any other sort of ratio-based

valuation, it's not a measure of value per se. It's just a measure of cheapness. So can you explain a little bit further like what the difference is between what your measure of valuation and cheapness and why it is in your view that some of these traditional metrics measures of cheapness that at one point did produce returns have failed to do so. Let me focus first on explaining our worldview and kind of how we get to an answer. And there's probably lots of reasons to speculate about why something doesn't work, but I'll offer our view of it as well. So first, as opposed to beginning with some metric of cheapness,

what we begin our discussion with is an estimate of intrinsic value. So how do we go from accounting data to an estimate of a firm's intrinsic value? So we begin, we process the data, and there's a lot of, around the world, there's a lot of accounting issues that we've dealt with and tried to incorporate systematically in our analysis, but I'll just talk about a few that I think make for a quick, interesting discussion. Since the beginning of our company, we viewed research and development as an investment rather than an expense. So since 95, we've capitalized R&D, and our view is this is a piece of operations-based cash flow the company is generating currently. Another item is the use of operating

leases. The accountants historically have treated that as an expense rather than anything having to do with the balance sheet systematically. In the last couple of years, FASB's kind of come around to our view of thinking and as required companies to start capitalizing operating leases. We want to view companies on a capital structure free basis, so we're going to add back interest expense on an after-tax basis. We're going to make adjustments to the balance sheet for inflation. It sounds a little silly now, and it certainly is silly for technology companies that have very little physical plant, but for industrial firms that have assets on their books that

they acquired in the 70s, inflation is a big deal because it's easy to have inflated return on investments because the balance sheet's reflecting historic cost, the income is respecting is displaying current values, current dollar values. So there's a litany of adjustments we go through. We do all that. And one last thing that we account for is how long a company's assets last on average to get an asset life.

So then we can configure a firm as a project. And we can calculate what we call an economic margin. We don't have to get into all the geek speak on that. But essentially, at the end of the day, what we end up with is a corrected return on investment, on an inflation-adjusted asset base minus a cost of capital. That spread or economic margin starts to tell us a lot of things about the company. First of all, if you have an economic margin that's negative,

it tells you the firm is investing in projects below its cost of capital. So the last thing we'd want to see a firm like that do is grow its business. That's what we call wealth destroyers. And it's a very key piece of how we think about analyzing companies. and what they're doing. If you have a negative spread to your cost of capital, you should be shrinking your business or rationalizing what you have to figure out how to at least get to a zero spread. Companies that have a positive spread, and again, this is after accounting for things such as investments in R&D, off-balance sheet, leases, so on and so on, companies that have a positive

spread should grow. And I think, Joe, in one of your tweets a while back that initiated our discussions, you asked a question about Monster. And you said, can I, anyone explain monster. To us, the monster story is pretty simple. This is a firm that's- Just so people know this is the energy drink maker with lots of caffeine. That's the best performing stock. Go on.

I just wanted to, just for people who are- Great, great. It's been an incredible wealth-compounding company through the years. It's not a recent thing. You know, this is a firm that's earning 10 to 15% returns above its cost of capital and has been growing its capital base at double digits. So you have, this is a classic example of what we call a wealth creator, a compound wealth creator, generally reinvesting in high rates of return, creating more and more wealth for the existing shareholders,

and the company continues growing into its valuation. I think we sent you a chart that traces out our estimate of monsters intrinsic value through time. And again, these are basically live estimates going back. This is, in this case, going back 10 years at least, but it traced out our intrinsic value for Monster relative to its traded price. And what's interesting is this stock consistently was trading at or below its intrinsic value. And even recently, with the big runs it has, it's not grotesquely overvalued from our perspective, which is what leads to the tension between the way we view the world from an intrinsic value

perspective, intrinsic value is a function of this level of economic profitability, how much you're able to reinvest in the business to create more economic profits. then we discount that back to reflect the firm's risk based on its size and its leverage characteristics. And then lastly, we incorporate an overlay, and this is where we diverge quite a bit from traditional DCF methods that say, okay, we're going to go out five, six, seven years, and then put a terminal value or use the Gordon Growth model to assume that the rest of the world is kind of static. we're going to capitalize everything back via perpetuity. We think that's kind of a crazy assumption. And you can just talk to Kmart about how valid the world being constant is.

And we apply what we call an economic profit horizon, which again, based on the research we did in 95, we assign every firm a factor based on fundamental characteristics that say, how long will this economic profit persist? How long can we expect this firm to have an economic margin greater than that? than zero because once the margin, once your return equals the cost of capital, just present value math says from that point forward, growth is irrelevant because the net present value of future growth is zero. So all of that, a lot of different complicated concepts going on at once behind each level, but at its most basic, we're basically saying, let's figure out what the true economic return of a firm is. Let's get a handle on how much it's reinvesting in itself. Let's get some idea on how risky these cash flows are from a risk-adjusted basis,

we can compare companies on an apples-to-apples perspective, and then let's say companies are not going to earn economic profits forever because of competition. So we combine those to get an estimate for intrinsic value for 20,000 companies around the world every week. That becomes the basis for us looking at the world and then sorting out portfolios of companies into buys and to sales or create. you know, slicing and dicing that to create subsets of products for our clients.

What do the tech stocks look like under that framework, you know, things like an Apple or Facebook or a Netflix? I think it's always useful when we're talking about differences in how we're measuring intrinsic value to actually speak about concrete examples. And the monster one was a great example just then. But I'm thinking something like Apple, you have a lot of profits, a lot of investment, a very low cost of capital. Does it look good in your method of intrinsic value? Let me go back a little bit in time as well. And again, not in the context of a back test or observation, but in context of kind of our main strategy portfolio. If we go back to 2011, and this is an example that we presented to some clients the other day

because they're kind of asking the same type of question. In 2011, we purchased three, this is a very low turnover portfolio. It averages less than 10% turnover a year. But in 2011, we made three purchases in the tech space, Alphabet, Invidia, and MasterCard. At the time, they were trading on a multiple basis well above the market median.

and from a multiple perspective, those multiples only grew more and more expensive relative to the market through time. I think in 2013 we added Apple to the portfolio. Five years ago, I think we added Facebook to the portfolio. So we own all of those stocks, and they've all been attractive to us from an intrinsic value perspective. In Vidia, we bought in 2011 at $13 a share. I think in 2018 or 2018, at the end of the year, it crashed. from 300 to 150. People thought we were nuts to have owned it at 300.

I remember having a conversation with a really prominent journalist, and he's like, you guys are crazy. How can you call that a stock that's undervalued at 150 after it had crashed? We're like, we believe it's undervalue. We're going to own it. And then we just sold it this past August at 520. We continue to own Google, Apple, Facebook, MasterCard. At the same time, we own what you consider to be some classic,

value stocks. We own Intel, we own Hewlett-Packard, we own financials, which is a big chunk of the, of the value universe. So valuation, this whole dichotomy of value versus growth is really a false way to think about firms because every firm is growing, whether it's positive or negative, there's growth taking place for every firm. And, you know, Warren Buffett, obviously super successful, wise guy. Everything's a function of what you're paying for in the market. place. You can have value stocks that represent an incredible investment opportunity just as easily as you can have a stock like Monster represent an incredible investment opportunity. It really is a function. Value ultimately, true value, becomes the intersection of economic profitability,

growth, competition, and risk. The attractiveness of that is a function of what the market's paying for or how psychotic the market is at a point in time for any of the stocks. Does that make sense? Yeah, I mean, talk to us further. Like, pick a name. Like, okay, Nvidia, you tell the story you got it super cheap. It's soared. It had that brief crash.

I remember that. But then it climbed back up and extraordinary. Like, walk us through. Like, what did you see? What year did you say you first bought it? 2011. Okay. So now everyone knows the narrative around.

and V-Vidia and video games and AI and automotive chips and stuff like that, like, what was it at 2011 that you saw and were able to identify as a, you know, so great perspective. So, NVIDIA in 2011 was basically a graphics chip producer, right? And this is an interesting view of how the world is continually changing around companies and around data. A graphics chip producer that we thought was very attractively productive. relative to its underlying fundamentals.

And then over time, it started to evolve. And in the early stages that we owned it, it continued to be a graphics chip producer, designer, if you will, because a lot of these chip makers are fabulous now. They don't produce. But a graphics chip designer, and that continued to grow and evolve. And then all of a sudden we started to get little indicators of AI. and technology continued blossoming and the AI story became interesting.

For us, the reason we were able to stay with it is because as much as it was increasing in value because of AI, the company was continually delivering on its investments, and even without our analysts modeling the company, the company continued to look attractive with our systematic valuations that we do for every stock. So just on pure as reported data converted into our economic margin framework and then an intrinsic value, the company was very attractive for years and years and years and years. And it wasn't until 2020 that these stocks exploded so much after that March decline that we painfully had to part ways.

You know, we prefer to never sell a company. A lot of our turnover is because these companies get acquired, which is kind of like a happy and sorrowful moment for us because we own them for a reason. We only own 50 stocks in this portfolio and we own across all the sectors. And we're jealous when someone buys a stock out below our estimate of its intrinsic value. It's nice to get the immediate buzz for performance, but it really is kind of annoying that someone's getting such a good deal on our back. On that note, I'm trying to think how to phrase this question. But I guess if you identify a stock that you think is undervalued in some way.

Does that signal something about the company, something sort of fundamental to the company that it will perform well in the future? Or is your measure of intrinsic value maybe correlated with something else going on in the market, like a much larger factor? I guess I'm trying to get to the flows argument that we briefly talked about in the intro. Does any of that make sense?

It does. And let me take your correlated comment. And if I may, let me go off on a tangent just for a moment. And then we can circle back if the conversation returns there. And I'm sure it will. But I think the question you're getting at is fundamentally is intrinsic value a causal variable or is it a correlated variable with a much larger set of phenomenon, right? Yes. Thank you. You put it much better than I. No, not at all. Not at all. The only reason I framed it so quickly is because we think about that all the time, and I think it's particularly relevant with respect to the cheapness literature. And if you were to think about taking both to price portfolios and intrinsic value portfolios, right? And if you were to form groupings of them, say the top 30 most undervalued companies, the bottom 30 percent most overvalued companies, and do the same thing with a book to price portfolio. And then you have the middle, the middle 40, which is some mix of fairly value, somewhat overvalued, somewhat undervalue. We can kind of test to see what is driving what by decomposing and deconstructing these returns. And if you take that approach and

you look at book to price, we only are looking at data that's live for us. We're not looking at at any simulated data or data that we were creating to kind of calibrate our estimates of risk or estimates of economic profit horizon, only looking at data that we've produced live consistently on a monthly basis. So going back to 1998, 1998 to 2020, if you look at book to price based stocks and intrinsic value based stocks, I would argue that book to price is not on a one, 3, 5, 10-year losing street. Book to price since 98 has added zero to the investing world. And I say that because if you take a look at those attractive book-to-price stocks and you separate out the ones that are supported by intrinsic value, in other words, those are also attractive

intrinsic value stocks. The resulting set of book-to-price stocks generate negative alpha. And that's not, and again, that's not a one three five-year window. That's a 22-year window. Book to price, if it's not supported by intrinsic value, generates no alpha. So then the flip question becomes, okay, well, intrinsic value then, and a lot of people will say, well, no one can trade short against us. No one's going to short the high multiple stocks against a value strategy. And that's not really true.

If you look at high multiple book-to-value stocks that are undervalued, they generate significant positive alpha. And so to me, the birth of Book to Price is the result of confusing correlation causality. Book to Price has done extraordinarily well when that section of stocks correlated with intrinsic value do really well and outperform the market and carry everyone else along. But by itself, unsupported, if you strip out the support of intrinsic value, the remaining subset of attractive book-to-price stocks generate negative alpha, and the absolute flip happens on the unattractive book-to-price stocks. The only ones with negative alpha are those that are really overvalued. The fairly or undervalued high multiple stocks generate positive alpha.

So this notion of correlation and causality is really near and dear to our heart because we see an entire industry that was birthed by confusing correlation and causality. And that's, you know, one of the messages that we're getting out, and we've just started, we've just started this message in earnest recently because we just felt we didn't have enough data to construct serious arguments. We've been accumulating this data out of sample for 22 years, and then a year and a half ago, we started to accelerate our efforts to begin organizing it, to do research the way, kind of in the Fama French tradition, just so that we have more of an apples to apples. comparison so people don't have to unscramble how we're organizing things. And then with the COVID

crisis, it gave us a lot of time to focus on that. And we completed this work back in September and released the paper in October. But I think the correlation causality argument is really interesting. I think also the quantitative investing world is starting to come to grips with some notion of valuation. If you look at how that work was extended by Falma French in 2015, they motivated their research up front with a dividend discount model. And from that, they derived a profitability factor, saying all else equal if firms increase their profits, they increase their value.

And then they have a second factor that they added, the investment factor, which I think really missed the mark. But I think it's interesting, virtually everybody in the quantitative value space, any firm that seems to be anybody, incorporates this investment factor into their work. And what that missed the picture on is it says, okay, firms that are investing in their business are expected to generate negative future returns. And what that missed from a valuation context is you can't separate investment without the return on that incremental investment. And so, of course, if you only make an investment in the firm and it generates no future returns,

absolutely, companies should never invest. But if you had the brightest quantitative value investing minds in the, world back in 1995 talking to Jeff Bezos. And he asked them, you know, I'm thinking about these different extensions of my business. If they're looking at the profitability factor, they'd say, oh, that's great. But if you're looking at the investment factor, they'd say, don't reinvest. Don't expand me on books because you're going to generate negative future returns. And to me, that's a worldview that's just missing the wealth creation element that comes from a complete valuation framework. Literally, you're biased against.

the greatest investments of the last 60 years, not tech companies necessarily. Of course, those are some, but you're missing the McDonald's, the Walmarts, the targets, the Fisers, Intel, as a manufacturing slash tech company, Apple, Google, Facebook, all of these firms have to make huge investments in their business. And if you have a worldview that says investing is bad, you automatically have at least one aspect of your investing worldview that's counter to the best returning companies in the world. And I think that's just a fundamental flaw in that approach. Why do you know, when you say that, when you describe it, it sounds so obvious that it's kind of weird to penalize these companies that are investing in building future technologies and future things we can't conceive of. What is their intuition in your view like?

Because it doesn't intuitively, it doesn't seem to make any sense at all, but I'm trying to understand like the counter argument. So the first argument is the data overwhelmingly says it's true, companies that invest underperform. That's an absolute fact. I wouldn't argue that with anybody, but I would say that's a very naive slice of the data that's not accounting for economic profitability and cost of capital.

So, yes, on aggregate, over these big periods of time, say 63 to 2015, if you looked at all firms that were reinvesting, on aggregate, the investment factor turned out to be negative. But I don't think that's a really, that's a complete view of the world because I think it naively misses the key component of valuation, which is that investment gets reinvested to generate future returns. So if you then segment companies based on their economic margin, positive economic margin firms that are growing versus negative economic margin firms that are growing, you end up with distinctly different return profiles. we captured this. We've been doing this notion of wealth creation and destruction through what we call a management quality score for 15 plus years out of sample also when we first created this metric back somewhere in the 2000s.

We converted this into a financing yield with the story being, look, there's a stewardship aspect to this. And when we look at what companies are doing and able to finance their business on their own versus having to turn to external capital. capital, that captures companies that are growing like an apple, but also returning money back to shareholders. And that changes the underlying characteristic of just saying growth is bad. Growth is bad, but it has to be tempered by what's the underlying wealth creation aspect of the company or wealth creation prospects of the company to really sign whether growth is a positive or negative. The overwhelming data shows the returns are negative. And in a factor world agreed you have a factor that's negative, but I think it's a factor motivated by really

poor theory, which is problematic in my mind. I wanted to ask you about that. So we've been focused on investment as one path towards growth. We haven't talked as much about the capital side, the cost of capital and capital funding decisions going into a company, whether to buy back shares or whether to borrow from the market and add-on leverage. How much has that impacted, or how much has trends in the capital markets impacted the performance of a traditional value investing in recent years? So that's a great question, and it's something we've started tackling more recently, and that's, I think this notion of value and growth getting at the heart of this question is really more of a duration argument and a duration problem. You have, from the

1960s to the 80s, sort of a period of increasing interest rates. Since the late 80s to today, we've basically seen decreasing interest rates. We think it's just present value math when we build out economic profit profiles for companies. The higher growth companies ultimately end up with much higher durations and a much greater sensitivity to what's happening to discount rates than what's traditionally viewed as value companies or low-duration companies that all their assets, all their cash flows are basically coming from what they already have in place. And there's not much on the horizon. So we've had a great environment for kind of traditionally defined growth stocks.

Since the 90s, obviously we've had periods of increasing and decreasing rates or many cycles within the larger trend of decreasing. rates. We're at zero now. It wouldn't surprise us to see these value low duration type stocks have their day in the sun again for an extended period of time if we end up with some thematic increase in discount rates and cost of capital over time because those further out cash wells become much less valuable to us. Doesn't mean that they'll be undervalued per se that. We have to see how the market, how fast and how the market digest that and prices that in. if indeed rising interest rates is even what happens. I have my ability, I would probably have to pay someone to listen to my thoughts on

future interest rate increases, so I won't speculate. But I can just say from an economic valuation perspective, those are the factors that will be at work driving, you know, longer versus shorter duration investments. A lot of short daily news podcasts focus on just one story. But right now, you probably need more. On Up First from NPR, we bring you. three of the world's top headlines every day in under 15 minutes because no one's story

can capture all that's happening in this big, crazy world of ours on any given morning. Listen now to the Up First podcast from NPR. What separates good leaders from transformational ones? I'm Jessica Chen and in season two of Leading By Example, we'll sit down with executives like Grace Chen of Bertie Gray to find out. It's important to understand where you spike, but also really acknowledge where you don't and find people who can fill those gaps. Listen to leading by example, executives making an impact on the IHeart radio app, Apple Podcast, or wherever you get your podcasts. So what happened going back, you know, since 1998? You sort of identify this moment in which your sort of intrinsic value framework seems to diverge from the cheapness.

models, is it just about the sort of rise of these more intangible-based business models, what we call tech or software, things like that? Or what explains this, this period since then? So I wouldn't say we recognize that our model diverges from cheapness. We have a very different worldview than what I'd say a typical quantitative value investor is. a quantitative value investor is generally going to have completely signed off on an efficient market hypothesis because by definition what they're saying is we're not here to create alpha. We're just trying to get you a really fair market return for all the risk factors you're

taking on any given portfolio. Our view has always been we systematically believe some companies are overvalued, others are undervalued, and probably the majority in the middle, there's not enough of the difference to really worry about. So it's kind of the plus 20 to 30% on each side that really are important to construct a portfolio. And with the rest of the stocks, it's okay, which leads to certain exploiting of specific behavior. If you look at passive investing, you know, our view is they're systematically overinvesting in the overvalued stock, systematically underinvesting in the undervalued stock. So we're super happy with the rise of passive investing because it's a natural second.

for us to trade against. Same with growth investors and value investors. We believe their views on the world systematically lend themselves to being exploited. The rise of intangibles and intangibles are a really interesting topic because now you have a lot of people that are trying to grapple with that and rescue book the price, if you will. I think there's a recent paper by Campbell Harvey and Rob Arnault saying let's capitalize R&D and let's take third. 30% of intangibles and of SG&A expense and call it intangibles and add it to book value. And indeed, when they do that, what they see is that book value performs a little bit better. Going back to that deconstruction of value that I mentioned on as reported book to price,

I think they called the variable IHML intangible based high minus low book to value, book to price. It does absolutely nothing relative to intrinsic value. all the alpha in that approach is also supported only when stocks are undervalued or overvalued in terms of the long short portfolios. And when you capitalize the intangibles, if you looked at the return profiles they published, that was for all the stocks, yes. But if you look at the large caps, the large caps really didn't do materially better than unadjusted book the price. And certainly on an intrinsic value-adjusted basis to perform poorly. And I think the reason is all of these approaches to intangibles are completely missing the picture on how to deal with them.

They're immediately treating intangibles as a valuation issue, and they're saying Google spending a billion on intangibles adds to its book value, but so does Macy's. And I would tell you, the properties of return for Google investing a billion dollars versus Macy's in advertising are wildly different. And to treat intangibles immediately as a valuation, concept, I think, is completely wrong. What you need to do is you first need to treat it as a corporate performance concept. You need to answer the question, how well is the firm performing? The reason you're struggling with intangibles is you disagree with the accounting convention of conservatism that writes it off immediately, and that's fine. Then treat it as an investment

and figure out what the real rates of return the company's generating on investments, just the way you would at the factory. Assign a life to it, put it on the books, and generate and calculate an ROI. Companies that consistently are generating significantly positive returns on that investment will start to see their ROI's increase as they continue spending, companies that don't won't. So Macy's can spend a lot on intangibles and R&D, whatever R&D would be for them, and it's not going to move the needle on their valuation much. So to just say they're cheaper because they've spent.

this money on advertising, I think it's just, it's a super naive view of understanding what valuation is all about. It's a convenient view because it lends itself to a lot of, you know, in the classic mean variance factor world, it's a convenient view, but I don't think it's anywhere near the appropriate view. And the other, the other aspect of a lot of these studies is going back to this notion of back tests, I don't believe you can, you can start to look at a variable and go back in time and look at it. And even if you don't have any malintent, it's really hard to not know over the last 10, 15 years,

technology firms, which are the ones spending the most on R&D, I've been the ones that have exploded in stock price. You don't have to have malintent, but everybody knows that unless you've lived in a shell. So it's hard to be an objective researcher and say, well, a solution is let's add back R&D and we get a better metric. Any of these finance studies that are looking backward in time and consistently drawing back-test results and claiming they're real,

I think you need to put in the hard work and effort. Every time you changed your model, your track record stops, and you need to start tracking what's the efficacy of the model going forward. You can't say, I launched this model 10 years ago. I've improved it now because of this additional variable that I've back-tested and added to the model. I don't think so. The model's reset when you added this new variable to it, buddy.

That's just the way it is. we waited 22 years to kind of make this explicitly public because we wanted to make sure there was enough data and the data is growing to really do the study properly. And I think that's a standard everybody needs to adhere to. You can't create a valuation model looking at historic data when you know how you're estimating these parameters to assign risk and it fits for that period and say you have a great model. It's great to observe and observations are an important part of science.

You have to observe, you formulate your theory, you formulate your model. Then you have to start calculating the data. And you have to let the data marinate live out of sample. It's painful and it's costly, but it's really the gold standard. And Harvey Campbell even wrote a paper describing how big of a problem, this potential for forward bias, you know, foresight bias and inadvertent data snooping is in financial research. And he said clearly, the gold standard is live. out of sample data, but it's just very difficult to obtain. And that's one of the things that I think

really sets us apart from any other firm is we've made the investment in time, because you can't shortchange the time. We've made the investment in time to get that data and to offer our results relative to other data that's been developed through backward-looking approaches. And we still came out on top. If you look at in this valuation beta research paper, we released in October, we systematically reconstructed an asset pricing framework looking at all the popular value, quantitative value factors, profitability, book to price, the investment factor, momentum, low volatility. They all basically are subsumed by these concepts of intrinsic value, wealth creation, or stewardship, and leverage. And what is quantitative value investing when you start

adding factors such as momentum and volatility there. What's the identity of that field? What's the theory that links price momentum and volatility to the intrinsic value of the stock? I think it started as a very clearly defined, having a very clearly defined identity, i.e., this book to price is a factor that either represents a behavioral problem, that people are missing this information, or it's some sort of embedded risk factor. But then over time, is that, factor hasn't worked and they've continually added on, I think quantitative value investing, the value component has lost a lot of its identity. What is it exactly? It's okay to be a quantitative investor, but I'm not sure, again, getting back to our initial discussion at the top

of the show, value doesn't belong in there. That needs to be reclaimed in a more appropriate manner, I think. So I'm going to try to squeeze in two hopefully interrelated questions here, But how dynamic are your own models then? And I know you just very much criticize people that are constantly changing their models in order to fit new data coming in. And secondly, what did you learn from 2020? How did your funds actually perform? And did you make any changes off the back of that? How dynamic are our models?

We pretty much locked in our models in 1998. Nothing has changed structurally to what we do the way we estimate the persistence of economic profit, the way we calculate cost of capital, risk adjust cost of capital for a company. There have been along the way what we consider to be minor accounting changes. For instance, recently accountants started capitalizing leases last year and putting them on the balance sheet. We have to undo those because we think they actually did it incorrectly for a number of reasons that are, way more geeky than probably the show needs to get into at the moment. But there's always little adjustments like that,

but the fundamental thrust of the model has not changed since 1998. It's a very dynamic model because market prices are always changing, and the performance and strategy of firms are always changing. So that's constantly at work. So the answer is we haven't really changed the model. I feel comfortable with my criticism because we haven't done that. There's been lots of times where our performance hasn't been what we wanted it to be. Going back to 2020, the first part of 2020, you know, the value guys weren't in this boat alone.

We performed poorly relative to the S&P. We were benchmarked in the value category. Our mutual fund in that space significantly outperform the value indexes last year. Since 2020, since I started 2020 to today, we've outperformed the S&P with this, with kind of our core strategy. the valuation 50 strategy. What we learned in 2020 is that it's really two interesting points from 2020. One, what we learned is the same lesson we've learned over and over. It's very difficult to be true to your process sometimes. It's extraordinarily painful when you, when you see the world is trading against you every day and you're underperforming. Yet you need to hold on because

if these are central truths you believe in, this is what your investment clients purchased from you, and this is what they expect they're getting, and that's what we delivered. So in 2020, I think we had two trades to the portfolio. One was selling Nvidia at 500 plus in August, after it had just run well above, finally exceeded our estimate of its intrinsic value, and we had to sell, as we were crying, pushing the button. It had become kind of an old friend of ours since 2011, and a great performer. The other interesting thing of 2020, in our history of 25 years as a firm,

we've issued four market calls, basically. One was in 2000, when we thought the market was overvalued along with everybody else. We don't think our insight was necessarily unique, although the way we went about it, I think was kind of fun with Cisco. We showed kind of what the expectations

built into Cisco's price were in 2000 off a process we created back in 98 called Value Expectations. where we take our model and we reverse out the performance implications of a stock price. And we showed Cisco basically had to grow at 50% plus sales for the next five years. In 20, the end of 2008, 2008, 2009, we came out with another market call that we said the market was just extraordinarily undervalued. That was fun because I happened to be on CNBC mentioning that at the time and they thought

I was nuts. And then this year, with all the volatility, we made two market calls, one in March saying the market's really undervalued. It reminded us of 2008. And then to us, finally, the market became overvalue in August of this year. Now, obviously, we've been way wrong. In August, we didn't say this is an absolute time to get out, but we did say statistically, we think the market's expensive. We'd be very cautious here. You know, who knows whether it will be right or not with that, but that's just what our models indicated to us. So that's the way we're approaching the world. I think it's important to stick to your discipline. And that's what 2020 taught us. Stick to your discipline. The market isn't always efficient, but it sure is generally rational

through time. And like I said, a lot of stocks that it underperformed continued to do really well after March. And if we looked at our returns last year, we underperformed the S&P year to date this year that fund has outperformed the S&P by Moore. So from the start of 2020, we're up. This process, we launched this strategy in 2004. It's significantly up on the S&P since inception over the last 10 years, as I mentioned over the last year plus. I think over three years, it's probably down. Over five years, it's probably up. I don't really track all the return profiles because we don't, you know, the turnover on this portfolio is about 10% a year, four or five stocks a year unless we have companies that are acquired.

So what do you do? I mean, you said you sold Nvidia. You said in August you identified the S&P or the market overall as being overpriced. What do you do from a portfolio perspective? What kind of shifts do you make or what are the implications for you when you make a overall market valuation call? In this particular portfolio, it's a long only fund. So we're, We aren't going to do anything, and the charter is to be fully invested. So we sold Nvidia, we replaced it. And again, this kind of speaks to why valuation is a little quirky. Let me just give you a little background prior to this, the direct answer to Nvidia.

The direct answer to Nvidia, we sold Nvidia, we bought KLA, the semi-conductor equipment maker. But the background of that that makes that particular purchase interesting is if you go back to 2014, the composition of this portfolio was approximately 25 stocks that would be classified as sort of value or core, 25 stocks that would be classified as sort of core or growth. Over time, as the market has kind of pushed up the valuations of growth stocks, the portfolio has naturally been shifting its marginal trades away from shading growth, getting more and more into value. by August of this year, the composition of the portfolio was approximately 75%, 70 to 75% of the

portfolio was in value core stocks, 25% in core value stocks. KLA Corp happens to be a growth stock. So it's hard to just pigeonhole what valuation says because it doesn't necessarily just have to put you in a, in a quote, non-growth value stock or a high-flying growth stock, it really is constantly shifting between what the market is giving us. And so when we pulled the trigger on Nvidia, we bought KLA Corp, and immediately KLOCorp dropped 10%, which is always the case when we take a trade,

which is really annoying. But since then, it's done a really nice job rallying that. Rafael, that was a great conversation. Any other sort of last thoughts or key things that you think we should, our listeners should think about? No, that was a lot of fun. Hope we can do it again sometime. That was a lot of fun.

Well, yeah, let's definitely do it again sometime and really appreciate you joining it. Thank you both. Thanks so much. So, Tracy, I thought that was super interesting. I mean, obviously we've talked about a lot of these themes before resuscitating value, reviving value, intrinsic, trying to come up with some new concept of intrinsic worth based on intangible assets. And I like that it feels like their work tries to just go about the problem differently,

rather than starting from this premise that there are ratios or that book value is a useful idea, sort of defined value, but not within the old constraints. Right. Although it does get me thinking if whether, you know, one of the enduring mysteries of our investment age is why, value investing hasn't performed better. It makes me wonder whether or not, like, it all just comes down to the definition and semantics. And, you know, I guess we kind of touched on this in the intro, but if you have a completely

different definition of value investing, then, hey, it actually works. I guess maybe the way, and I'm sure this won't be our last conversation on the topic, but I wonder if the better question is, why don't you? cheap stocks do better because I sort of like, I mean, ultimately, or why don't or why don't the sort of the traditional value factors do better? Because it seems like Raphael's criticism is not with the concept of value investing per se, obviously, because he is sort of in that category, but just in this idea that the sort of like the traditional like Fama French factors that one at one point seem to point to outsized returns no longer do.

Well, I was just going to say, then we can at least sort of define what we're talking about better because, you know, again, if anyone can sort of redefine value, then you can never really prove that it's working or not working. But if we could start our conversation with why don't these traditional ratios work the way they used to, why doesn't price to book, why doesn't price to earnings work the way used to, then at least we can define the debate. Yeah, I think that's a good point. I also liked how strongly he feels about back testing and sort of like fitting your thesis to the data. And also this idea of if you're making substantial changes to your model every month or every year

or whatever, you're basically starting from scratch. I don't think you hear that very often among the systematic investors. So that was fun. He also had some pretty like strong negative words towards, you know, what we called quant investing. And so you, you know, the idea that you could have one person who's a sort of quant investor who looks at things like price to book, another person who's a quant that looks like at momentum factors, they're not really, as he put it, you know, maybe some people argue they would. They're not really, they're not intuitively connected. It's not obvious why they should be under the same family of thought about investing or about stock picking, sort of ratio-based investing is obviously intuitive to the company.

Momentum factors are intuitive to the price of the stock itself or related to the price of the stock itself. It's pretty different stuff that ends up getting lumped into one broader category that we call quant. Yeah, I think that's a good point. Should we leave it there? Yeah, let's leave it there. Okay. This has been another episode of the Alldots podcast. I'm Tracy Allaway. You can follow me on Twitter at Tracy Allaway.

And I'm Jill Wisento. You can follow me on Twitter at the stalwart. Follow our guest on Twitter, Rafael Resendez. He's at R. Resendez. And check out their paper that he co-authored, Valuation Beta, addressing inadequacies, and book to price with intrinsic values, stewardship, and leverage. that's available for download online.

Follow our producer Laura Carlson. She's at Laura M. Carlson. Follow the Bloomberg head of podcast Francesca Levy at Francesca Today. And check out all of our podcasts under the handle at podcasts. Thanks for listening. Hi, I'm PJ Vote. My podcast search engine has a new two-part series for you. Of all the new technologies coming out of AI,

the most transformative one might be driverless cars. They're already on the road in 10 American cities, and they're quickly coming to more. We tell the story of how we got here. The secret team at Google that spent 15 years building what might be the safest vehicle on the road, and we cover the fights brewing in blue cities, where unions and politicians are working to keep those cars off the streets. Listen to search engine wherever you get your podcasts. What separates good leaders from transformational ones?

I'm Jessica Chen, and in season two of Leading By Example, we'll sit down with executives like Grace Chen of Bertie Gray to find out. It's important to understand where you spike, but also really acknowledge where you don't and find people who can fill those gaps. Listen to leading by example, executives making an impact on the IHeart radio app, Apple Podcast, or wherever you get your podcasts.