Introduction

Patrick: [00:00:49] My guest this week for the third time is Michael Mauboussin. If there's a major question about markets and investing, Michael has usually written one of the best pieces of research on that topic. Today's conversation is a mix of several of his research pieces, but focuses on the sources of alpha. The framing of the conversation is the brilliant question, who is on the other side of a given trade. If you're buying, who is selling, and why? Knowing the answer to this question is one of the keys to understanding where excess return comes from. As is usual with Michael, we explore tons of other interesting ideas that will serve as food for thought. Please enjoy.

Michael, maybe a fun place to start this time, because we were talking about it before we hit record, is for you not to go into each vertical, but give the rough syllabus for the class that you teach, because these chunks, I think, represent a great way for people to think about building out their own market education. And then we're going to spend a lot of time talking about the sources of alpha today and valuation measures. But as framing, I think that the outline of the course would actually be a great place to start.

Michael: [00:01:48] This is a course I've been ... This is my 27th year teaching this class and it really has evolved over the years. It basically is broken into four chunks. By the way, if you see most of the work I do, you'll see it fall into one of these buckets. And the first is, market's a market ... I call capital markets. Markets, market inefficiency. The big questions I try to pose there are, what does the market care about? And should we be focusing more on cash flows, earnings? How do we think about economic value? The second component really is market inefficiency. If you're an active manager, why do you think you can do better than a benchmark? The second big block is just on valuation, and this is translating prices into value and value into prices. There, I spend a lot of the time on talking about traditional metrics, price earnings being the most prevalent, EV-to-EBITDA, enterprise value to EBITDA. What's good about those things, what their limitations are. So as I would say to students, you have to earn the right to use a multiple. You have to understand what's behind it. We also spend a lot of time on return measures, like return on equity, return on assets, return on invested capital, and so forth. And there, we actually tuck in some stuff on capital allocation. So here again, as an investor evaluating a company, how do we think about how they allocate capital. The third building block is competitive strategy. What are the economics of this industry? What are the economics of the company that I'm studying? The key is, if I think this business is a particularly good business, what underlies that advantage? Is it some sort of consumer advantage? Some sort of production advantage? What are the factors behind that?

And then the fourth one is one that I did not have at all when I started this course many years, but is actually probably now, I think, perhaps, the most important component, is decision-making. And really organizing yourself to think about the world probabilistically, organizing yourself to think about how you're going to be effective at integrating new information, which I think we all struggle to do. And thinking a lot about things like base rates. So as a quant, this is something that's very natural to you and sort of in your DNA. But I think for a lot of discretionary investors, and a lot of people just walking around in life, they don't understand or think enough about base rates and how those base rates can be incorporated into their day-to-day decision-making to improve the quality of their thinking in general. So those are the four building blocks. Again, you'll see most of the stuff I do can fit into one of those areas pretty well. And there are a lot of subcomponents to those, but those are the big four.

Patrick: [00:04:02] You mentioned before that the last one, decision-making may've been the last into the mix, but may in fact be the most important thing that you now talk about. What do you think the lowest-hanging fruit is for these obviously incredibly smart competitive people that are in this business, that maybe aren't taking advantage of this part of your curriculum?

Michael: [00:04:20] It's interesting. I don't know. Roger Federer is the greatest tennis player of all time, and I think Roger Federer spends a lot of time practicing. So he's not just playing matches. He's practicing. Practicing means you're breaking down what you're doing to try to get more effective at doing it. And I think that's one thing in our industry that sometimes people don't do enough of, is thinking about what they're doing, and thinking about how they can do those things most effectively, and introducing specific tools that we now know can be more helpful in allowing you to be more effective. So part of it is, your whole life, people have gone to elite schools, and they worked really hard, and they're outputting a lot of stuff. And they don't really spend a lot of time thinking about how they're coming up with their results or their decisions. So that, to me, is a big part of it. Look, I've been very blessed to have a career, to have some time to allocate to this, to think about these kinds of things. And every time, I sort of stumble on these ideas like base rates, or pre mortems, or red team, blue team, and actually start to think about how to implement them, you just realize, "I don't know why everyone's not doing these things all the time." By the way, they're not super time-consuming. They're not super expensive.

Patrick: [00:05:20] They're habits, more than anything.

Michael: [00:05:21] They're habits. So that, to me, is the one thing I would highlight, is, you can have incredibly smart people, incredibly hard-charging organizations, but they may not be as efficient as they could be or should be, because they're not thinking about how they make decisions.

Excess Returns and Alpha

Patrick: [00:05:33] So another thing that we were riffing on a bit is, this idea there are accepted concepts, take EV-to-EBITDA, which you already mentioned, which everyone throws around. Most people use. A lot of people talk about. But very often, these concepts, we just accept them without ourselves doing the work to get to the root level of what the hell's going on. So I want to do that root-level work with you today, on those two concepts specifically. So we'll talk about valuations, specifically EV-to-EBITDA, a little bit later. But I want to start with this idea of where excess return, where edge or alpha might actually come from. Everyone throws around the same framework, which is behavioral, analytical, informational, and maybe structural, or trading friction type stuff. So everyone says those are the ways, but no one has really, until your paper, sat down and said, "Well, how true is this? Is this the right framework? Where do each of these things come from?" So if you could begin by laying out why you started down this path. And then we're going to into each of those vertical buckets one by one, to really lay it out for people.

Michael: [00:06:27] I was indulging that as well, sort of those things. I ended using the acronym BAIT, just because, sort of, I was clever. So Behavioral Analytical Informational and Technical. But like you said, I mean, I just use that more as a convenience than anything else. This is not an uncommon thing for me, which is, I will pontificate about something. I pontificated for years and years about capital allocation. I'd tell my students, "Capital allocation's the most important thing assessing managements." I really had very little to back up what I was actually saying. And then finally, I got off the stick and did a much more deeper dive on that. So this is one of those areas where I think many of the components that I talked in the piece are things that I had thought about, or they were not completely novel. But really try to ground these things in solid, what we know in academia as, strong economic work. And then really trying to create for people, A, a very important question to pose every single time. Who's on the other side? Why do I think I have edge? And then give people, specifically, a checklist at the end, to say, "Okay, here's why I think there's something going on where I'm in a better situation than others." That was sort of the background for ... The one thing I'll say right off the bat. The one thing I thought was really interesting, that I'm not sure ...I had read it a bunch of times and didn't really internalize. So really, one of the big things is ... This is the famous paper by Grossman and Stiglitz. So Sandy Grossman and Joe Stiglitz wrote this paper in 1980, called On the Impossibility of Informationally Efficient Markets. And that's important paper, obviously, given the time it was written in 1980. This is probably the peak enthusiasm for the efficient markets, was the late '70s. These guys, their argument was, "Hey, markets can't be perfectly efficient because there's a cost to gathering information and a cost to implementing it. And you should get some requisite benefits."

So Lasse Heje Pedersen's got this great phrase, "Markets are efficiently inefficient." But this makes a clear demarkation that there are sort of different markets we're doing with. One market's a market for information, and the second is the market for assets. So it's basically how you can implement that information. You can be in situations where the information's super clear and obvious, but it's actually prohibitively or impossible to implement that to make money. A famous example of that was, in the lat '90s, 3Com owned PalmPilot, and they spun out 5% of PalmPilot. That thing went bonkers. So if you bought a 3Com share, you owned the 3Com business, plus basically 95% of the PalmPilot. And it turns out, because PalmPilot was sky rocketed, the value of the 3Com business was negative $22 billion. So this is the most obvious trade of the face of the earth, which is, you short Palm, you go long 3Com, and you cannot lose money.

But of course, there was no borrow. So this is that idea that, we also had not only is there just information, but we have these frictions that we have to think about it. And it's really both those components that are super important. So that was, I think, just leading into the whole discussion, that's that I probably ...I mean, people know about arbitrage costs and frictions, and so forth. But I think sometimes in the academic literature, it's understated a little bit. And I think I probably wasn't thinking about those two things, or making the demarkations clearly as I should.

Patrick: [00:09:13] Yeah, in the paper, that's definitely the thing that ... I love that framework, as a precursor to each of these four verticals, to thinking about those two different markets as incredibly important, and it keeps happening. It happened with Yahoo and Alibaba. It happened with Naspers and Tencent. That same story just repeats as a great example. Let's begin with what I think is the very popular category of behavioral edge. So I think you had a line in there somewhere like, "Beware behavioral finance."

Michael: [00:09:38] Yeah.

Patrick: [00:09:39] So let's start there.

Michael: [00:09:40] That's a great place to start because there's a simple ... it's almost a syllogism, which I think it incorrect. It seems correct, but it's not, which is something like this. Individuals, you and I as individual, are suboptimal. We could use the term irrational, but whatever. We're suboptimal. We don't make perfect decisions. Markets are made up of people. So markets must be suboptimal or irrational in some way, and that C does not follow from A and B. So this goes into the idea of the wisdom of crowds. The key for crowds to be smart or markets to be smart is really a number of conditions, including diversity or heterogeneity of the underlying agents and their belief systems, some properly functioning aggregation mechanisms.

So you can have people with different bits of information sitting around the table, but unless you have some way to bring it together, you're not going to be effective. And then incentives, right? Which is, you're rewarded if you're right, and you're penalized if you're wrong. So you and I can be both, be overconfident, and I can be overconfident as a seller and a buyer, and we offset each other, right? So I think that's a really important idea, is, you can't conclude that markets are irrational just because individuals are irrational. Now, that said, of course, we do have episodes, obviously, of extreme pessimism, extreme optimism. So the more fundamental question is, why do markets go from wisdom of crowds to the madness of crowds? The other thing I'll say about behavioral, and this is important, especially because ... I go through this distinction in my class.

We talk a lot about the psychology. The issue is not really psychology. Psychology's like the unitive analysis in individual. What we're really talking about is more sociological, which is, how do groups behave in a group setting? And that's a very different thing. Now, here's the thing that's really important is, the reason this is so difficult to exploit is because, by definition, we're all humans, right? And we typically want to be part of the crowd. So the point of maximum optimism means the most people are ... it's like a magnet is the most powerful, drawing you to that conclusion.

And the point maximum pessimism is the magnet drawing you, right? So unless you're extraordinary in your temperament, at those extremes, you're going to be drawn to it. It's almost impossible not to be drawn to it. Almost by definition, it's telling you, that's the most people have been drawn to it. So these behavioral things are not just you and I competing against one another. It's us competing against this thing called the market, and the market's going to be much ... So I think that's a really hard thing to recognize, is that, where there's all these little individual biases, and those are important for things in life. In a market context, those are not quite as valid. So that's why I said be ... Not to say you need to understand those things, and think about them, and manage them in your own life, but just to recognize that's not going to be the answer to thinking about the behavior inefficiencies, I think. Simple as that.

Patrick: [00:12:03] So if you think about each of these things, and we'll start with behavioral, as creating a supply of error. So who's on the other side in each trade? Assuming that, obviously, sometimes people just need the cash. And that's a worthwhile person to buy from. But let's assume it's someone also trying to compete against you to earn excess return. It's a zero-sum game to some extent, to a large extent. So you have to be able to say, "Okay, there's a supply of investor error." How much of that supply comes from these different buckets, do you think? And in the case of behavior to begin, what would need to present for you, as a person making a trade, to feel some degree of confidence that you know that the error's being made? So what are the specific errors that you could take advantage behavioral?

Michael: [00:12:44] You're asking what percent of the error comes from each of the buckets. Yeah, that's a real interesting question. And I'd love to get your answer on that or what yeah think about that. I was thinking about this the other day. In some ways, I would just say the taxonomy is a little bit contrived, because at the end of the day, I guess you could say probably everything's behavioral, right? At some level. Everything could tumble into that box on some level. Yeah, my sense is, if you look at ... I don't know how to quantify this, but I would suspect that behavioral is the biggest one. So something more than 25%, if we're going to think about it that way. I think informational is probably the least, right? It's the most difficult one to do. There are certainly episodes where there are certainly, in retrospect, you could see. And then it would probably be technical. Technical doesn't probably ... day in, day out's probably not a big deal, or it's a little bit every day. But periodically, it's a gargantuan thing, so this is huge unwinds, and arbitrage. We could talk more about that in detail. Then analytical, I think, is another interesting one. Perhaps, the analytical thing really would be ... it's more subtle. But I think, maybe the most important component there is really a time-horizon thing.

I mean, the second question is, how do you actually know? I think that's a really tricky one. But the first thing, for example, in behavioral warnings, we talk a lot about ... and this is the most established thing in the literature, is this idea of over extrapolation. I think that's the backbone for things the value factor and so forth, and even momentum, right? You say to yourself, the thing's interesting about over extrapolation is that you see that basically everywhere. I was actually doing some work for a different purpose on ...

Well, it was actually like long-term contracts for baseball players, right? So it's uncommon for a player, if the last couple years before he goes for free agency, he has a couple good years, that it gets extrapolated, even though ...And you'd say, "Okay, baseball GMs, and they have analytics team, they've got the data, and they got big money on the line. Millions of dollars, right? They have a lot of incentive to get it right, and yet still see this as a pervasive thing."

So this idea of over extrapolation, we know is really hard for people to overcome. How do you offset that? How do you realize that you're on the right side of it, I think, is to say the over extrapolations led to a price, either high or low price, where the set of expectations embedded in that price are simply unlikely to unfold as that price implies. So it's a probabilistic statement if something goes way up. It's almost like a bubble-like situation. There is some probability that the company will deliver as anticipated, but chances are, probabilistically, you're better off being on the other side of that trade. Everything we're talking about is probabilistic, but I do think the behavioral one's probably the biggest source of these returns. But again, emotionally, it's the most difficult to actually exploit.

Patrick: [00:15:14] Another way of thinking about this is, how, either, even or lumpy the distribution of these edges are Thursday time. You've already mentioned this idea about points of maximum optimism and pessimism in the behavioral bucket. Those probably represent one of the greatest of opportunities to earn an excess return going the other direction. But everyone names the same examples of euphoria through time, and they're fairly lumpy and clustered. So they're not evenly distributed through time. So how do you think about that concept from an active manager's seat?

Michael: [00:15:42] Well, I think, first of all, that's a not-correct observation. There's another thing that's important in all this, which is principal-agent type of issues, which is exactly to your point, the greatest moments of opportunity is often when people who should be acting don't have ... either they act, but more importantly, you don't have the capital to act.

We spend time on that, mostly in the technical thing, which is, you can go back ... As you point out, there aren't that many huge cases of this, but you can go back through time and trace out where the epic arbitrage opportunities existed. And you say, "Where were the arbitrage?" This is, in math, the simplest thing ever, and they didn't show up. And the answer is, they didn't have access to capital because, by the way, their principals often were freaked out, hence they, as agents, couldn't be-

Patrick: [00:16:26] This is Julian Robertson closing shop in the late '90s.

Michael: [00:16:29] Precisely. So I think that's a thread that runs through all this, which is to say, it's easy to say and very difficult to do. But having the right kind of principle capital to stand ready to take advantage of these opportunities becomes ... Like you said, day in day out, markets are pretty tough to beat. But again, you get these opportunities from time to time.

There's a line, I hope I get it correct, from Seth Klarman at Baupost, where he said something like, "The definition of a great client is one who cashes a check when we write one, and a writes a check when we ask for one. If he's doing his job correctly, right? When the opportunity sets become really robust, he asks for more money from his principals, and they write him a check, and he can go out and exploit those opportunities. "And by contrast, when opportunity sets are thin, he'll say, 'Here's your money back. See if you can go off and do something else with it." That's a really interesting ... give how we've institutionalized a lot of this principal-agent stuff, it becomes a very difficult thing to realize in real life.

Investing Edge and Arbitrage

Patrick: [00:17:21] I love that because one of the things we talked about ... Actually, I talked about it first with our friend, Modest Proposal, from Twitter, the anonymous genius investor. And the observation was, the entire active management business is sort built like opportunity is evenly distributed through time. But it really isn't. And that maybe, oftentimes, the best place for an active manager to be is in SPY, and to wait, and act when there is an opportunity. But it's incredibly tricky to do because some of these principal-agent problems. The next one in is analytical. I think this one is fascinating, right? Because I think there's no denying that, when you meet as many investors as you and I do, you just know there's a difference in some of the investors, in terms of the depth to which they're able to process and interpret the same data that other people have. And I really, really love this section of your paper, talking about how this actually manifests in terms of who's on the other side of the trade. So walk us through the analytical edge and where the sources of that might lie.

Michael: [00:18:15] Yeah, there are two or three things that I try to highlight there. One is simply, can you compete against people who are just less sophisticated than you are. So I'm sure, Patrick, you're a better tennis player than I am. I'm confident of that. So if we play tennis on the weekend, you're just better at it. So we have the same racket and the same tennis shoes, and we play on the same court in the same clothing, and so forth, but you're just better. So now, the truth is, obviously, individuals are becoming less and less part of the market, and so that's one thing.

Another one I've always been fascinated with, and it's related, in some ways, you could say it's behavioral, is, we call it the concept of information waiting. And there's really obviously a pretty rich literature on this in the world of psychology. The idea is, you have ... I mean, it's called predictive validity and strength. But it's this idea of you saying, there's a strength of a signal and then there's how valid the signal is. I think we get confused by that analytically. The question would be something like, "Oh, I'm going to flip a coin. Is it tail biased or something," right?

So the first 10 flips, 7 times shows up tails. "Hmm, what do I do with that?" So that's a strong signal that it is tail biased, but the sample size is so small that the validity of it's very low. By contrast, and this may not seem totally intuitive, you flip the coin 10,000 times, and it shows up tails 5,100 times, that's actually a much stronger ... validity's much vastly stronger. But the streak doesn't seem as high. Just sort of like, "51, whatever," right? So that ability to be able to distinguish. And it goes the other way. So we tend tricked by small sample sizes. So analytical is having that really clear in your head. Probably, the biggest one for me, maybe the two biggest ones are, this idea of updating our views effectively. We call it being a good Bayesian.

But if you said to me, "What are the biggest ... Of the cognitive biases, what are the most challenging ones?" I was probably over CONF to be on there. But the one I would probably say is confirmation bias, which is to say, especially when you're a discretionary manager, and you're making a decision, you're going to essentially try ... it's hard not to fall for the confirmation bias, which is, you're going to seek information that confirms your point of view. You're going to dismiss information that doesn't.

And if something comes in that's ambiguous, it's going to break in favor of you, right? All the jump balls are going to come your direction. Question really becomes, how do you become really good at having an open mind and updating your views as new information tumbles in? I mean, that's a big one, I think, analytically.

And then last one, and you and I have talked about this a lot over the years, this idea of time arbitrage, which is, how we think about playing for a different time horizon than others? And that goes back to signal noise, another good way to think about that is, time arbitrage, to me, would be something like, when the market is reflecting noises. If it's skill, and pricing that way, and you ... For you to succeed in time arbitrage, A, you have to understand the signal better than the market. Second, the signal to reveal itself. And third, you have to be around, right? You have to have the money and capital-

Patrick: [00:20:54] With capital.

Michael: [00:20:54] ... with capital, at that. So those are the three conditions. But playing a long game, if you're good, it can be a really big advantage. So those are some of the things I thought about in terms of analytical. But again, they're tricky. They're tricky both principal-agent points of view. But they're also tricky in terms of thinking, right? Because we tend to get fooled by small samples. We tend to not want to update our views.

Patrick: [00:21:16] One of the things that I'm always interested in, is for any given type of advantage. So let's take time arbitrage as an example. Does that piece of the supply pool of investor error tend to get eaten by a very small group of people? So you mentioned Klarman, which I think is somebody that ...I heard him speak once, were someone asked the question, "Well, how can you expect to do as well with all this capital you have now? Shouldn't more capital inhibit your ability to do well?" And his answer was, "No, actually it's the exact opposite of that in this case, because what happens now is, with our minds set in this sort of time arbitrage, when one of those opportunities presents itself that's big, we're one of the only buyers that can do it. So we face less competition." Obviously, you could say Buffett or someone like that is in a similar bucket. So do you think that there's accumulating advantage to something like the ability to execute time arbitrage, that just clusters to a few people?

Michael: [00:22:03] For sure, but it's interesting. Klarman's, I think, very unusual in the sense that, he's been willing historically to run very high cash balances in his portfolio. The way, if I were to justify that ... Most firms don't do that, mostly because an allocator says, "We're going to give our money and we want you to invest in large-cap equities," or whatever it is," right? "So go do your thing."

That's the bucket you're in for our portfolio. I think the argument in favor of what Klarman's doing is saying something like, "We're going to have excess cash, but that is, for us, an option. And we as an organization are thoughtful about exercising options well." So this becomes, who's got the options?

Berkshire Hathaway is a sort of canonical example, right? Because it's an insurance business where money's flowing in every single day, so they always are going to have more money to deploy tomorrow than they did today. That gives them the ultimate optionality, and that's super hard to replicate.

But there are organizations trying to do that. There's call on capital. Why would this big sovereign wealth fund not say, "You could do a Ulysses contract, right? If these levels come, if the spreads in high-yield bottom market get to X spread, we're going to call you up and say, 'Write us a check for X amount.' And we're pre-committing to this thing," right? So that those types of mechanisms might ... By the way, if the spreads get that level, I can promise you they're not going to want to write the check, because they're going to be freaked out, right? So that's precisely the point of it. So yeah, I do think that, that is a really interesting concept.

But again, generally speaking, being much bigger tends to be deleterious to results like that's roughly true. But this is a case where it can give you the option value, or you become the last person standing. You think about even, you could write a big enough check to take over the long-term capital measurement portfolio. You would've done just fine, right? And he called, I think, Soros and Buffet. There weren't very many people that they could call to write that check.

Patrick: [00:23:50] Let's take your order that you put before, which would lead us to, I think, to technical next. And then we'll go to informational last.

Michael: [00:23:57] Wait, it's BAIT.

Patrick: [00:23:58] Oh, BAIT. But I'm going in your order of the weight of the pull that the stocks have. So the technical one is actually one that probably gets cited the least, and maybe is the most obvious when you read about it, which is things like the flow of funds, and forced selling, and things like that, limits to arbitrage, et cetera. So talk through the categories here, and why this might be a really interesting source of earning excess for [crosstalk].

Michael: [00:24:23] Yeah, I mean, the first big one is just, are there people ... I mean, the basic ... I try to define as something like, people are buying or selling for reasons that have nothing to do with fundamental, so they have to. Their hands are basically forced. I think the classic one on that, or one of the best developed ones is, the work by John Geanakoplos at Yale on the leverage cycle. The basic story's pretty simple, which is, optimist people like an asset, usually for good reason. The lenders tend to be quite generous, so haircuts are attractive. So they can borrow a lot to do that. Asset goes up. Everything is good. And then some bit of bad news comes out, and then that asset goes down. So what happens, all of a sudden, that was the feedback cycle on the way up, and now it's the feedback on the way back down, right? Which is to say, you get a margin call. You have to sell some of the asset. And then, by the way, at the same time, the lenders go, "Gee, this thing, we gave you these really good loan terms, that maybe, we got to rethink that." So now, you need to put up more collateral, and so that forces even more selling. So you get this cascade up and cascade down.

So when you're selling because of a margin call, you're not selling because you think it's overvalued or fairly valued. You're selling because you have to. So that's a great example. Again, as you pointed out, these things, I think, tend to be quite episodic, but those things are really big deals. Fund flows, and this one, you probably know much more about than I do. But fund flows, I've always found fascinating. I don't know. They tend not to be the center of a lot of discussion for some reason. There's sort of two angles to this that I find interesting.

One is, I mean, there's a fair bit of empirical work on that, and the people tend to give money to managers who've done well, and they tend to take it away from managers who have not done well. And of course, we know, and this has been fairly well documented, that if you fire a manager and hire a manager, it's often the case that fired manager does better than the hired manager in the subsequent 24 months, or something like that. So there's studies of studies of plan sponsors that suggest that. But there's another interesting component, which is disentangling flows from actually performance, right? There was one really interesting paper in the Financial Analyst Journal a few years ago, where these guys ... Up to a third of, "Alpha," in hedge funds is a consequence of flow.

So the basic narrative is, fund does well. Skill or luck, whatever. More money comes in, and what do those guys do with the money? They buy the stuff they own, and that makes it go up more. So it's almost becomes, again, a positive feedback. And then eventually that effect, of course, fades. And the same thing. When you get redemptions, you have to sell the stuff you own. So you get this compounding effect that's just super interesting. So just looking at how people are spending money. By the way, we played with this. I'd like to keep working on this. You might know much more about this, but I was thinking how, here's a simple model. Pick an asset class, or a sector in an industry, whatever it is, and just ask a simple question. I would look for two signals. One is, positive flows and more than one-cent adhesion verse historical valuation, and just short those.

And then negative flows and one-cent adhesion cheap, and go long. That would seem like an incredibly simple algorithm that may actually be pretty good. I don't know if you guys have played around with something like that, but that would be something I'd like to continue to explore. There's another I thought was interesting. It's related to the flows, which is the idea, demand shocks. There was this great paper by Gompers and Metrick a bunch of years ago, where they talk ... I thought this was mind-blowing. One of the first factor-oriented papers was in the early 1980s on small cap versus large cap.

Basically, the readers were like, "Hey, small cap is actually really good. It gives you better returns than you're supposed to get from cap anyway." So 1981, you're like, "Oh, I got it, man. All you do is buy small caps. I'm good to go for the ... " You would've gotten smoked, right? For 20 years. So Gompers and Metrick, these two academics, they're like, "What's the story here?" And the story is, basically, we were getting to the institutionalization of money management. As a consequence, people, individuals, were giving their money to the big mutual fund companies. So think of T. Rowe Price, and Fidelity, and Capital. By the way, what are their incentives? Their incentives were to buy large-cap stocks. Why? Because they were easier to ... Okay, so guys were like, "In pure academic theory, demand curves for stocks are basically horizontal." But we know in real life, they're actually sloped downward.

So these guys, they're argument was, it turns out that a reasonably decent chunk of the excess return of large cap was what they called demand-based return, which is just based on flows. We're not talking about months here. We're talking about a 15-or-20-year period. So I think that's another really interesting one, is asking about the demand base. By the way, I think, in the early 2000s, we actually saw the opposite of that, which was, if you look at sort of golden year of hedge funds when they were decent sized, was really at the dot-com bubble burst, right? So-called ... whatever. From 2000 into 2006.

Well, what was going on there is that you had extremely high valuations for large cap, extremely low valuations for small-and-mid cap. And basically the hedge funds were long small stuff, and underexposed the short ... the high stuff, and that was an incredible trade. But the very fact that they were buying those small-and-mid-cap stocks itself, created demand for those, that actually helped their own performance. And then the last thing we talked a little bit about, which is, historically, there have been instances of mathematically tremendously interesting opportunities for arbitrageurs. And because of principal-agent problems, they just can't show up.

Again, like long-term cap ... The very famous on-the-run off-the-run bond story from Long-Term Capital Management, it's textbook arbitrage 101. 30-year bond, 29-year bond, it's as simple and straightforward as you can get. Just the spread kept going the wrong, just because there was no one there to take the other side.

Active versus Passive

Patrick: [00:29:34] Have your views evolved at all since, maybe, our last conversations, about the impact, I'm thinking here about specifically flow of funds, that ETFs in particular, and I'm really talking much less about a total market index and flows into funds like that, because by definition, they're buying the whole thing. Vanguard Total Market. But there's obviously been a huge growth of whatever it is, subindexes, by following a whole bunch of different rules. And any sort of distortions or alpha opportunities that flow of funds and ETFs specifically create in the market.

Michael: [00:30:05] Well, I'd like to get ... You'd probably have a more informed view on this than I do. I think it's an incredibly interesting question. I spend a lot of time thinking about active to passive in general. And obviously, the traditional index funds that you said may not make many waves. But the ETF thing specialization seems like it should have an impact. My first line of thinking on that, my framework, might be something along the lines of what I described before, which is, this particular ETF ... I'm making this up, whatever ... Gold ETF or whatever it is, some sort of thing where it's very narrow, has become very popular with flows. And if we have some sort of way of calibrating valuation, just look for extremes, like lots of money in, in high valuations, lots of money out in low valuations, and so forth. So that would be one element. Maybe the other thing you put in there as a wrinkle is, ask about, are there differences in the liquidity parameters for each of those different things?

So huge large-cap companies, liquidity's not an issue. But you might start to get into, whatever, stock's $1,500 down, where they have ETF participation, where the liquidity may not be quite as good. That could be also another factor. That goes back to our first thing on information and implementation. So yeah, I've not, but that's an incredibly interesting ... It's an interesting question about ... I have a good friend who's backing this guy, who is trying to invest in companies in the United States, who are not involved in any indexes or ETFs. So literally, you run a screen on any company that's involved in index and ETF, and then you look at how many companies that are residual orphans.

Patrick: [00:31:29] The orphans.

Michael: [00:31:29] The orphans, right. Again, I don't think it's a big-

Patrick: [00:31:32] It's not a huge universe yet.

Michael: [00:31:33] You can't run a ton amount of money in that, but it's really interesting because you know there's nothing going on with those things, right? Anyway, that's-

Patrick: [00:31:40] One factor that I've heard of. We haven't done the work on it. But you take the aggregate weight of stocks in ETFs and come that to its overall market weight. And companies where it's overrepresented in ETFs, perhaps that's somewhere that you should apply factors or whatever. Really cool.

Michael: [00:31:56] That's super cool, right? Sort of same mindset, right? Over-representation. I mean, I guess you are making ... I mean, the premise there is that, that's excessive in some way, if it's way over weighted or if way under-

Patrick: [00:32:08] You could only look for like 10 years or 5 years, so the sample is small, right? But it seems like an interesting ... The orphan idea is always interesting.

Michael: [00:32:14] Yeah. Even though it's a sample size, we don't have enough data on that. It sort of fits ... That's an intellectually, I think, worthy path to explore, because there's some other stuff behind it, that would make it a sensible place to search.

Patrick: [00:32:25] The last one is informational. In some ways, this is the one I'm most interested in, because I think that it's very obvious that if you have better or more accurate information on a security than a market does, that's a source of edge. The interesting question is, how and where do you source that information edge? And I'd love to hear your thoughts on pre and post Reg FD, and whether or not Reg FD effectively neutered the ability to even gain this kind of edge. And to the extent that there's one that remains, what that looks like that looks like today.

Michael: [00:32:52] Yeah, obviously, this is, I think, the most difficult. I mean, you'll sort of see that I went off on some other sort of not ... They were related issues. So getting the information before everybody else. Look, there's got to an original source information somewhere, right? So this can't, by definition, go away. But every regulator, every law, is going to try to encourage the universal dissemination of material information, so forth. There was, on Reg FD ... By the way, there's a lot of academic research on this. I think it was the case that, for example, large mutual-fund families would have access to management teams, and would get information that was clearly advantageous to them.

And that was quite clear, because you see that the alpha's pre-Reg FD and post-Reg D for some of these large mutual-fund families degraded. I think that's pretty good evidence. But there another I thought was also super cool on this, which was, when Reg FD was put into place, credit analysts were exempted. By the way, that was reversed in Dodd–Frank in 2010. So there was this eight-year window where actually, equity analysts could not get access to the information [crosstalk] . You got a side by side, and actually flipped both ways, right? They had it and then didn't have it. It turns out that the information embedded in changes in that window were vastly more consequential than before and after. So super cool, right? There's some evidence that, that's going on. And then the other thing on information ... Okay, the other thing, I think these two tricky areas. But one is, I've been fascinated by this literature on ... I actually did a whole conference on this topic ... but this literature on limited attention, right? So the point is, I can give you tons of information, but you can only process a certain fraction of it.

So obviously, the very famous Chabris, the gorilla walking across the video with the people throwing the basketballs, right? You're like, "If I'm focusing on one thing, which is counting passes, I can't see something that's incredibly obviously and important in this, coming across the screen." So there's a really interesting literature on this sort of attention thing, which is, it turns out that investors tend to ... They're like little kids, right? They pay attention to what's glittering and shiny, and they ignore a lot of other stuff that's around them. So we're going to call attent information, but that's-

Patrick: [00:34:57] It's important, yeah.

Michael: [00:34:57] It's important. You're just discounting stuff. And the last thing, and this is probably where you guys are doing some work as well, and I think this still remains, probably, one of the more interesting angles in the literature, is called task complexity, which is, there are a lot of moving pieces. And there's information in a value chain, that sometimes the market tends to be slow in reflecting in the appropriate actors throughout the chain. So tax complexity is, the more complex the task is, the more difficult it is for people to figure out what's going on, and actually implement it.

So those are some of the things in informational. Look, I think that, for example, a lot of discretionary investors continue to ... I think there probably can be some ... there's some art to it ... but they continue as sort of a Phil Fisher Scuttlebutt thing, which is to say, "I'm not going to have any special information, but I'm going to glean it from a bunch of different sources. And I'm going to put it together in a way that gives me a better picture than other people."

Now, the other thing, and I should as you about this most effectively, which is, it does feel like there is an arms war in terms of this stuff. I make the distinction as paper, which I think is pretty obviously the difference between data, which is things that are presented to you, which is things that presented to you and information. And those are different things. So data by itself actually doesn't do much for you. Information, and technically information theory, information's what reduces uncertainty. Information is what's really valuable. So then it gets into these really interesting questions like, do I need the same data as everybody else?

How good am I at translating data into information, that allows me to act on it in a way that creates value. And that's probably more of the quant world, right? What you guys are thinking a lot about, data versus information, versus how I can implement that information in a way that allows me to generate attractive return. So in many ways, it is the most interesting of these things, right? Because this stuff has to get out into the world, and has to be reflected in prices. And the question is, are there ways to pick up advantage along the way.

Patrick: [00:36:39] So we've got this very detailed framework, so maybe there's something small missing, but it feels fairly complete. If you're going to earn alpha, it's one of these four reasons, right? And it's one of the subcomponents that drives the four reasons, that's the reason you're going to be able to do so. With that in mind, I'd love to hear your impression of ... with a very interesting career, being both on the sell side and on the buy side, for chunks of time with gaps in between. Working with Bill Miller. Now, working at Blue Mountain. And then working more on the sell side, research side in between those two stints. What has subjectively changed, in your view, the most, between your experience on the buy side? So in that period of time, what are the major most notable evolutions that you've noticed?

Michael: [00:37:33] I mean, look, I think that there're a few things. One is, diffusion of information continues to be incredibly rapid. Things get out there and they're cheap, right? So that's an ongoing forever phenomenon. But to me, probably the things that's most noteworthy is, the introduction and more serious integration of quantitative or systematic approaches, to this, how do we get to this hold grail of taking the very best of what machines do, and allowing them to do that?

And the very best of what humans do, and allow human ... I do think there's no question there's a role for humans in all this stuff, even if it's just how you're going to set up your algorithms, and the judgements you need to make in doing that, and really, how you integrate those. So I was giving that stuff not that much consideration, probably 15 or 20 years ago. And I think, today's front and center. It's not super clear to me that anybody's completely cracked the code, right? It's an ongoing thing. The other thing I find just observationally is, it feels like these are still two different tribes, the systematic tribe and the discretionary tribe.

And sometimes, they look at each other funny. So who are the people that really sit at the middle of these things? And that's an interesting question about ... and this is probably one way to think about it, which is, if you see a value chain between identifying mispriced securities, to delivering good returns in your portfolio, as you look at that value chain, asking the question along the way, "What would be better done by a machine?

"What would be better done by a human? And where do we try to figure those things out?" Let me give one example. To me, I think that, where there's actually a huge amount of opportunity is portfolio construction. Before we went online, we were talking a little bit about portfolio construction and systematic. But portfolio construction's a really interesting area, which is, at the end of the day, if you know what you're trying to do and you know what your constraints are, it's basically math. You have inputs that are these distribution inputs. And you put them in a way, such that you optimize for your objective, given your constraints.

Very few, I think, discretionary people do that in a way that ... It's usually finger-in-the-air kind of stuff. How could you be more rigorous? I mean, that's one area. I'll tell you the other thing I also found I've been super excited about over the years is that, if I endeavour to be a long-term-oriented investor ... I'll just make this up. I'm looking at two-or-three-year time horizons ... it really is difficult if you're discretionary, right? Because you put the stock in the portfolio a year long, and it goes down. Am I right or am I wrong? That's an interesting question, right? So part of that is to say, are there mechanisms to give intermediate feedback that's useful and timely, right? So that's where the work by Philip Tetlock and others on things like Brier scores-

The Brier Score

Patrick: [00:39:48] What's a Brier score?

Michael: [00:39:49] A Brier score is a measure of the quality of your probabilistic forecast. Glenn Brier, himself, by the way, is a meteorologist. So it was basically judging, you woke up this morning and said, "X percent chance of snow," whatever it was, how accurate are those forecasts, right? So the way a Brier score works is, I'm going through the math, but basically, zero means you've nailed it, right? Every day it's sunny, you say sunny. Every day it's rainy, you say rainy. And Brier score scale can go to one or two, but the scale of two, let's say, is, you're wrong about everything. Exactly the opposite.

By the way, if you have a Brier of two, you're useful. I just do the opposite of whatever you say, right? So it's a way of keeping track of problems for it. Now, there are a couple of things that are really important about this. One is, whenever Brier scores are kept, and that feedback is given back to decision-makers, whether it's meteorologists or people in the medical field, or whatever, they get better at this. They get better calibrated. So the feedback makes people better. So you say, "Okay, what does that have to do with investing?" Well, you think about, you have a thesis on a stock. Again, I'm a discretionary manager. I have a thesis on a stock. What I should be able to do, a priority, is lay out the thesis, path I expect this to go. Again, it's deviating from what the market believes. That's a really crucial thing. And then I should be able to assign probabilistic forecasts to certain signposts.

So I believe that their sales are going to be higher than the market believe. So there's X probability that sales will exceed this amount in this quarter. And I can give a probability to it. So now, I've set myself up for a Brier score, which is, it's a probabilistic forecast, it's within a specified time period, we can agree on the outcome, and it's important to our thesis. So it's a beautiful way of ... Now, all of a sudden, I'm give you all this intermediate feedback. And by the way, it opens up ... We talked about bayesian updating before ... It also opens up your mind to say, "I thought this was going to happen. It didn't. Let's talk about whether we are in the right ... same place, and the thesis is still unfolding correctly."

That's a technique that's super cool. By the way, it's the same thing you can use ... Businesses can use it. So you're operating a company, right? You're running your asset management firm. You might say, "Here's what we expect to happen. Here's the probability we expect to happen. Let's think about it." And then just get better at it. So it's this idea of intermediate feedback in a field that, A, the feedback's very messy and noisy. Again, if you're two-or-three-year horizons, this is a way to give you way points along the way to make sure you're doing things right.

Patrick: [00:41:58] What a fascinating loop that I bet almost no one does. I know of a couple firms that make their discretionary analysts ... Citadel's one that comes to mind ... input all their data. And then they can use that to train a model or whatever. But my guess is, nobody does that. And it's just a straightforward thing.

Michael: [00:42:17] You can write it out. It's not hard to do. You just got to be disciplined. The thing is that some of these techniques, they're not super expensive or fancy. It's not highfalutin math. It's really straightforward stuff, but it [crosstalk] .

Patrick: [00:42:31] It's individual, yeah, yeah.

Michael: [00:42:32] The other thing I'll just say about that is, it is interesting that I think, in a lot of other probabilistic fields, the really best people do document it. So I think you look at, really, the best GMs in sports, they think a lot about this, this way. And they try to document it. You think about poker players. When we were talking about Annie Duke is a great example. Annie's an amazing decision-making person in general, but this is the kind of stuff that would be very second nature to what she's doing, what they think about. And even people that are critical like-minded people around you can really be helpful.

Patrick: [00:43:01] There's a great story about Phil Ivey, the Michael Jordan of poker, if you will, and his obsession with, even after he wins a tournament, going back and documenting each decision, and basically doing the equivalent of a Brier score to the extent ... You can't really do that in poker I guess. But it's so interesting to document this stuff. We're going to talk about EV-to-EBITDA, but you mentioned GM, and sports, I know, is a passion of yours. It's just an interesting way to think about these ideas. So if you had to be a GM, and extra points if you name this specific franchise, but I'm more interested in the sports where you have two goals. The first is just passion. So I'm just curious, where you'd most enjoy being GM, in what sport. But the second, maybe more interesting, is, if your task was to go win a championship, which sport do you think this kind of stuff that we've talked about would be most applicable in?

Michael: [00:43:45] Man, that's really interesting. I grew up playing soccer ... Even though I love soccer, I don't spend that much time focusing on it ... ice hockey, and lacrosse. I played lacrosse in college. Actually, last night, I was with some friends and spent three hours talking about lacrosse statistics. So I would probably have to say, just in terms of my fundamental interests, it'd probably be ice hockey. I think I ice hockey's probably the furthest behind a lot of the other sports in trying to pin down some of these analytics. Again, baseballs gotten pretty sophisticated.

Recently, I was with Jeff Luhnow, who's a GM of the Houston Astros. They were talking about, "What is the frontier?" Astros done a phenomenal job and Jeff's done a phenomenal job. And the question is, "What is the frontier?" Jeff was very cagey about it, but he's like, "A lot of it is sort of biometric stuff."

It's like, "Can we really measure how these guys work physiologically? Can we measure what they're eating, how they're sleeping, and all those kinds of things, to have these natural performance maximizers?" That's an interesting whole next domain, or even look at the mechanics of how these different players will throw, or hit, or whatever it is.

So baseballs the ... Basketball, I think, has also come really far a long way. So you think Daryl Morey of the Houston Rockets, and what they're doing, and how they think about it. Sam Hinkie. You just hang around with these guys, and you realize they've got a really good sense of what's going on, and how to optimize results. Hockey, I think ... By the way, football's another one, just seems to me that there's some ... Bill Belichick ... some of these guys are just operating at a completely different level. I think the Super Bowel is an example of ... Sean McVay's a great young guy. I'm sure he's got a great career ahead of him. But I think that wasn't even a fair match in terms of coaching. So football, but the thing is, a lot of football is a really good head coach, and usually, you need a good quarterback. A quarterback's such a key thing in the whole thing. I think there's a ton of opportunity though in football. For sure, there's a ton of opportunity, because I think the football guys don't know a lot of stuff, in terms of actual play by play. I also think that there's still ... It's gotten a lot better ... but even game-time decisions and the classic ones. Punting on fourth down, but even two points versus one points, clock management and so forth.

But I think hockey's a little bit like football too which is, I think there's a lot of just traditional people that do. And a lot of the coaches are people who played in the game over time. So it tends to be more difficult to crack. Baseball, for whatever reason, sort of the let the academic type of guys in, right? And they've been between Theo Epstein, and Jeff Luhnow, and the Moneyball guys. These are pretty cerebral guys and they've enjoyed some success. So to me, hockey's a thing where it's still, I think, probably very early days, analytically, it's a super fun sport. But it's also, there's some really big challenges. One is that it's a very difficult thing to chart, hard sport, but positions are not discreet.

Patrick: [00:46:30] The movement's so crazy, yeah.

Michael: [00:46:31] It's hard to get ... And then the other thing is, even though there are roughly six players versus five players in basketball, and the length of the season's the same, in hockey, your Star Forwards are on the ice, whatever, low 20s, 23 minutes, 24 minutes, 22 minutes. Whereas, LeBron James on the court essentially the whole time, right? Or he might take a minute or two off at the end of quarter or something. Just, the dynamics are different. So it's the sport adjusted-for-sample-sizes sport ... Well, maybe baseball, but it's the one that's closest to randomness, which is interesting.

But I think that's a really ... And that's, again, player acquisition, how do you value players, goal tending, how do you think about scoring goals, shot breakdowns. There's this really cool hockey analytics community out there, and they're doing some really neat stuff. The other is thing is, I'll just say, I play beer-league hockey, so I still play. It's funny, because that stuff's in my head, actually, as I play. I'll give you one example, is, I was with a guy who was money man who's really into hockey. And the guy's like, "You should never shoot ...

"In lacrosse, you can shoot wide because you can back up the shot. Doesn't make any difference. Basketball, you miss a shot, someone can try to rebound it. And hockey is, basically, you never want to shoot wide ever, because if you shoot on net, there are only three things that can happen, right?

"The goalie can save it, which is fine. If they save it, it's going to be a rebound. That's usually good. Goalies save and cover it up, which is a face off in the offensive zone, or it goes in. There are only three things that can happen. They're all good, basically, or they're not bad. Shooting wide can be bad." Anyway, it's probably hockey. I love talking to all the analytics guys. It's fascinating to me, only because, again, you think about the NFL. Huge dollars moving around, right? These franchises are worth billions of dollars, right? So we're not talking about-

Patrick: [00:48:02] Care about the equity value you can create by being at the frontier.

Michael: [00:48:05] It's not chump change, right? And yet, there's still a lot of ... We talk about our inefficiencies. So I will mention that I sent the report to a couple sports guys. One or two of them wrote back, and they're like, "Yeah, dude, I know this is investing, but this is exactly our world. This is exactly what we deal with. Exact same stuff." So I was like, "That's pretty cool," right? But in a way, what we're talking about are just probabilistic fields. So pretty much, if you're making decisions in the realm of probability, pretty much all these things are going to come into play one way or another.

Buybacks and EV-to-EBITDA

Patrick: [00:48:35] So we'll close with two really interesting back-to-investing topics. The first of which is buyback. So just because I've got you here, this has become, strangely, like some sort of Rorschach test, like what you think about buybacks tells you a whole lot about who you are, which is bizarre to me. I'd love to just take two, three minutes to set the record straight here, and just get your opinion on what the source of this is. What is so hard to understand about what, to me, seems like an incredibly simple mechanism?

Michael: [00:48:59] Yeah, I don't know. I'm with you. I'm mystified by this whole thing. I do think there is underlying thing that is probably a real, at least, concern, which is, the companies, generally speaking, in the United States, have pretty good returns on capital. They're generating a lot of cash. They're not growing that fast. I mean, we had good growth last year, but part of it was just tax induced, and so there's a lot of money around. So the question becomes, what is the best way to use that capital? And I can see the point to say something like, "Gee, you got money. Why don't you pay your employees more? Gee, you got money, why don't you build a new factory?" Right? Understand that sentiment to some degree.

Now, on the waste thing ... By the way, the thing I think is hard for people to ... Look, it's about competition, right? So you and I both have a restaurant. We're basically the same in every way, and I decide I'm going to pay my employees a lot more than your employees, assuming that there's no change in behavior of my employees. Now, that's a big assumption, but assuming that my employees just behave the same as your employees do, I'm going to lose to you. I'm going to go out of business, right? In other words, you're going to lower prices to the point where I become non-buyable, and I'm gone.

There's a notion of competition that somehow gets lost in this discussion, that's really important. Now, you might say, "Hey, raise your salaries by 30%. That's going to lower your turnover and your training costs, and so forth. In fact, Costco. There're examples of where that is a very valid, legitimate strategy. All right, but going back to buyback. By the way, the other thing is that, buybacks were weren't legal until 1982 for a bunch of different reasons. So to me, I don't understand this at all. By the way, the repatriation, this whole, "If we had a tax rule," gave people a green light to ... They had to bring the capital back a some point. And returning it to shareholders, in some ways, seems to be not an unreasonable thing.

Now, one of the things I've talked about, and I do think this is at least worth thinking about when you're looking at a company buying back stock. And you guys, I'm sure, empirically buying back ... Companies that buyback stock, that tends to be a good thing, right? Just, they do well. But there's, I call it, the three motivations. One is, the market-efficiency motivation, like, "We don't know if our stock is cheap or expensive, but basically, we've got this capital. We're going to return it back to shareholders. We'll buy it back consistently. Sometimes, we maybe overpay, underpay, but it washes out." The second camp is the intrinsic-value camp. We will be very strict. We'll only buyback our stock when we deem it to be undervalued." Now, most management teams think their stocks always undervalued, but that's fine. There's some guys that are better at that than others.

William Thorndike wrote this great book, The Outsiders, and you look at a lot of those folks in there who are really smart buybackers. I happen to be the analyst that covered Ralston Purina and for many years. Bill Stiritz was a genius with this. He was really good about buying it cheap, and laying off when it was dear. And then the third camp is what I call impure motives. I think that's the other thing where this comes up, right? Which is, the impure motive says, "Buybacks can be accretive to earnings, or accretive to ROE, or allows us to make our bonuses, or offsets solutions from options." And these are things that may not be purely economically motivated.

Now, they may be benign, but we don't know. I can say for sure, there have been cases where a company has bought back stock they knew was overvalued, in order to achieve some of these sort of bad incentives. That could be the only case where I would say that may be suboptimal. Now, the flip side of all that is, if you're a shareholder and you own the stock of a company buying back stock, doing nothing is doing something, right? Which is increasing your steak in the company, right? So people just have to be mindful that you're making a decision by doing nothing, which is, in a sense, an active decision. And the other thing I'll say is, my premise is that if you own a stock, you're long it is because you think it's undervalued. And if that's true, you should always want a company to buyback stock, you probably should never want them to pay a dividend, right? Because you have to take the dividend-

Patrick: [00:52:30] Get taxed.

Michael: [00:52:31] ... get taxed, and then redeploy. Versus, buying back stock. It's an automatic built-in accretion in your intrinsic value. So I don't really ... I guess I understand some of the underlying sentiments, but this stuff seems bonkers in some of the discussions.

Patrick: [00:52:41] I'm with you on the frustration. It's stunning, the lack of evidence that seems to be ... The people that are vitriolic against buybacks, who pretend to be pro science and pro facts, that completely ignore the evidence, that they're just completely wrong.

Michael: [00:52:54] It makes no sense.

Patrick: [00:52:55] So the last one would be your paper on EV-to-EBITDA, yet another metric that everyone talks about. It's sort of the king of the value factors in many ways, popular in the P/E industry that, again, I had never read this deep of a dive into what this means. And most specifically, this idea of a warranted ratio. So talk through your exploration here.

Michael: [00:53:15] Well, it's interesting, Patrick, and same thing, I'm with you. We've now, even in my investment career, which is a little bit more than 30 years, this is very sparingly used in the 1980s, and has become, like you said. I think we decided in the paper, I think P/E is still the number-one thing people use, but EV-to-EBITDA is now right behind it, which is really interesting. We also showed, it's just Google Ngram thing, the citation of determining EBITDA. By the way, I don't really know where it came from. I think William Thorndike suggest it came from John Malone at Tele-Communications Inc. I went back and read about it, and I think that's probably as good a place as you can ... So we'll call 1973, 1974, when that got going.

Yeah, once again, people talk about this a ton, and no one really has grabbed it. I mean, I used it to invest means all the time, like, "This is an 8-times-EBITDA business. This is a 10-times-EBITDA business. 6 times. Where do these numbers come from?" Comps, or people, or whatever, historical. So I'm like, "Okay, can we decompose this?" And the first question I ask people is, "Okay, you're looking at EBITDA. How much of it is EBIT, and how much is a DA? And do you care?" People are like, "Yeah, I care," right? So it's like, "Consumer staples, 80% EBIT, 20% DA. Energy, 40% EBIT, 60% DA. Do you care about that? Which one's going to be more interesting to you?" Right? So it turns out that's obviously an indicator capital. Intensity right away. That then becomes an indicator, usually of balance sheets, right? So financial leverage. So we're like, "Okay." We wrote this piece that was very popular, called What Does a P/E Multiple Mean?

It was going back to Miller-Modigliani, some basic thing to say, "Okay, we know that, given certain assumptions for growth rates and return on incremental capital, and we can define that in more specificity, here's the optimal P/E you should pay. Lock down cost to capital and some other assumptions." And that's a very intuitive thing. By the way, that leads to a couple lessons that are really important, right? Is when a company's value-neutral, it's earning something close to its cost capital, it's very insensitive to growth. And that's like you're on an economic treadmill. Turn up, turn down, you don't go anywhere, right?

If you have high returns on capital, the business is incredibly sensitive to growth. That's when people sometimes get ... they're surprised by this. So if you take a very rapidly growing high return on capital, or promising high return on capital business, and it misses numbers, even for a quarter, what happens is, the trajectory of growth goes down. So people, "What do you mean, we're only reducing our estimates by 5%?" You're like, "No, you're actually reducing the trajectory and it crushes the stock." And you just do the math. 15%, 20% declines is actually justified based on that.

So we're like, "Can we map that over to EBIT?" "That's a little bit more complicated, right? Because you have to assume capital structures." But we basically said, "We're going to do different categories based on this ratio of EBIT-to-EBITDA." And then we locked down capital. There was obviously some dynamism there. And then you get, again, these baseline value-neutral businesses. "Here's," we call it, "the commodity multiple you should pay. High returns, you pay more. Low returns, you pay less." So it's, again, just trying to ground all this stuff. The other interesting question, by the way ... I'm embarrassed how much I learned about this ... which is of the DA, right? Depreciation and Amortization, how much is A, right?

So that's another, "I hadn't thought about that." And it turns out that the pattern of amortization has swung wildly in the last 30 or 40 years, mostly because of accounting changes. So if you're not up on your accounting and different methods of acquisition accounting, and what's happened to amortization of intangibles, you're sort of missing the whole thing on this. So that's another really interesting aspect of all this stuff. I loved writing that, because I also went back and did a history of valuation. How do people think about valuation, going back 100 years. I actually bought some books from the 1920s, and there's incredible tables of how these guys used to do these calculations. And just to say, again, "Let's ground this." So as I said before, you have to earn the right to use a multiple, in other words ... This is where to say to my students, "You guys can use multiples to your hearts delight, but you can't use a multiple and not tell me you understand the underlying economic assumptions that justify it. If you can't tell me those things, then you're not in business."

Patrick: [00:57:01] So if someone wanted, let's say, a hypothetical quant for wanting to improve their EBIT-to-EBITDA ratio. The rule would be, you have to do it systematically. So obviously, I think the best version of anything would a higher human touch laid on top of a machine that was helping them. But let's say you had to do it systematically. Would the recommendation for transform be, basically, just normalize the comparison against similar EBIT-to-EBITDA capital intensity? Would it be return on invested capital? Would it both?

Michael: [00:57:26] It would be both, but those are related, so it would probably be return on invested capital, yeah. So it would be return on incremental ... We were talking about this before. It's hard to measure incremental returns, but it would be some sort of expectation. Then you could use the history, maybe, as a way to project into the future. Yeah, I mean, it's the two dimensions in terms of multiples are incremental returns and growth, and that's it. I mean, basically, if you have those things down. Now, we're going to make some ...

The model gets a little bit tricky because the fact, if you assume really high returns and assume lots of growth, then the capital structure changes, and so on. So the ration to EBIT-to-EBITDA changes and so forth, so it gets a little tricky in that regard. At the end of the day, these are all ... That's what I kept saying. It says in this piece and all the other pieces, "Multiples are not valuation. Multiples are shorthand for the valuation process." So what I'm just trying to do is say to people. "Timeout. Let's make sure that we're connecting the first principles to what we're doing every single day, and make sure that we keep that bridge well-structured, and we're going back and forth in an intelligent fashion."

Patrick: [00:58:22] It's a great paper to read, one, for the ratio itself. But two, if you're allocating to a manager, it's a great question to ask. "So tell me matters to you and tell me how you decompose it." And I think you'd get disappointing answers a lot of times.

Michael: [00:58:32] I think you'd get very disappointing answer, because I think people ... By the way, the other thing I should say really quickly on this is, that this is the other thing that's really interesting about all the stuff on multiples is, it probably doesn't matter that much. If it's an efficient market, it actually doesn't matter that much. So you could be a total dope with your valuation work and your multiples, and you just buy and sell in the market, and the markets are efficient, it's sort of carrying you along, right? And you'll ... good luck, back luck, or whatever, right?

So it's a really interesting ... This is for people who want to distinguish themselves and try to find these inefficiencies, but the market does what the market does. I will say thing, by the way, that this is a big deal for private equity stuff. That's an area that's really interesting to watch what's happened to the multiples. I think the other thing that's been really out of control is this notion of adjusted EBITDA. So these companies layering all these assumptions about the future, that are built into ... and asking you to pay for those things today. We're getting into the shenanigans zone a little bit on some of this stuff.

Patrick: [00:59:27] Last topic I've got for you, which, it's actually a perfect transition, because adjusted EBITDA is a classic example, like John Malone, himself, of telling a story, weaving a story, and having that convince people to do things differently than they would otherwise. Maybe you could argue that adjusted EBITDA is sort of a nefarious use of this technique. But we started, before we recorded, taking about a notion that I'd never heard of, called benign myths. I would love to just hear you riff on this concept because of how powerful a concept it is for everyone doing just about anything.

Michael: [00:59:55] Yeah, and I first learned about this from Jim March, who was a very famous organizational psychologist at Stanford. By the way, his stuff is great. It's really worth reading all his stuff. So I'm at the Santa Fe Institute, and I'm at this session. I never heard of this guy. He was an older gentleman at the time. I'm hanging on every word. This guy was so fascinating. Everything he was talking about. So I go look him up afterwards, and it was like, "Oh, right, he's this really famous guy." What was he was talking about that particular was this notion of benign myths.

These are myths in the sense that they're not really based on anything truths. But they're benign in the sense that they're not harmful. And the key to these benign myths is they motivate people. I think a lot of stories or a lot of narratives could be probably in this category, these benign myths, that there's really no empirical, necessarily, foundation for these things, or facts behind them. But in a sense, they get people motivated to do the right kinds of things.

This could be controversial, but you could say components of religion might be benign myths. But most organizations, when you talk about things like culture, often they rest on some sort of benign myths. I'll give you one example I think is also quite concrete, which is, I've been critical, along with a lot of other people, of some of the research methods of Jim Collins. He's a very famous guy on company research.

I just want to be clear about this that, whereas I'm not sure that the actual analysis is buttoned down or as rigorous as it could be, I think the guy's a great guy. I think that managers feel better after being with him. I think they're motivated by what he says. I think he, himself, is very thoughtful. So in a sense, is the interaction with Jim Collins going to make you worse off? I think, in most cases, it's going to make you much better off. You're going to feel better and you're going to be motivated. So might say, that's an example. It might under the category of benign myths. The research may not be exactly the highest-quality science, but it's got a lot of other attributes that make people feel good and get them motivated to do the right kinds of things. So this is just, again, one of these things. You become circumspect about everything in life as you get older. Realize that there are a lot of these narratives and stories that get you to the right thing, and that's important.

Patrick: [01:01:54] It's a wonderful closing idea, especially for me as a quant. You want the Jim Collins methodology to only check out empirically before giving a credence, and it's helpful to remind ourselves that, that does not need to be true. So very last question, which is, what's the frontier for you? What are you most interested in now, having put out some really big pieces recently? What's on your mind next?

Michael: [01:02:13] A couple things. One is, we're working on maybe three things. One is, we're working on a piece on active to passive in fixed income or bonds. Obviously, the equity markets are much larger than the credit markets, so that's our first to start. And you've seen, it's really been violent in the equity markets. It's a much more nuanced story, as you probably know in bonds or fixed income. So we're working on a piece on that. And there's simple things, like the Bloomberg Barclays credit index is something like 10,000 securities, whereas the S&P 500 call 505 securities.

So 10,000 is complete zoo with different maturities and so forth, and liquidity and so forth. So tracking that. So the question is, is you're an investor, and you're trying to say, "I have active, versus passive alternatives." And equities is pretty straightforward. You go buy a SPDR and you're good to go. Basically, it's very low cost and you're going to track very accurately in credit. So that's one area that we're working on, which is interesting. By the way, the other thing, going back to the stuff on BAIT, credit's another area where they may be a number of actors in the credit markets, especially central banks and so forth, that may not be completely ... They're not looking at fundamental value. They're doing other things, right? So that's one area.

Another area, we touched on it briefly, which is, I find this incredibly fascinating, apparent migration from public to private. So we well documented that the number of public companies is down a lot in the last 20 years. At the same time, we had this rise of this ecosystem in private markets, whether that's private equity, but also this late-stage venture. We see these large companies getting literally hundreds of millions of dollars in late-stage financing. So maybe that'll turn. We're seeing a bunch of big high-profile IPOs. Certainly, Lyft and Uber, and some others. And we'll see if that shifts the tide.

But it does feel like there's this huge ecosystem that's operating outside of public markets, that is really worth paying attention to. The thing ... This goes back to sort of an inequality thing ... is, regular Joe has a hard time accessing that directly if you're not sort of a wealthy or plugged-in person. You could obviously go buy the SPDR and that's fine at S&P. So that's an interesting dynamic, so that's another thing we're thinking a lot about. And the third is, I read this book, The Myth of Capitalism. Do you know this book by Tepper? But there's a whole thread of stuff out there I think is really interesting.

If I recap, the premise of that book is something like, the Justice Department has been very lax. We've had a lot of consolidation. As a consequence, most industries are now very consolidated. Measured the Herfindahl Index. The measure of industry concentration has been sort of skyrocketing. It's almost the mirror image of the number of public companies. We've seen that increase. As a consequence, when they companies get rich, they can lobby, and they can change the regulations in their favor, and so on, so forth. So this whole interesting idea about, what is the role of competition? And then there's this weird thread of research ... We didn't talk about this ... about cross holdings.

So there was one papers. I just thought it sort of blew me away. The argument is something like, if you own the company and I'm the sole proprietor, you're going to try to maximize profits for me. But I own multiple companies, including your competitors, you're going to try to think more like a pool, right? Versus, just us. So these guys, they did it with the S&P 500, and they found that if 0 ... They put it on a scale, so if 0 is perfect competition, we go after each other every single day, and 1 is complete collusion, they said we've gone from a .2 in 1980 to a .7 now. And you could talk about the methodology, and so on, and so forth. But basically, the arrow ... I mean, it's extraordinary. So what's going on with cross holding and so forth. So that's in a whole another area like, "Let's think about the notion ... "

And then what does that mean for competitive advantage of businesses? What does that mean for pricing? What does that mean for return on capital patterns? Does this get reversed? I mean, these are really interesting. So they get into things like all the inequality things. But those are the three big ones, active to passes, the private versus public, and what's going on with this competitive set? And it's mostly a US thing, but those are the three big ones.

Patrick: [01:05:56] Wonderful. Well, as soon as those papers are out, we'll do this again. Thank you, as always. This was a blast, and we'll see you in a year and a half.

Michael: [01:06:02] Thank you.

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