Michael Mauboussin – The Four Sources of Alpha - [Invest Like the Best, EP.126]

I know firsthand how complex the tech stack is for asset managers, and seemingly every new tool and data source makes the problem even worse, adding more complexity, more headcount, and more risk. Ridgeline offers a better way forward, one unified platform that automates away all that complexity across portfolio accounting, reconciliation, reporting, trading, compliance, and more, all at scale. Ridgeline is revolutionizing investment management, helping ambitious firms scale faster, operate smarter, and stay ahead of the curve. See what Ridgeline can unlock for your firm. Schedule a demo at ridgelineapps.com. Hello and welcome, everyone. I'm Patrick O'Shaughnessy, and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at investorfieldguide.com. Patrick O'Shaughnessy is the CEO of O'Shaughnessy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaughnessy Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of O'Shaughnessy Asset Management may maintain positions in the securities discussed in this podcast. 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. you 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 sort of 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. This is a course I've been, this is my 27th year teaching this class, and it really has evolved.

over the years. But it basically is broken into four chunks. And 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 markets and market, I call it capital markets, markets, market inefficiency. And sort of 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? And the second component really is market inefficiency. So why, if you're an active manager, why do you think you can do better then? benchmark. The second big block is just on valuation. And this is translating prices into value and value into prices. And 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, and what's good about those things, but what their limitations are. So as I would say, students, you have to earn the right to use a multiple. You have to understand what's behind it. And 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? And 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 ago, 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 sort of building blocks. And again, you'll see the most of the stuff I do.

can fit into one of those areas pretty well. And there are a lot of little subcomponents to those, but those are the big four. You mentioned before that the last one, kind of decision-making, may have 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? 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. not just playing matches, he's practicing. And 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 helpful in allowing you to be more effective. So part of it is your whole life, people have gone to elite schools and they've 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. And so that to me is a big part of it. And, you know, 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. 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. And by the way, they're not super time consuming. They're not super expensive. It's their 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. So another thing that we were riffing on a bit is this idea that there are huge accepted concepts, take 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 is going on. And so I want to do that root level work with you today on those two concepts specifically. So we'll talk about valuation, specifically 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, like 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 go into each of those vertical buckets one by one to really lay it out for people. I was indulging that as well, sort of those things. And I do, I ended up 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 like I pontificate for years and years about capital allocation. And I tell my students capital allocation is the most important thing assessing managements. And I really had very little to back up what I was actually saying. And then finally, I kind of got off the stick and did a much more deeper dive on that. So this is one of those areas where I think each of the many of the components that I talked about in the piece are things that I had thought about or they were not completely novel, but really trying to ground these things and sort of solid. what we know in academia and strong academic 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 like, 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 it. The one thing I'll say right off the bat, the one thing I thought was really interesting that I'm not sure, I'd read it a bunch of times, but I 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 an important paper, obviously, given the time it was written in 1980. This is sort of probably the peak enthusiasm for the efficient markets was the late 70s. And 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-Petterson's got this great phrase, markets are efficiently...

But this sort of makes a clear demarcation that there are sort of two different markets we're dealing with. One market is a market for information. And the second is the market for assets. So it's basically how you can implement that information. And you can be in situations where the information is super clear and obvious, but it's actually prohibitively or impossible to implement that to make money. So the famous example that was in the late 90s, 3Com owned Palm Pilot and they spun out 5% of Palm Pilot. That thing went bonkers. So if you bought a 3Com share, you owned the 3Com business plus... basically 95% of Palm Pilot. And it turns out because Palm Pilot skyrocketed, the value of the three-comp business was negative $22 billion. So this is the most obvious trade on the face of the earth, which is you short. Palm, you go long, three, calm, and you cannot lose money. But of course, there was no borrow. So this is this idea that we also have, not only is there just information, but we have these frictions we have to think about. And it's really both those components that are super important. So that was the other thing just leading into the whole discussion. That's something that I probably, I mean, people know about arbitrage costs and frictions and so forth, but I probably. 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 that demarcation as clearly as I should. 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 so incredibly important. And it keeps happening. Like 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. I think you had a line in there somewhere like, beware behavioral finance. Yeah. So let's start there. That's a great place to start because there's a simple, it's almost like a syllogism, which I think is incorrect. It seems correct, but it's not, which is something like this. Individuals, you and I as individuals are suboptimal. We could use the term irrational, but whatever, we're suboptimal or just we don't make perfect decisions. Markets are made up of people.

And so markets must be suboptimal or irrational in some way. And that C does not follow from A and B. And 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. And so, you know, 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 that 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 kind of an important, especially because, and I kind of go through this distinction in my class. We talk a lot about the psychology. The issue is not really psychology. Psychology is like the unit of analysis and individual. What we're really talking about is more sociological, which is like 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 want to be, 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. At the point of maximum pessimism is the magnet drawing you, right? So unless you're extraordinary in your temperament, those extremes, you're going to be drawn to it. It's almost impossible to not be drawn to it. And 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. The market's going to be much. So that's, I think that's a really hard thing to recognize is that whereas there are all these little individual biases and those are important for things in life. In a market context, those are not quite as valid. And I think, you know, so that's why I say be here, 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 behavioral inefficiencies. I think it's as simple as that. 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 like, 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 behavioral to begin, what would need to be present for you as a person making a trade to feel like some degree of confidence that you know that the error is being made? So like, what are the specific errors that you could take advantage of? You're asking like, what percent of the error comes from each of the buckets? Yeah, that's a really interesting question. And I'd love to get your answer on that or what you think about that. I was thinking about this. Yeah, I mean, in the other day, in some ways. I would just say that taxonomy is a little bit contrived because at the end of the day, I guess you could say probably everything is 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 expect 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 can see sort of different. And then it would probably be technical where they're technical doesn't probably day in, day out. Probably not a big deal or it's a little bit every day. But periodically, it's a gargantuan thing. So this is like huge unwinds and arbitrage. And we can talk more about that in detail. Then analytical, I think, is another interesting one. And perhaps the analytical thing really would be it's more subtle, but I think maybe the most important component there is really like a time horizon thing, which is.

So, and then how do you know? I mean, the second question is, how do you actually know? And I think that's a really tricky one. But the first thing that, you know, for example, in behavioral warnings, we talk a lot about, and this is sort of the most established thing in the literature is this idea of over extrapolation. So you sort of take, and this, you know, I think that's sort of the backbone for things like the value factor and so forth, and even momentum, right? You say to yourself, the thing that's interesting about over extrapolation is that you see that basically everywhere. So, you know, I was actually doing some work for a different purpose on. Well, it was actually having like long-term contracts for baseball players, right? And so it's not 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 like, okay, baseball GMs and they have analytics teams. They've got the data. They've got the data and they've got like big money on the lines of millions of dollars, right? Like 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. And how do you offset that? How do you realize that you're on the right side of it is, 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. And everything we're talking about is probabilistic, but I do think the behavioral one is probably the biggest source of these returns. But again, emotionally... it's the most difficult to actually exploit. Another way of thinking about this is how either even or lumpy the distribution of these edges are through time. So you've already mentioned this idea that 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 seat? Well, I think first of all, that's not correct observation. And there's another thing that's really important in all this, which is sort of agent, you know, principal agent type of issues, which is exactly to your point.

And sort of the greatest moments of opportunity is often when people who should be acting don't have, either they don't act, but more importantly, you don't have the capital to act. And that's, you know, we spend time about that mostly in the technical thing, which is there, 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 sort of the epic arbitrage opportunities existed. And you say like, 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. And hence, they, as agents, couldn't be. This is Julian Robertson closing shop in the late 90s. Precisely. So I think that's sort of 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. principal capital to stand ready to take advantage of these opportunities. So 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 writes a check when we ask for one. And if he's doing his job correctly, right, when 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, you know, see if you can go off and do something else with it. And that's a really interesting. Given how we've institutionalized a lot of this principal Asian stuff, it becomes a very difficult thing to realize in real life. I love that because one of the things we've 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 of built like opportunity is evenly distributed through time, but it really isn't. And that may be oftentimes the best place for an active manager to be is an SPY and to wait and act when there is an opportunity. But it's incredibly tricky to do because of some of these principal agent problems. The next one in is analytical. And 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 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. 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? I'm sure, Patrick, you're a better tennis player than I am. I'm confident of that. And so if we play tennis on the weekend, you know, you're just better at it. So we have the same racket and the same tennis shoes and we play on the same court and 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, it's actually in some ways you could say it's behavioral, is we call it the concept of information weighting. And there's really obviously a pretty rich literature on this in the world of psychology. And the idea is you have, I mean, it's called predictive validity and strength. But it's this idea of saying, like, there's a strength of a signal, and then there's how valid a signal is. I think we get confused by that analytically. So the question would be something like, I'm going to flip a coin. Is it tail biased or something, right? So the first 10 flips seven times shows up tails. Hmm, what do I do with that? And 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 know, you flip the coin 10,000 times and it shows up tails 5,100 times. That's actually a much stronger validity is much vastly stronger. But the strength doesn't seem as high, just sort of like, ah, 51, whatever, right? So that ability to be able to distinguish, and it goes ahead. So we tend to be tricked by small sample sizes. And so analytical is sort of 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. You know, we call it being a good Bayesian, but...

You know, if you said to me, what are the biggest like of the cognitive biases? What are the most challenging ones? You know, I was probably overconfident to be on there. But the one I would probably say is confirmation bias, which is to say when you're just especially when you're a discretionary manager and you decide to make you make a decision and 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, you're going to it's going to break in favor of you. Right. You're 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 and that's a I mean, that's a big one, I think, analytically. And the last one, and you and I have talked a lot about this over the years, this idea of time arbitrage, which is how do we think about playing for a different time horizon than others? And that goes back. The signal and noise is another good way to think about that. Time arbitrage, to me, would be something like when the market is reflecting noise as if it's skill and pricing that way. And so for you to succeed in time arbitrage, A, you have to understand the signal better than the market. Second, the signal has to... reveal itself. And third, you have to be around, right? Like you have to have the money with capital at that. And so those are the three sort of conditions, but playing a long game, it's really, it can be, 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 all very tricky. They're tricky both from 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 not to want to update our views. 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 where someone asked the question, well, how can you expect to do as well with all this?

capital you have now? Like, 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 mindset and this sort of time arbitrage. When one of those opportunities presents itself that's big, we're like one of the only buyers that can do it. So we face less competition. And 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? For sure. But, you know, it's interesting. But these also, you know, Carmen's, I think, very unusual in the sense that he's been willing historically to run very high cash balances in his portfolio. And the way, if I were to justify that, so most firms don't do that, mostly because an allocator says, we're going to give you our money and we want you to invest in large cap equities or whatever it is, right? And so go do your thing. And that's kind of the bucket you're in for our portfolio. I think sort of the argument in favor of what Carmen's doing is saying something like, oh, 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 like, who's got the options? And like Berkshire Hathaway is just 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. And that gives them the ultimate optionality. And that's super hard to replicate. So that's, but there are organizations trying to do that. You know, it's like call on capital. Why would this big sovereign wealth funds not say like, even you could do almost like a Ulysses contract, right? Like if these levels come, you know, if the spreads in the high yield bond 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, and by the way, if the spreads get that level, I can promise you they're not going to want to write the check, right? Because they're going to be freaked out, right? So that's precisely the point of it is like, so I, yeah, I do think that that is a, that is a really interesting concept. But again, generally speaking, being much bigger tends to be deleterious.

results. I think that's roughly true, but, but this is a case where he can give you the option value or you can become the last person standing. And you think about even like, if you had had, you could write a big enough check to take over long-term capital management portfolio, you would have done just fine. Right. I mean, so, but there were, and you know, he called, I think Soros and Buffett, there weren't very many people that they could call to make that, write that check. So 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, to informational last. Wait, it's bait. Oh, bait. But I'm going in your, I'm going in your order. I'm going in your order of the weight of the pool that the stocks have. So the technical one is actually the 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 for 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 return. Yeah, I mean, the first big one is just, are there people, I mean, the basic, I try to define it 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. And I think sort of the classic one on that or one of the best developed ones is the work by John Chinacopoulos at Yale on the leverage cycle. And the basic story is pretty simple, which is optimistic people like an asset, usually for good reason. The lenders tend to be quite generous. So haircuts are attractive. And so they can borrow a lot to do that. Asset goes up, everything's good. And then, you know, some... A bit of bad news comes out. And then so that asset goes down. So what happens all of a sudden, that was a feedback cycle on the way up. And now it's 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, hmm, this thing, we gave you these really good loan terms. Now that maybe we would have to think that. So now you need to put up more collateral. And so that forces even more selling. So you get to sort of 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 before, these things I think tend to be quite episodic, but those things are really big deals. Fund flows, and this is something you probably know much more about than I do, but fund flows I've always found fascinating and tend not to be... I don't know, they tend not to be the center of a lot of discussion for some reason. And 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. People tend to give money to managers who have 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 the fired manager does better than the hired manager in the subsequent 24 months or something like that. And so there are studies of planned sponsors that suggest that. But there's another interesting component, which is disentangling flows from actually performance, right? Which is, there was one really interesting paper in the Financial Analyst Journal a few years ago where these guys, up to a third of alpha, quote unquote, 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 like, 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 sort of compounding effect that, you know, that's what's super interesting. So just looking at how people are spending money. And by the way, we played around this. I'd like to keep working on this. And you might know much more about this, but I was like, you know, here's a simple model, like pick an asset class or a sector, an industry, whatever it is, and just ask a simple question. Like what I would look for two signals. One is like positive flows. and like more than one standard deviation versus historical valuation and just short those and then negative flows and one standard deviation cheap and go long it's like that would seem like an incredibly simple algorithm that may actually be pretty good so i don't know if you guys have played around something like that but that that would be something i'd like to continue to explore there's another one i thought was interesting which is

It's related to the flows, which is this idea like demand shocks. And there was this great paper by Gompers and Metric a bunch of years ago where they talked. I thought this was mind blowing. So the first paper, one of the first kind of factor oriented papers was in the early 1980s on small cap versus large cap. And basically the researchers were like, hey, small cap is actually really good. It gives you better returns you're supposed to get from cap average. So 1981, you're like, oh, I got it, man. All you do is buy small caps. I'm good to go for the next. You would have gotten smoke, right? For 20 years. So Gompers and Metric, these two academics are like, hmm, what's the story here? And the story is basically we were getting to the institutionalization of money management. And as a consequence, people, individuals are giving their money to the big mutual fund companies. So it's like a T. Rowe Price and Fidelity and Capital. And by the way, what are their incentives? Their incentives were to buy large cap stocks. Why? Because they're easier to, okay. So these guys were like, in pure academic theory, demand curves for stocks are basically. But we know in real life they're actually sloped downward. And so these guys, their argument was it turns out that a reasonably decent chunk of the excess return of large cap was what they call demand-based return, which is just based on flows. And that's – we're not talking about like months here. We're talking about a 20-year, a 15- or 20-year period. So I think that's another really interesting one is asking about this sort of demand-based. And by the way, I think in the early 2000s, we actually saw the opposite of that, which was if you look at like hedge – sort of the golden year of hedge funds when they were – size was really after the dot-com bubble burst, right? So called, you know, 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 basically underexposed or short to the high stuff. And that was an incredible trade. But the very fact that they were buying those small and mid cap stocks, it self-created demand for those that actually helped them their own performance.

And then, you know, the last thing we've talked a little bit about, which is historically there have been instances of tremendous sort of mathematically tremendously interesting opportunities for arbitrageurs. And because of principal agent problems, they just can't show up. And, you know, and again, like long term, you know, like the very famous on the run, off the run bond story from long term capital management. It's textbook arbitrage 101, you know, 30 year bond, 29 year bond. It's as simple and straightforward as you can possibly get. The spread just kept going the wrong way just because there was no one there to take the other side. 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 like a total market index and flows into funds like that because kind of by definition, they're buying the whole thing, Vanguard total market. But there's obviously been a huge growth of... whatever it is, sub-indexes by following a whole bunch of different rules and any sort of distortions or alpha opportunities that flow of funds in ETF specifically. create in the market? Well, I'd like to get, you probably have a more informed view on this than I do. I think it's an incredibly interesting question. I spent a lot of time thinking about active to passive in general. And obviously, you know, sort of the traditional index funds, like you said, may not make many waves, but sort of the ETF thing, specialization seems like it should have an impact. And that, 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, gold, 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, you know, like lots of money in and high valuations, lots of money out, low valuations and so forth. So that would be one element. And maybe the other thing you put in there as a wrinkle is ask about, are there differences in liquidity, sort of liquidity parameters for each of those different things? So, you know, huge, large cap companies, liquidity is not an issue, but you might start to get into, you know, whatever stocks, 1,500 down.

where they have ETF participation, where the liquidity may not be quite as good, that could be also another factor. And that goes back to our first thing on information and implementation. So yeah, I'm not, but that's incredibly interesting. And it's an interesting question about, I have a good friend who's sort of 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 every company that's involved in indexing ETF, and then you look at how many companies, if there's a residual or not in the orphans, right? And it's like, again, I don't think it's a big, you can't run a ton of money in that, but it's really interesting because you know that there's nothing going on with those things, right? Anyway, so that's one factor that I've heard of, which is kind of we haven't done the work on it, but you take the aggregate weight of stocks, like the average weight of stocks and ETFs and compare that to its overall market weight. And companies where it's overrepresented in ETFs, perhaps that's somewhere that you should apply factors or. That's super cool, right? Same sort of same mindset, right? Overrepresentation. But I mean, I guess you are making I mean, the premise there is that that's. excessive in some way, if it's way overweighted or if it's way- And it's only like, you can only look for like 10 years or five years. So like the sample is small, right? But it does seem like an interesting, the orphan idea is always interesting. Yeah. But that, even though the sample size, we don't have enough data on that, but that is, 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. So the last one is informational. And 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 the 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 like 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 today. Yeah, obviously this is, I think, the most difficult. And you'll see, I mean, you sort of see that I went off.

on some other sort of, not they were related issues, but yeah, so getting the information before everybody else. Look, there's got to be an original source of 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 and so forth. There was on the regular, I mean, and by the way, there's a lot of academic research on this. I think it was the case that large, for example, large mutual fund families would have access to management teams and would get information that was clearly advantageous. them. And that was quite clear because you see that the alphas pre-Reg FD and post-Reg D for some of these large mutual fund families degraded. So that's pretty good evidence. But there was another study I thought was also super cool on this, which was when Reg FD was put into place, credit annals were exempted. And by the way, that was reversed in Dodd-Frank in 2010. So there was like this eight-year window where actually equity analysts could not get access to information. Got a side-by-side, yeah. Got a side-by-side and actually flip both ways, right? They had it and then they didn't have it. And it turns out that the information embedded in credit changes in that window were vastly more consequential than before and after. So super cool, right? So there's some evidence that that's going on. And then the other thing on information, I think, okay, so the other thing, I think these are sort of two tricky areas. But one is... I've been fascinated by this literature on, and I actually did a whole conference on this topic, but this literature on limited attention, right? So the point is like, I can give you tons of information, but you can only process a certain fraction of it. And, you know, so obviously the very famous Shabri, the gorilla walking across the video of 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 obvious and important in this, right? Like 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 that attention information, but that's, you know, you're just- It's important. It's important. You're just discounting stuff. And then 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, and the literature is called task complexity, which is there are a lot of moving pieces.

And there's information in the value chain that sometimes the market tends to be slow and reflecting in the appropriate actors throughout the chain. So like that's, you know, so tax complexity is the more complex the task is. 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. And look, I think that a lot of, for example, a lot of discretionary investors continue to, and I think there probably can be some, there's some art to it, but they continue with sort of the Phil Fisher scuttlebutt thing, which is say like, I'm not going to have any special information, but I'm going to gather information from, 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 ask you about this most effectively, which is it does feel like there is an arms war. in terms of this stuff. And I make the distinction in this paper, which I think is pretty obviously the difference between data, which is things that are presented to you, and information. And those are different things. So like data by itself actually doesn't do much for information. And technically in information theory, information is what reduces uncertainty. So information is what's really valuable. So then these gets in these really interesting questions, like, do I need the same data as everybody else? How good am I translating data into information that allows me to act on in a way that creates value? And that's a whole nother really, 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. is the most interesting of these things, right? Because the 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? 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 sort of 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 like what are the major most notable evolutions that you've noticed i mean look i think that there are 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 thing that's most noteworthy is the introduction and more serious integration of quantitative or systematic approaches. There's sort of this, how do we get to this holy 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 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 judgments 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 like an ongoing thing. And 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 they have sometimes they kind of look at each other funny. And so who are the people that can 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 there's a sort of a value chain between sort of identifying mispriced securities to delivering good returns in your portfolio. As you look at that value chain, asking the question along the way, like 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? And let me give one example to me. And 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 is like a really interesting area, which is at the end of the day, if you know what you're trying to do, you know what your constraints are, it's basically math. You have inputs. They're 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.

And 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 it over the years, is that if I endeavor to be a long-term oriented investor, right? So I'm looking at, let's 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, you're 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 us intermediate feedback that's useful and timely, right? And so that's where like the work by Phil Tetlock and others on things like Breyer scores. What's a Breyer score? I'm not familiar with that. A Breyer score is a measure of the quality of your probabilistic forecast. And Glenn Breyer himself, by the way, is a meteorologist. So it was basically like judging, you know, you woke up this morning and said, you know, X percent chance of snow, whatever it was, how accurate are those forecasts, right? And so the way a Breyer 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 a Breyer score, the scale can go to one or two, but a scale of two, let's say, is you're wrong about everything. Like exactly the opposite. By the way, if you have a Breyer of two, you're useful. I just do the opposite, whatever you say, right? So it's a way of keeping track of problems. Now, there are a couple of things that are really important about this. One is... Whenever Breyer scores are kept and then given 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 gets makes people better. So you say, OK, 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 priori is lay out. The path that I expect is the thesis path. I expect this to come down. Again, it's deviating from what the market believes. That's a really crucial thing. And then I should be able to assign probabilistic forecast to certain signposts. So I believe that sales are going to be higher than the market believes. 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 Breyer score, right? 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 sort of, and now all of a sudden I'm giving 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, hmm, I thought this was going to happen. It didn't. Let's talk about whether we're in the right same place and the thesis is still unfolding correctly. So those are like, that's a technique that's, yeah, super cool. And 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 like, 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 is very messy and noisy. And again, if you're using two or three year horizons, this is a way to give you sort of waypoints along the way to make sure you're doing things right. What a fascinating... loop that I bet almost no one does. I know of a couple firms that make their analysts, their discretionary analysts, instead of those one that comes to mind, input their... all their data and then they can, you know, use that to train a model or whatever. But my guess is like, nobody does that. And it's just like a, like a straightforward thing. And you can write it down and it's, you know, it's not hard to do. Yeah. You just got to be disciplined. And the thing is that some of these techniques, it's not like it's way, they're not like super expensive or fancy. It's not like highfalutin math. It's really straightforward stuff, but it gets around you. And the other thing I'll just say about that is that it is interesting that I think in a lot of other probabilistic fields, the really best people do document it. So I think, I think if you look at the real. 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. And we were talking about Andy Duke is a great example. Andy's an amazing like decision-making person in general, but this is the kind of stuff that would be very second nature to what she's doing and what they think about. And, you know, having people and even getting people that are, that are sort of like-minded, critical, like-minded people around you can really be helpful. 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 prior 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. And it's just kind of an interesting way to think about some of these ideas. So if you had to be a GM. and extra points if you name the 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, like, where you'd most enjoy being a GM and 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? Man, that's really interesting. So I grew up playing soccer. And even though I love soccer, I don't spend that much time focusing on it. Ice hockey and lacrosse. I played lacrosse in college. And 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 interest, probably ice hockey. And I think ice hockey is probably the furthest behind a lot of the other sports and trying to pin down some of these analytics. Again, like baseball has gotten pretty sophisticated. And recently I was with Jeff Lunau, who's a GM of the Houston Astros. They're talking about what is the frontier? You know, Astro's done a phenomenal job and Jeff's done a phenomenal job. And the question is like, so what is the frontier? And, you know, Jeff was like, he's very cagey about it, but he's like, you know, 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 sort of these natural performance maximizers? And that's sort of 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 baseball is the first. Basketball, I think, has also come really far a long, long way. And, you know, so you think about Daryl Morey at the Houston Rockets and what they're doing and how they think about it. Sam Hinckley. 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 is another one. Just seems to me that there is some.

Bill Belichick, some of these guys are just operating at a completely different level. Yeah, saving in Alabama. Yeah, I think the Super Bowl is an example of, you know, Sean McVay is a great young guy. I'm sure he's got a great career ahead of him. But I think that's like, that wasn't even a fair match in terms of coaching. So football, but football's, the thing is a lot of football is like a really good head coach and usually you need a good quarterback. Quarterback's such a key thing in the whole thing, but there's a ton, 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. And I also think that there's still, that it's gotten a lot better, but even game time decisions, you know, and the classic ones punting on fourth down and, but even two points versus one points, clock management and so forth. But I think hockey is a little bit like football too, which is I think that there are a lot of just traditional people that do it. And a lot of the coaches are people who played in the game over time. And so it tends to be more difficult to crack. Baseball, for whatever reason, sort of let the sort of academic type of guys in, right? And they've been between. Theo Epstein and Jeff Lunau and the Moneyball guys. These are pretty cerebral guys, and they've enjoyed some success. So to me, hockey is a thing where it's still, I think, probably very early days analytically. It's a super fun sport. But it's also, there are some really big challenges. One is that it's a very difficult thing to chart, hard sport, because possessions are not discreet. And then the other thing is, and even though there are roughly six players versus five players in basketball, and the length of the season is the same. In hockey, you know, your star forwards are on the ice, whatever, low 20s, 23 minutes, 24 minutes, 22 minutes. Whereas LeBron James is on the court essentially the whole time, right? He might take a minute or two off at the end of court or something. But so that is just the dynamics are different. So it's the sport adjusted for sample sizes, sport, well, maybe baseball, but it's the ones closest to randomness, which is interesting. So, but I think that's another really interesting area. And so stuff like, and that's.

Again, player acquisition. How do you value players? Goaltending. How do you think about scoring goals? Shot breakdowns. And there's a really cool analytics, hockey analytics community out there, and they're doing some really neat stuff. The other thing is I'll just say, you know, I play sort of beer league hockey, so I still play. And, you know, it's funny because that stuff's in my head, actually, as I play. Of course, yeah. And we'll give you one example. I was with a guy who was a money manager who actually was really into hockey. And the guy's like, you should never shoot. Like in lacrosse, you can shoot wide because you can back up the shot. It doesn't make any difference. Basketball, you miss a shot. try to rebound it in hockey you should 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. A save it can be a rebound. That's usually good. Goalie 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. So shooting wide can be really bad. Anyway, so probably hockey. And I think that I just love talking to all the analytics guys. And it's fascinating to me only because, again, like you think about the NFL, huge dollars moving around, right? These franchises were billions of dollars, right? So we're not talking about the equity value you could create by being at the front team. It's not chump change, right? And yet there's still like a lot of, we talk about our inefficiencies. So I will mention that I sent this, I sent the report to a couple sports guys and one or two of them wrote back and they're like, yeah, dude, I know this is investing, but like this is exactly our world. Like 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. And 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. 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 like take two, three minutes to set the record straight here and just get your opinion on

what the source of this is? Like what is so hard to understand about what to me seems like an incredibly simple mechanism? Yeah, I don't know. I'm with you. I'm like mystified by this whole thing. I do think there is an underlying thing that is probably a real least concern, which is that companies, generally speaking, 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. And 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. So that's understand that sentiment to some degree. Now, on the on the wage thing, by the way, the thing I think is hard for people to look there. 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. But assuming that there's no change in behavior of my employees, and that's a big assumption, but assuming that my employees 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-viable and I'm gone. There's a notion of competition that somehow gets lost in this discussion. It'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 are examples of where that is a very valid. legitimate strategy all right but going back to buybacks so i you know to me and by the way the other thing is that buybacks really weren't legal until 1982 for a bunch of different reasons and so to me this i i can't i i don't understand this at all i mean and by the way the repatriation this whole if we had a tax rule gave people some a green light they had to bring the capital back at 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 worth at least thinking about when you're looking at a company buying back stock, and you guys, I'm sure, empirically, companies that buy back stock, that tends to be a good thing, right? They do well. But there are sort of, I call it the three motivations. One is sort of like the market efficiency motivation. 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 maybe overpay, underpay, but it washes out. The second camp is the intrinsic value camp. We will be very strict. We'll only buy back our stock when we deem it to be undervalued. Now, most management teams think their stock's always undervalued, but that's fine. But, you know, there's some guys that are better than others. You know, Will 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 for many years. Bill Steers was a genius at 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. And I think that's the other thing where this comes up, right? Which is the impure motive says buybacks could be accretive to earnings or accretive to ROE or allows us to make our bonuses or offset solution from options. And these are things that may not be purely economically motivated. Now, they may be benign, but... We don't know. So there have been, I can say for sure, there have been cases where companies have 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 stake 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, you know, that 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 buy back stock. And you probably should never want them to pay a dividend, right? Because you have to take the dividend. Get taxed. 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. I'm with you on the frustration. And it's stunning the lack of evidence that seems to be, the people that are vitriolic against buybacks who pretend to be like pro-science and pro-facts, like completely ignore the evidence that they're just. So the last one would be your paper on EBITDA. So yet another metric that everyone talks about. It's sort of the king of the value factors in many ways, popular in the PE industry. That again, like 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. Well, it's interesting, Patrick. You know, same thing, like, and I'm with you. We've now, even my investment career, which is a little more than 30 years, this was. This is very sparingly used in the 1980s and has become, like you said, I mean, it's not – I think we cited in the paper. I think PE is still the number one thing people use, but EBITDA is not right behind it. And, uh, which is really interesting. We also showed, it was just a Google Ngram thing, like the citation of the term. And like, you know, and by the way, I don't really know where it came from. I think Will Thorndike suggested that it came from John Malone at telecommunications Inc. And I, and I, and I went back and read about, and I think that's probably as good a place as you can. So that we'll call that, you know, 73, 74, 1973, 74, when that got going. So yeah, once again, that people talk about this a ton and. No one really is grounded. So I mean, I just sit in investment meetings all the time. Like this is an eight times EBITDA business, 10 times EBITDA business, like six times. Like 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 like, okay, you're looking at EBITDA. How much of it is EBIT and how much is it 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, it's an indicator of capital intensity right away. That then becomes an indicator, usually of balance sheets, right? So financial leverage. So we're like, okay. So we wrote this piece that was very popular called, What Does a PE Multiple Mean? And it was like going back to Miller and Ridigliani, some basic finance side, like, okay. We know that given certain assumptions for growth rates and... return on incremental capital, and we can define that more specificity. Here's the optimal P you should pay, lockdown cost of capital and some other assumptions. And that's a very intuitive thing. And by the way, that leads to a couple of lessons that are really important, right? If a company's value neutral, like it's earning something close to its cost capital, you'll see the growth, it's very insensitive to growth. And that's like you're on an economic treadmill. You turn it up, turn it down, you don't go anywhere, right? If you have high returns on capital, the business is incredibly sensitive to growth. And that's what people sometimes get, they're surprised by this, right? 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 estimates by 5% and 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. We're like, can we map that over to EBITDA? That's a little bit more complicated because you have to assume capital structures. But we basically said we're going to do different categories based on this ratio of EBITDA. And then we locked down capital structure, but there's obviously some dynamism there. And then you get sort of, again, these baseline value-neutral businesses. Here's what we call the commodity multiple you should pay. High returns, you pay more. Low returns, you pay less. And so it's, again, just trying to ground all this stuff. The other interesting question, by the way, and I'm embarrassed how much I learned about this, which is of the DA, depreciation and amortization, how much is AH, right? So that's another, like, hmm, 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.

If you're not sort of 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 love writing that because I also went back and did a history of valuation. How do people think about valuation going back, you know, a hundred years? And I actually ended up buying some book. I bought some books from like the 1920s and these incredible tables of like how these guys used to do these calculations. And just to say like, again, let's ground this. And so, as I said before, like you have to earn the right to use a multiple. In other words, and this is what I say to my students. I'm like, you guys can use multiples to your heart's delight, but you can't use a multiple not. tell me you understand the underlying economic assumptions that justify it. And if you can't tell me those things, then you're not in business. So if someone wanted, like, let's say a hypothetical quant firm wanted to improve their EBITDA ratio, and the rule would be like, you have to do it systematically. So obviously, I think the best version of anything would be 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 EBITDA, like capital intensity? Would it be return? invested capital? Would it be both? It'd be both, but those are related. So it would probably be return on invested capital. Yeah. So it'd be return on incremental. And we were talking about this before. It's hard to measure incremental returns, but it would be some sort of expectation. And you can use sort of the history maybe as a way to project into the future. Yeah. I mean, it's the two dimensions and turning multiples are incremental returns and growth. And that's it. I mean, basically, if you have those things down, 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 and so forth, like the relation to EBITDA changes and so forth. So it gets a little tricky in that regard. At the end of the day, these are all – and that's what I kept saying that 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, time out. Let's make sure that we're –

kind of connecting the first principles to what we're doing every single day and make sure that we keep that bridge sort of well-structured and we're going back and forth in an intelligent fashion. It's a great paper to read, one, for the ratio itself, but two, like if you're allocating to a manager, it's a great question to ask. So tell me what matters to you and then tell me how you decompose it. And I think you'd get disappointing answers. I think you get very disappointing answers because I think people... And by the way, the other thing I should say really quickly on this, that this is the other thing that's really interesting about all the stuff on multiples, is actually it probably doesn't matter that much. If it's an efficient market, it actually doesn't matter that much. So you can... be a total dope with your valuation work and your multiples and you just buy and sell in the market. Like you're in the markets are efficient. It's sort of carrying you along. Right. And you'll good luck, bad luck or whatever. But right. So it's a really interesting, this is like for people who want to distinguish themselves and try to find these inefficiencies. But if you know, like the market does what the market does. And I will say though, by the way, that this is a, this is a big deal for private equity stuff. And I think that's a, 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 in all these sort of assumptions about the future that are built into, you know, and asking you to pay for those things today. So that's. We're getting into the shenanigan zone a little bit on some of this stuff. The 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, talking about a notion that I'd never heard of called benign myths. And I would love to just hear you riff on this concept because of how powerful a concept it is. everyone doing just about anything yeah and i first learned about this from

Jim March was a very famous organizational psychologist at Stanford. And, you know, by the way, his stuff is great. It's worth really worth reading all the stuff. And so I'm at the Santa Fe Institute and I'm at this session. I'd never heard of this guy. And this, he was an older gentleman at the time. And, and I'm like hanging on every word. This guy was like so fascinating, everything he was talking about. So I go look him up afterwards and it was like, all right, this is a really famous guy. So he, he, what he was talking about that particular day 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. So I think a lot of stories or a lot of narratives could be probably in this category of 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. And you might, this could be controversial, but you say it. 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 people to some of the research methods of Jim Collins, a very famous guy on sort of company research. And 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 is like 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. And so you might say like that's an example. It might be under the category of benign myths. Like the research may not be exactly the highest quality science.

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. It's like, realize that there are a lot of these narratives and stories that they get you to do the right thing. And that's important. It's a wonderful closing idea, especially for me as a quant, where 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 kind of big pieces recently, what's on your mind next? So a couple of things. One is that we're working on specifically, maybe three things. One is we're working on a piece on active to passive in fixed income or bonds. And obviously the equity markets are much larger than the credit markets. And so that's a first place 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 are simple things like the Barclays, the Bloomberg Barclays credit index is something like 10,000 securities, whereas the S&P 500 is called 505 securities. And so 10,000 securities is a 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 alternative. And equity is pretty straightforward. You go buy a spider and you're good to go. It's a 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. And by the way, the other thing, going back to the stuff on bait, credit's another area where there may be a number of actors in the credit markets, especially like central banks and so forth, that may not be completely economic. They're not looking at fundamental value. They're doing other things, right? So that's one area. Another area is just we touched on briefly, which is I find this incredibly fascinating apparent migration from public to private.

So we've well documented that the number of public companies is down a lot in the last 20 years. And at the same time, we sort of had this rise of this ecosystem in private markets, whether that's private equity, but also sort of 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. Maybe with some of these, we're seeing a bunch of big high profile IPOs, certainly Lyft and Uber and some others. And we'll see if that sort of 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 that, you know, 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 spider and that's fine. 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 Jim Tepper? But there's a whole thread of stuff out there I think is really interesting. So if I recap the premise of that book is something like we've had the Justice Department has been very lax. We've had a lot of consolidation. As a consequence, most industries are now very consolidated. Measures like the Herfindahl Index and measure of industry concentration have been like sort of skyrocketing. It's almost like the mirror image of the number of public companies. You've seen that increase. And as a consequence, when these companies get rich, they can lobby and they can change the regulations in their favor and so on and so forth. So this whole interesting idea about what is the role of competition. And then there's this. Weird thread of research. We haven't talked about this, about cross holdings. And so there was this one paper I just thought it just sort of blew me away. So the argument is something like if you own the company, I'm the sole proprietor, you're going to try to maximize profits for me. But if I own multiple companies, including your competitors, you're going to try to think more like a pool versus just us. So these guys, they did it with the S&P 500 and they found that if zero, they put on a scale. So if zero is perfect competition, like we go after each other every single day and one is complete collusion.

They said we've gone from like a 0.2 in 1980 to like a 0.7 now. So it was like, yeah, I mean, you could talk about the methodology and so on and so forth. But basically, the arrow, I mean, that's extraordinary. And so what's going on with crossholding and so forth? So that's in a whole other 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 stuff. But those are the three big ones, sort of active to passive, sort of the private versus public, and then sort of what's going on with this competitive set. And it's mostly a US thing, but those are the three big ones. 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. Thank you, Patrick. Hey, everyone. Patrick here again. To find more episodes of Invest Like the Best, go to InvestorFieldGuide.com forward slash podcast. If you're a book lover, you can also sign up for my book club at InvestorFieldGuide.com forward slash book club. After you sign up, you'll receive a full investor curriculum right away and then three to four suggestions of new books every month. You can also follow me on Twitter at Patrick underscore Oshag, O-S-H-A-G. If you enjoy the show, please leave a quick review for us on iTunes, which will help more people discover Invest Like the Best. Thanks so much for listening. Thank you.