Title: I Studied Every 100-Bagger Stock in History. Here's What I Found | Chris Mayer
Show: Value After Hours — The Acquirers Podcast (hosts Tobias Carlisle & Jake Taylor)
Guest: Chris Mayer (portfolio manager, Woodlock House Family Capital; author of 100 Baggers, Dear Fellow Time-Binders and The Investor's Odyssey)
Date: 2026-AUG-25
URL: https://youtu.be/Frp8iAhunNU
Length: 60:10
Note: Auto-transcript, lightly cleaned per the archive convention — pure fillers (um / uh / "you know" as interjection / contentless "I mean") removed and stutters and false starts collapsed; wording, numbers, names and hedges otherwise verbatim, and every (mm:ss) cue kept in place. Speaker changes are marked ">>" as in the source; three voices — Chris Mayer (guest) and hosts Tobias "Toby" Carlisle and Jake "JT" Taylor. Auto-transcript garbles corrected: "Tobias Carlile"→Carlisle; "Bergkshire / Burkshshire / Birkshire"→Berkshire; "Daniel Mendlesson"→Daniel Mendelsohn; "Odysius"→Odysseus; "Mer"→Munger; "Hunter Bagggers / underbaggers / Hundred Baggers"→100 Baggers; "Liftco"→Lifco; "Logger"→Lagercrantz (probable); "ACT tech"→Addtech (probable); "Watskco"→Watsco; "Ropers"→Roper; "Haiko"→HEICO; "Valiant"→Valeant; "Nabiscoco / RJisco"→RJR Nabisco; "Tom Warner"→Time Warner; "Samzel"→Sam Zell; "Troll Price"→T. Rowe Price; "Bess and Binder"→Bessembinder; "Alfred Krisky / Kriskian / Kripski / Kipsky"→Alfred Korzybski / Korzybskian; "Joseph Langam"→Joseph Langsam; "Solomon"→Salomon; "bearings bake"→Barings; "archo"→Archegos; "Dear Fellow Timebinders" / "deer filler time behind us"→Dear Fellow Time-Binders; "EV toe E bit"→EV/EBIT; "giving away chairs"→shares; "across enough winners"→winters; "if you went through a tin"→a 10-K (probable); Graham's line, garbled as "render a necessary and accurate estimate of the future", restored to "render unnecessary an accurate estimate of the future". "The great tit" is a real songbird, not a garble. One name at (29:18) was unintelligible in the source and is marked [unclear]; the phrase at (18:51) likewise.

(00:01) And I believe we are live. This is Value After Hours. I'm Tobias Carlisle joined as always by Jake Taylor. Our special guest today is Chris Mayer. He's a portfolio manager, author of 100 Baggers, and author of a new book, The Investor's Odyssey. Welcome, Chris. How are you? >> I'm doing well.
(00:20) Thank you for having me on, guys. It's good to be with you. >> Tell us a little bit about >> remiss for not having Chris on earlier at this point. It just feels like an unforced error on our part. [laughter] >> Tell us a little bit about the new book. >> Well, sure. So yeah, I wrote 100 Baggers and then after that I've done of course a lot more thinking about long-term investing and what it's all about.
(00:49) And so I've put this book together and the core — there's a story I tell in the beginning that kind of inspired a lot of the thinking in it, and that was that there was one year when I was headed to the Berkshire meeting in Omaha and I sat next to this woman; she was bumped from business class and there was an empty seat next to me.
(01:06) She was placed next to me at the time, got to chatting with her and turns out she was one of the original investors in Berkshire and she's been to every meeting and she knew Buffett and she had just invested with him and basically left the money there and she became very wealthy and then she lives in Nantucket and she's giving away shares to her grandchildren and I thought to myself, wow, that's — she basically made one decision, which is to buy this stock, and just left it
(01:37) alone. And she's got a track record that beats most every active manager anywhere on the planet. So it got me thinking about, well, what is a good investor? What do we say is a good investor? What does that mean really? So I started thinking about that and kind of using her story a little bit to unpack that journey.
(01:58) And I was thinking, well, if long-term investing is kind of like a journey — and we were talking a little bit before we got on the air that I had read the latest translation of the Odyssey last year that came out, one by Daniel Mendelsohn. And so that was in my head as well while I was writing this and sort of inspired thoughts about all the obstacles to holding on to a stock for a very long period of time, reminiscent of Odysseus and his journey and all the temptations, all the travails he has. So I use that
(02:27) too as a kind of organizing metaphor for the book and just talk about this journey. So it was fun to write. It was fun to put together and yeah, I hope people like it. >> You refer a little bit in the title to avoiding the siren call. What's the secret there? >> Well, the sirens are metaphorically for anything that is calling us off the course of holding on to our investment.
(02:56) So all the media that we have to put up with as investors all the time wants us to take action. That's sort of the bias, right? And every financial program or show or newsletter or whatever always makes it seem that whatever's happening now is really important and you have to do something about it.
(03:18) Now, there's all these headlines about things that are happening. So those are the sirens, those are the things you have to sort of ignore. If you're going to own a business for a very long time, it's a lot about ignoring the things that don't really have much to do with your business over the long period of time.
(03:36) So that's really a good metaphor for the sirens. >> What's the secret to ignoring it? [laughter] >> Yeah. And there's a lot of — there's no secret per se. I think a lot of it's common sensical. And I go through some >> looking for a friend, right, Toby? >> Yeah. Right.
(03:54) I go through a number of ways in the book, and some of them people have heard me say before — focusing on the business itself. And every business you can probably boil down to a handful of key essentials and just tracking those things. And as long as those things are within a reasonable range of performance that you expect, then you continue to hold on.
(04:16) And there are a lot of bad habits investors pick up on as well. So checking stock prices every day or multiple times a day would be one; doing that makes stocks seem a lot more volatile than they are. And like I say, those little blinking green and red colors are kind of calls to action. So you don't want that.
(04:37) So you have to avoid those temptations and you have to regulate what you pay attention to. So there's a lot of — and I try to talk about this in the book — years ago I used to read every day, I would read the Journal, the Financial Times. I'd read all this stuff.
(04:56) And now I don't do any of that. So you can cut back on your media diet as well, which helps >> from getting too distracted. There's a current example where AI is going to disrupt a very large number of businesses and a very large number of good businesses, but it's going to take a little while for that to propagate out.
(05:20) There's probably some people who are using it more than other folks, but given 5 or 10 years, it'll probably be much more widely dispersed and some of those impacts will be seen. Do you have a view on how you separate out threats that are not real and threats that are probably more >> versus manageable? There's no evidence in the results yet but you can see that it could impact them at some point. >> Sure. That's going to be something you have to evaluate case by case, business by business, but I do
(05:57) think it does remind me a little bit about investing in the '90s when you had the internet and then anything that might be under threat there was getting hammered or ignored, and I think you're in a somewhat similar situation. Though there will definitely be casualties — there'll be businesses that are just zeroed out as there were with the internet; Amazon has taken business from a number of old brick-and-mortar retailers, so it was an existential threat for a lot of
(06:27) them. But what I mean is today nobody would claim they have a competitive advantage because they have a website. It's just a ubiquitous technology. Everybody uses it. >> And we're probably like that. I imagine AI will be something like that.
(06:42) It'll be so ubiquitous that everybody will have it, we'll use it in some way, but no one's going to be able to claim that we use AI and that's our competitive advantage somehow. >> So I think that no one else has a toaster. >> Yeah. No one else uses electricity. I don't know. There's lots of examples, I guess. >> Chris, how do you make sure that you're tying yourself to the right mast? >> Well, some of it is — I mention this in the book.
(07:09) There's some different studies you can look at on what drives long-term stock performance. And return on invested capital is a good northstar. Companies that can reinvest or earn high returns on capital over a very long period of time tend to be good investments. And so I think that's a key metric somewhere in there in your business that you're analyzing — is ultimately what's return on capital and their ability to continue to do that for a very long period of time.
(07:41) That's what's going to help create value. So businesses — and you can also look at it sort of inverted like Munger would do. So what things do we know probably don't work out well? Businesses where there's lots and lots of competition, it's going to be difficult. Businesses that are very heavily leveraged, that could be a problem next time there's some sort of financial crisis or something like that. Unscrupulous insiders. There's lots of things we can kind of knock out,
(08:10) so those are all clues and those are all things I talk about as well in the book, different ways you can kind of get at those ideas. >> What ideas carry over from 100 Baggers to the current book? What hasn't changed? I guess I'm saying, >> yeah, what hasn't changed? Well, another good question would be what has changed, too.
(08:34) But I'll say what hasn't changed. I think the core premise of just holding on to businesses that generate high returns on capital for a very long period of time — that's been a core, it's hard to get away from. And then which always plays into this also is the quality of the people involved in the business. So there are more examples I go through in this book where you have a particular entrepreneurial person who makes a difference. I remember there's one specific example I talk about, the New York Times and News
(09:12) Corp, and I think they both had the same market cap at one point and then like 30 years later I think the New York Times basically had the same market cap and News Corp was up 80x [laughter] and well, he had a very entrepreneurial person, whether you like him or dislike him, and did a lot of things and created a lot of value that way, and there are other examples of that too.
(09:37) So that's another thing that hasn't changed, because I also talk a lot about that in 100 Baggers about how some of the biggest winners are companies you can readily put a name to. If I say Walmart, you know about Sam Walton's story. Say Apple, you know about Steve Jobs and his story. Charles Schwab, you know about Charles Schwab.
(09:55) So there's a lot of businesses like that. What has changed? I guess that's the logical next question. >> Yeah. [laughter] >> Yeah. Well, what's changed is some things I didn't talk about much at all in 100 Baggers, I talk about more now. So let's say growing by acquisitions comes to mind.
(10:24) And I think I have a much more favorable view of companies that grow by acquisition than I did back then. And part of that is just more reading. There's a book, Deals from Hell, that summarizes a lot of good research on M&A and >> goes against the grain. Yeah. Goes against the grain of what most people think about M&A. The basic conclusion is that it's no worse off than anything else that companies invest their money in.
(10:56) And then personal experience. I've learned about a group of companies with people called Swedish serial acquirers, these companies like Lifco and Lagercrantz and Addtech, and these names I didn't hear about and knew nothing about when I wrote 100 Baggers. And those are companies that have proven to grow very well by acquisition, including something like Constellation Software, which again is something when 100 Baggers came out, maybe I was vaguely aware of, but otherwise didn't really know or appreciate. So those
(11:27) are a couple big ones for sure. >> Rise of intangibles in business as something that's changed maybe more so, and how to think through that. >> Yeah, that's certainly true. And there's a lot of research about that as well. More and more companies creating value by the intangibles, and the way accounting doesn't capture that value as much.
(11:56) So I don't know if that's changed so much because I do remember talking a little bit about that in the first 100 Baggers. So I don't know that I have anything incremental necessarily from that 10 years ago. But yeah. >> I think the M&A question is an interesting one because for a long time it was just common wisdom that you wanted to short the acquirer, buy the seller — that's the arbitrage — but then they tended to underperform.
(12:20) If they were sort of acquisitive, either because they were overpaying, you just have to pay more. And I think of something like Valeant as an example of that. The acquisitions get proportionately bigger. They get proportionately more expensive. Performance sort of disappears. They're heavily levered. They need probably a promotional CEO to work.
(12:38) But then something happened over the last 10 or so years where that's not really the case anymore, where the acquisitions seem to be maybe just this crop, maybe something has happened in the market, that the acquirers seem to have done very well. And it's come along at the same time as there's been this rise in these serial acquirers.
(12:57) Berkshire's always been an acquirer, but then Constellation Software sort of become more prominent in the investors' consciousness. In any case — and I agree, the Swedish, I knew nothing about Swedish acquirers maybe 5 years ago and they seem to be much more prominent now.
(13:14) What do you think? What's causing that? Is that phenomenon real? Am I making that up or is that >> I think some of it is that the large deals get all the attention, so we know the big deals and their spectacular failures are well documented and well known, and nobody writes about the little humdrum acquisitions that happen behind the scenes or are not in the front pages.
(13:38) Yeah, the little bolt-ons, and those are the ones — they're accessible. When you started talking about >> M&A is not successful. You mentioned a couple things right off the bat that the research will support as red flags in M&A. One is leverage. That's one. Size. When they're really big and they use leverage, odds are against you.
(13:57) But there's smaller bolt-ons. Lots of companies have been able to grow sustainably with good returns doing more programmatic smaller acquisitions. And you can think of there like Watsco and Roper of the world, and HEICO, however, 100 plus acquisitions in this time. So those companies have been enormously successful.
(14:21) But they're out of the limelight. They're not the big splashy acquisition that's getting all the press, and so those ones I think skew our perception of M&A. >> There's often an era-defining acquisition too. So in the 80s LBO boom it was >> RJR Nabisco >> RJR Nabisco got the big >> RJR Nabisco, of course that was a great >> and Time Warner in the dot-com boom. I don't know what it's been more recently. Is there — what's the big emblematic acquisition this time around? >> Maybe we haven't had it yet. >> It's not over yet.
(14:56) >> We haven't had it yet. >> Paramount's not yet, that's struggling a little bit >> regulatory reasons >> that's a good question, what's been the flashy M&A deal >> that everyone's going to point to and laugh at 10 years from now. >> Do you think it's companies staying private for longer and coming on the boards much bigger than they did in the past? I don't know if that makes much difference at all, but >> Mhm.
(15:25) >> Well, that's a real phenomenon, that the companies obviously, that IPOs are much more mature now and bigger than they were. How does that >> A lot of times it comes too from a >> it's a big incumbent who is desperate to change the narrative and feels like they're left behind and they need to grab on to something that looks like the life raft to the future.
(15:47) >> Yeah. >> Like when Time Warner bought AOL, right? That was like the top of the >> cycle. So what, it'll be somebody buying some AI related thing or chip maker right at the top? >> Basics buying Cursor, I don't know, there's — that one's probably not big enough, but >> the last one was probably something like one of the big private real estate transactions, right? The big leveraged real estate transaction. It just escapes me at the moment but it was Sam Zell selling, right, Sam Zell
(16:17) selling to the PE guys. Yeah. >> Maybe that's part of it, too. It's private equity. You don't see the >> there's not like the stock price a year later to show how much it blew up in your face yet. >> So maybe it's just still sitting on a private equity balance sheet today. >> Yeah. >> What are you looking for, Chris, in sort of general terms? You're like high return on invested capital >> and there's got to be some protection.
(16:51) There's some reason for that beyond sort of a cyclical. >> Yeah. I spend most of my time when I find something that I like like that, most of the time on competition and how they're able to defend that moat. So there's a lot of time spent on that and a lot of time spent on analyzing the incentives.
(17:15) >> Let me redirect the question. Where are you willing to compromise on things and where are you not willing to compromise? >> Yeah. So I'm certainly not willing to compromise on the character of the people. I'm not knowingly getting involved in anything where I think they're bad capital allocators or there's any question of their integrity, or taking advantage of minority shareholders or stuff like that. So steer clear of that.
(17:47) For me also the return on capital's important, but that can also be something where if it's something that's getting better. So there's like a look-through, you kind of look forward a little bit. It doesn't necessarily have to be something that's earning high returns right now — could be decent returns now and then there's some underlying scale or some other parts of the business that are going to improve over time that are pretty reliable.
(18:15) So that, and then for me I'm always scared of dealing with high leverage balance sheets because I've been burned by that in the past, and >> I can remember even during the '08 crisis, even if you had something that had an okay balance sheet became problematic in short amount of time.
(18:31) So I just prefer to sleep well at night and have great balance sheets, and when things get crazy, then I know my companies will be fine and maybe have the ability to take advantage and do something during those distress periods. So I would say those are kind of the big three for me. I know I haven't mentioned valuation yet.
(18:51) [laughter] >> Where's the compromise? >> Yeah. So obviously everything you run through, I run through some sort of analysis where I'm looking at what kind of IRR I expect, and that's got to make sense as well. So I think all four of those things are a pretty good little stool, if you will, [unclear] check off.
(19:15) >> How far out are you typically looking? >> Like five to 10. >> Okay. >> Yeah. Five is not that long, but it's long enough. If something works pretty well in five and then looks even better over 10. >> But you got to be careful because obviously we all play around with numbers and you can make something look really good over 10 years and >> gets anywhere. Exactly.
(19:39) Whatever you want to say, you get to say. So you be careful about that kind of stuff and what kind of multiples you assume, but yeah, I'd say five to 10 is what I'm looking at. >> Where does culture play over and above just management? >> Yeah, that's huge for me. And I write about that in the book, too.
(19:59) Different ways you can look at culture. For a long-term investor, I think that's really important. If you're holding a stock for a year or two or three, who cares? Culture may not matter so much, but over the long term. So what does that mean? Culture really, it's a hard term to define.
(20:19) It sort of involves the way a business does business, the way it treats people, the way it treats its employees. So you can look at things like employee tenure. You can look at things like are the employees shareholders as well. Do they have some culture of ownership there.
(20:38) How do they treat their suppliers and their other corporate relationships? And so there's good studies that show that long-lasting companies also have long-lasting relationships with their suppliers. So it's kind of like an ecosystem. >> Yeah. >> That's another sign of good culture.
(20:57) So, a team that promotes from within, has a lot of executives that have been around, worked their way up the business — that can be an indication of good culture, too. So we have all these little markers. You just have to sort of dig to find them out. It's not that easy. But I think over the long period of time those things can really matter.
(21:16) >> Is that the single uniting trait beyond return on invested capital and other things like that — culture? >> For me, I think it would be. Yeah, I think it's important for me. With all these things we talk about investing, there's exceptions to everything.
(21:33) But as a general rule, I love to kind of get into the culture of the business and what's it like to work there? >> How do you figure that out from the outside? >> It's very difficult. >> Yeah. >> Sunglasses and a job. >> Yeah, you really can't. That's what I mean.
(21:52) You have these little clues, little markers that we can look for. But I make use of the expert networks and talk to people who work there and you can get some sense of that, but even that you have to be sort of careful because those people don't work there anymore and sometimes there's a reason. So >> yeah, if the culture rejected them then that might be actually the inverse signal.
(22:12) >> The same thing when people go and look at Glassdoor and they see bad reviews or something, but they could just be disgruntled employees, very small sample size. So yeah, to your point, it's very difficult to know for sure. You just have little clues and markers that we can look for and pay attention for — obvious red flags, and I don't know what those would be like, lawsuits, workplace lawsuits, and bad behavior in certain ways that it manifests itself that we
(22:42) can see it, but otherwise hard to detect, but a nice to have. >> Buffett did a good job talking about the culture of Berkshire Hathaway while he was there. I think that's probably the best example that I've ever seen. >> I don't remember what exactly you said. What do you have in mind? >> Just from dealing honestly with people.
(23:13) Don't lose money for the firm. Don't lose a shred of reputation. Deal with people fairly. That's sort of — it's not something that they discussed a lot, but it's discussed a handful of times through the letters as I've gone through. I put it all together for my last book and I thought it was a pretty good cohesive argument for Berkshire and for other companies, and I sort of looked at that as being a good example of — you probably won't find anything as clear as that,
(23:39) but anybody else who's talking in those terms I thought would be doing a pretty good job. >> Right. And I remember Buffett saying things like he wouldn't do business with somebody he didn't trust no matter what the deal looked like.
(23:52) And I remember learning that lesson early on in my banking career, too. People are like, it doesn't matter how much collateral you have. Doesn't matter — if you don't trust them, forget it, because they'll find a way. There's always a way. And I remember one time I was a very young banker and I had a deal. I thought it was a no-brainer.
(24:06) It was cash secured with a CD. I was like, this is a no-brainer. And a wise old banker told me — I remember sitting down and talking to it — and this guy was not a very trustworthy character and he told me he wouldn't do it and I was just baffled, and he told me there's always a way, you can't think of it now, but if this person wants to screw you they will find a way, they'll take the CD, they'll cash it, they'll figure out a way, the teller will be there, they won't check, they won't see that
(24:36) it's tied to a loan or whatever, there'll be some way they can, and then you'll be out. So I always remember that because I was thinking it was pretty shocking at the time. I was like, wow, it was like a no-brainer. And he said no because he didn't trust the guy. And then of course there's that famous quote from JP Morgan as well, which said he wouldn't lend — what did he say? He wouldn't lend a dollar against all the collateral in Christendom for a man he didn't trust. Something like that.
(25:02) Same idea — that somebody you don't trust, they'll figure out a way to screw you one way or the other. So I think Buffett had that ethic as well. >> When you look at equity ownership, do you factor in stuff like share-based compensation? Do you look at how they earn their equity? >> Yes, absolutely.
(25:23) And the power of dilution. Again, this is important. If you're a long-term investor, this is very important. If you're just going to own a stock for a year, what do you care? Or a year or two even. What do you care if there's one or two percent dilution? If you're going to own something for 10 years, 1% dilution adds up quite a bit.
(25:40) 2% dilution is very significant. And some companies, of course, have way more dilution than that. >> 15% >> 15% common. >> Think about how much more you have to grow just to stay in place. There's a table in the book I have about that. Even at 2%, how much more growth does it require over say five years just to stay even? And there's some surprising numbers there.
(26:06) So yeah, that's why I love and admire these companies that have share counts that are unchanged over a long period of time. Constellation Software is an obvious one, but Lifco has the same number of shares as when it went public — Swedish serial acquirer — and then perhaps even better, the companies that slowly shrink it over time opportunistically.
(26:27) Those are pretty special. >> Yeah, I think that's some of — I don't know if Buffett talks about this. He talks about it, but not in these terms — that there's a lot of return to be had from buying a good company and then letting the company take out half of your other co-owners basically over a long period of time and just concentrating your share of it.
(26:53) >> Oh, yeah. It's the whole analysis on cannibals, and they can be great. I remember even from 100 Baggers, it was AutoZone, one of those that just — even though the business didn't really grow that much over that period of time, the stock was phenomenal because they were just gobbling up so many shares year after year after year, and wound up being very good.
(27:18) So yeah, capital — and I think that speaks more broadly just to the power of capital allocation. What does the management team do >> with the cash that the business earns >> and that low valuation is a gift over that whole time period, right? >> Yeah, you have a management team takes advantage of it. And then Buffett has that famous quote where he says a management team that earns 15% return on its equity over the next five years will determine how all the capital in the business has
(27:47) >> turn, exactly. Basically the next five years they'll invest the same amount of capital as the business has to that point in its history, which is remarkable, but mathematically true. And that speaks to how important it is, given how selective you have to be to find a potential 100 bagger, and your bias towards holding.
(28:14) >> Wait, I thought those grew on trees, Toby. What are you talking about? Everybody's got a whole portfolio full of them. [laughter] >> Potential 100 baggers. And given how you probably have to have a bias towards holding, otherwise there's lots of opportunities in any given year to sell.
(28:30) How do you finally make the decision to sell something? How traumatic is the sales process? What happens? >> No, I like that — dramatic — because I sometimes feel that way when I sell something, like, is this the right thing to do? Sometimes you feel good about it and then you're like, well, thesis is way off from where I started or something dramatic has happened and you got to cut loose.
(28:57) But I always say selling is like the hardest thing in investing. I don't know anybody who's really good at it. [laughter] And if you're buying good businesses generally, then what you're selling is eventually going to be worth more at some point. It's just a matter of what you do with the capital instead. >> Right. >> So [unclear] said when he was like 93,
(29:18) like, I'm still trying to figure out the right sale process. It's hard. [laughter] >> Very hard. >> I'll never know the right answer. >> That's right. The other thing is, you don't have to do this with all of your money. You could take some portion of your money and do this where you're going to leave it alone, be long-term, and try to just not trade a lot.
(29:42) And then you have that itch you have to scratch. You have some other smaller portion of your money that you allow yourself to trade more or whatever. That's another way to do it too. But we have to find some way to sort of fight this psychological urge to >> do something, chase, do something. That's the way it — yeah.
(30:01) >> Let me just give a quick shout out, and JT, you want to do some veggies? >> Yes sir. Valpareo, what's up? Tallahassee, Belleview, Toronto, Saratoga Springs, Sacramento, Boyisey, Breenidge, Servaton, Luzan, Switzerland. >> Jump >> Sounded out. >> Misil, Austria, Toronto, Tmacula, what's up? Snomish, Jamaica.
(30:28) >> Wow. >> Is SpaceX buying Tesla? Is that real? Has that just happened? Oh, that would be the defining — that was it. >> That's a great one. That is, if that happens. That would be great. That is it. >> Tampa, New London, Bologn, France, Vancouver. I don't know if that room is — I just read that off the — is everybody listening at home confused about what's happening? Glasgow, what's up? >> I think Britneyland. Good.
(31:04) All right, JT, take it away. >> All right, so before we get started, shout out to my friend Otto for sharing the white paper that inspired today's little segment. So everyone is in love with the idea of fine-tuning. Adding one more factor to the model, one more decimal place in the DCF, position sizing down to the basis point.
(31:28) It's science, right? And we have trillion parameter models that we're using and it's all very exact. Well, today I want to make the case for the opposite of that. What you might call coarse tuning. Coarse as in rough, not fine. And I want to start with a few animals that look like they're quite bad at their jobs.
(31:48) Now, Toby, do you have any thoughts about the great tit? >> Is it a bird? >> All right. Don't take the bait. Okay. Yeah, it's a bird. It's this little songbird. And in a good year, if you run the math on the food and the territory and how many chicks the parents could feed, the optimizer would say that the bird should probably produce 18 eggs.
(32:11) Yet she only lays about nine — half the chicks that could have been raised. Why is that? The next is a desert plant where many of its seeds stay in the ground and refuse to sprout even after the best rains in a decade. Every seed that stays dormant is a plant that never grows in the best year that it might ever see.
(32:28) Why is that? Songbirds will start migrating on the day length and not the weather, not the available food. They'll take to the skies on a warm autumn day with fruit still on the trees. Why is that? And then of course the cockroach, which should probably have its own segment at some point. I'll do that, I promise.
(32:46) Ignores everything it can see and smell and will always run from a harmless puff of air. Why is that? Why would evolution put up with such sloppiness? Millions of years of selection. Survivors seem like they aren't really all that dialed in. Like they're not very good at their jobs.
(33:03) I thought this was supposed to be survival of the fittest, right? So 1985, a business school professor named Rick Bookstaber and a mathematician named Joseph Langsam published a paper in the Journal of Theoretical Biology together and it was called On the Optimality of Coarse Behavior Rules. And a fine-tuned rule or behavior carefully adjusts to the cues that are in front of it — there's a new input, you change your response, you tweak it — and it's optimal inside the world that that animal can perceive at that time. The
(33:36) issue is that precision is based on the assumption that today's picture of the world is complete and final, like this is it, tuned to this. Bookstaber called that gap extended uncertainty, and this is events that in their words cannot even be delineated, much less assigned probabilities.
(33:55) So it's not a low probability event. It's not even on the list. Rumsfeld's unknown unknowns basically, right? And against that, the fine-tuned animal isn't just wrong. It's wrong in proportion to how well tuned they are to the old world. So coarse rules are very different. It's suboptimal in damn near every single environment, yet it's satisfactory across all of them.
(34:18) So the bird laying nine eggs isn't really bad at math necessarily. It's betting on a tough winter that it's never actually seen before. Or the seed that stays down is betting on a drought that's outside of the data set potentially. And so mother nature really knows how to hedge her bets and gives up some of the average to cut down on the variance, and across enough winters that smaller clutch actually compounds further than doing bigger clutches.
(34:43) So mother nature really is playing the long game here. So maybe said more eloquently, 18 eggs is the arithmetic hero and nine is the geometric survivor. So it's interesting — animals actually know instinctively when to hedge. So if you take one out of the wild and you put it in a lab, somewhere it's never been before obviously, its behavior actually gets more coarse.
(35:07) It'll be blunter responses, including it'll eat a broader diet in a lab because it's not optimizing. And the strange room that it's in is a signal to itself not to fine-tune. So a little bit more on Bookstaber. He actually went to Wall Street and funny enough he ran risk at Salomon through the LTCM crisis in 1998, and he wrote it up and he opens with a thought experiment which I think is quite interesting.
(35:36) Imagine you're the CEO and you're handed a perfect view of every risk that your firm is carrying or would be facing, and it's all of the ones that you've identified and even the ones that you haven't actually. So this is like you have a crystal ball, but there's a catch, one condition. You can't tell anyone what you saw.
(35:54) You can only build a structure that would try to survive. So what do you do differently then? And Bookstaber's answer was you spend less time investigating the risks you already know about. You simplify dramatically the models. You flatten the hierarchy. You shorten the reports. You don't over-optimize.
(36:13) Basically, you don't fine-tune, you coarse tune. You maybe be less of a laser focus on risk and more like 360 degree coarse radar coverage to look for incoming problems. So our world obviously is awash in fine-tuning. Every allocator has a 40-page risk report. AI will crank out 10,000 pages if you want to read them tomorrow.
(36:38) And all of it's tuned to the world as we currently see it, probably even just really the last 15 years if we're being honest, especially in finance. And every decimal of that precision is a bet that the world will still hold today as it looked previously. So Toby, maybe this is a good time to take a little break and talk about why EV/EBIT is such a useful coarse rule.
(37:03) >> Why I like it — it takes into account the enterprise value, takes into account the debt levels, the minority shareholders, the off-balance sheet liabilities. It's a good view of what you're actually paying, and operating income. EBIT; EBITDA doesn't really matter.
(37:30) Operating cash flow, good indication of what comes into the business. As a rough rule of thumb, it's a reasonable proxy for a price earnings metric and it's hard to game if you're doing those calculations yourself. It's imperfect. There are other things that you could do, but I like it as a rough cut. >> Good.
(37:53) So I did spend a little time daydreaming on five specific ways that we might coarse tune a little bit more, and I'll just share those and then I'll wrap it up. So first one is coarse measurement. Graham said the function of the margin of safety is to render unnecessary an accurate estimate of the future. It's almost impossible to know the value of anything precisely.
(38:12) It should always probably be a range. So operate so that you don't need to be exactly right. I personally try to leave out all decimal places in my note-taking and analysis. I don't want my brain to get subtle clues that there's more precision in what I'm actually doing than really exists. And this might be a good time, after this, Chris, to get into general semantics a little bit.
(38:39) So I usually try to use round numbers as much as I can because almost always that's good enough and as close as the accuracy as I can really expect of myself. Two, coarse sizing. In 2009 three finance professors tested 14 portfolio optimizers against the dumbest rule that there is, which is a simple equal weighting.
(38:59) Out of sample, not one of these fancy algos beat the equal weighting consistently. So for a 25 stock portfolio, the optimizer needed something like 3,000 months of data to earn its justified precision, which is like 250 years. You could effectively throw that in the garbage, right? So three, coarse — what you might call the migrating bird rule.
(39:24) Rebalance maybe on the calendar, not on some ever-changing macro signals. Review your rules once a year, not after every loss. Sometimes not immediately fighting the last war is important so that you don't overfit your model of the future. Four, coarse monitoring. Every blowup creates a one sentence afterword. Barings Bank, Long-Term Capital, Archegos, situational awareness — all these blowups, they don't require fancy Greek letters and explanations, almost always they can be explained in one sentence and usually it's leverage and
(39:56) hubris. But it behooves you then to perform a simple premortem, and if you can't say how a position dies in one simple sentence, the thing that actually is going to get you is probably still lurking off the page and you need to think about it more. And that monitoring also goes towards what Chris was saying about don't check stock prices every single second, because you're just introducing a lot of friction into your brain.
(40:23) Last one, coarse prompts. We're all in this rapidly changing laboratory environment right now of using AI. And two years ago, the best AI prompts were the most precise ones. We had to add "take your time and make no mistakes" to your prompt to get it to work, right? And adding "think step by step" took one model's math score from like 20% to 80%. Okay.
(40:47) So we all built these little libraries of tricks and incantations that we were dorking around with, and that was tuned to the model of that year, right? And now all these models have gotten better and OpenAI's own guidance says keep it simple and direct and stop telling it to think step by step. So a precise prompt is a bet that today's model will hold, but they're changing all the time.
(41:06) So be more coarse perhaps. So what's really survived every generation is a brief — like what you'd give a new analyst if you were asking them the question: what do you want, why do you want it, and what does a good answer look like. So tune to the task maybe less so than the tool, because the best tool is changing every week.
(41:27) So now of course there is a premium to be paid for all this coarse tuning. There is no free lunch. It underperforms in every year where the world still holds, and it does hold most years, right? So the fine-tuner collects that premium every year until the year the picture isn't complete and then it tends to hand it all back at once — which, you think about the turkey on Thanksgiving morning.
(41:49) They thought things were going well for a long time and then all of a sudden they weren't. So to wrap this all up, we humans, we love to fine-tune, but mother nature very rarely fine-tunes and the survivors typically don't do that. Can't really blame us. We're still a very young species. We're still figuring things out.
(42:06) We fell in love with this fine-tuning because precision like that feels rigorous, but it isn't really. And the cost of our coarse tuning is quite visible — like, oh, we're not updating the model to the new world; you knowingly leave some performance on the table when the model is increasingly right, of course — but the benefits of coarse tuning are invisible.
(42:26) And until the model is suddenly wrong in a way that you never specified before, that's when you get your payback. And then you totally redeem yourself, to do a little pun from Dumb and Dumber. So anyway, there's the veggies on coarse tuning for today. >> Good one, JT. Yeah.
(42:45) Chris, JT mentioned the general semantics. What is general semantics and what can we learn from it? How can we use it? Easy. >> Yeah. Easy question, right? >> Yeah. >> Let's see. Let's preface this a little bit with that Chris wrote a really interesting book that I read not too long ago called Dear Fellow Time-Binders, and it's all about general semantics and is a really great introductory exploration of the concepts that are otherwise actually quite hard to
(43:18) penetrate on your own without Chris holding your hand through the whole thing. >> Oh, thank you. Yeah, that was the intent of the book actually, just to be kind of an introduction to those ideas, because Alfred Korzybski was a Polish engineer type who came up with this idea in the 1930s and wrote a big fat 800-page book about it with lots of footnotes and math, and so yeah it can be very difficult to get to, but the basic ideas are very valuable and they're geared around how we use language and how that
(43:52) influences how we think about certain things. So you mentioned for example your decimal point example — that's very Korzybskian. He would have loved that. Because we do have this false precision when we say something has a P/E of 25.2 or whatever it is, whatever number it is.
(44:11) Of course it would be just as well served by just knowing that it's sort of a guess. We'd probably be better off if we didn't know the number exactly. We just do a range. [laughter] So that's the basic thought of it, and there's lots of examples and little tools he gives you a way to check that out. >> Maybe the subscript of the date would be an interesting one to just talk.
(44:29) >> I was just thinking that as one example. Yeah, the date one is an interesting one because let's just say, in the context of our business, you're looking at Berkshire Hathaway and you looked at it last year and you came to certain conclusions.
(44:44) What Korzybski would say is that you actually put the date in there as a little subscript. So Berkshire Hathaway 2025, to denote your thinking on it was from last year. But if you don't actually put the date there, at least mentally you know that the last time you looked at it was 2025.
(45:01) So that's your thought, and not to get anchored onto that thinking, because there's lots of new things that may have happened, and so it prevents you from getting attached to your ideas that way. Using a date makes you recognize that things change and then you need to look at it again. And that's one very very simple tool, and he has a number of tools like that that I use all the time and I think are very helpful.
(45:27) >> The only Korzybski quote that I know off the top of my head is the map is not the territory. That's Korzybski, isn't it? >> Yeah. >> Yeah. He popularized that term, map is not the territory. And that's another good way to sum up general semantics. So the map of course is our verbal descriptions of things, which is not exactly matched up with the territory we're describing.
(45:50) So his whole toolbox of general semantics is to kind of get behind and parse what people's descriptions are using the language, and to try to get at what's really being described, what those descriptions hide, what they maybe say about the person giving the description, so on and so forth.
(46:11) So it can be interesting and applicable to lots of things in life, of course, not just investing. >> One thing I really liked about it was that it really gives you an even deeper appreciation for that slippage between your map and the territory, whatever that delta is — you just should be a lot more humble and have humility about how much you think you really understand something, because it's quite difficult to fully truly understand anything, maybe impossible. >> Said better than myself. Exactly. Yeah.
(46:42) I think that's one of the benefits of studying it, and it gives you a greater dose of humility about your own ideas and conceptions and being more open to being proven wrong. So those are valuable in themselves. >> So the idea is just to retain the idea that what you're looking at is a model rather than the thing itself, and that your model could be wrong in various different ways and you should be open to correcting.
(47:09) >> Yeah. Or like Jake said, not only that it could be wrong, but you're probably wrong. It's just how badly you're off. >> Yeah. >> No matter how good of an artist you are, you're still not going to be able to capture the thing exactly as it appears. >> So there's always things that you leave out and the question is how important are those things and how they change over time. So, yeah.
(47:37) Well, >> sort of an aid to critical thinking you can think of too, is another way to think of it. >> How does that manifest in your process? Do you keep smaller position sizes, or what is the practical implementation? >> I'm dying to see your notes now for a typical, if you went through a 10-K or something.
(47:55) >> Yeah. One thing is you certainly — there's lots of ways. So one other way I would say is you don't get too attached to labels. What other people say things are. >> Compounder. >> That's a good one. Compounder or value stock or whatever. I can think of even more conceptually what people would argue about — what is Tesla? Is it an auto manufacturer or is it a battery company or is it what? What people say, and how you frame it, how
(48:28) you describe it really greatly influences how you price it and how you think about it in your mind. So there's a certain amount of getting past those things. >> I would love to hear the answer to that question, by the way, [laughter] because I don't know what the answer to that question. We're >> still waiting to find out.
(48:43) >> Yeah. No, I just pose the questions. I don't answer them. [laughter] >> Exactly. >> Well, moving on from that a little bit, but from a practical perspective, how do you think about position sizing? >> For me — again, there's lots of ways to do this, so I'm not saying always the right way — but for me, I like to kind of start things small and get to know the business.
(49:11) And I always say it's different when you actually own something versus just following it. I don't know why. I just somehow when you actually own something, you're just much more in tune. You're paying more attention. You work on it more because you actually own it, as opposed to something that you're following and saying you're doing work on it. It's just different.
(49:28) It's a different level. So I feel like you know more about it when you've owned something for a year. And I like to keep it small because that stretch of time there is probably where you'll make a mistake, is probably early. And you might learn that a business isn't quite as good as you thought a year in.
(49:48) It'll be easier to get out of if it's a smaller position. It won't hurt as much and all that kind of stuff, whereas if you started really big, then just stress level and everything is much higher and it's harder to pull out. So this way, you start small, you kind of grow into it. And the way I also say with these things — if it's real, you've got plenty of time.
(50:10) So you shouldn't feel like you've got to be rushed into something. If it's a really good business that you can own for 10 years, you could probably buy at the 52 week high this year, next year, the year after, and still do very, very well. Not that you want to aim to do that, but it'll probably work. So, >> that's a really important point to highlight when you're in year 15 of a bull market, and it's always felt like you didn't buy enough fast enough. I don't know, maybe not enough older people watching the show, but when you bought too
(50:40) early in 2007-8 and it just kept going down and down and then eventually you're like, I don't have any more money to put to work. That was the opposite feeling. It was a bad feeling but in the opposite direction, and it just hasn't been that way in so long. There's probably an entire generation that hasn't experienced that.
(50:58) >> That's right. And then if you start small and that happens to you, then it's much more — first off it's less painful, but then you can more readily add to it as you're going down. And so I feel like you get a lot more flexibility starting small. And then whatever a full position is for you — for me, I don't know, somewhere around 7, 8% I think — and then after that I will just kind of let it ride. I like to just sort of let the portfolio get unruly at that
(51:29) point. Something compounds and takes over and becomes 12, 13% of the portfolio — that's good, that's great, earned. [laughter] >> Yeah, I don't feel like I have to trim it or I have to manage it so carefully, but everyone's different about that. So that's generally how I think about it. >> Is there a max size where you would be like, okay, just for prudence, I need to trim this back?
(51:50) >> Yeah, definitely. Kind of always debate what that is, but I know from my fund doc, it's legal at 25%. I definitely have to cut it at that point. [laughter] >> Okay, there you go. >> But I don't know that I would take it that far. It might be a little before that. But yeah, there's a point I think, and again that number is probably different for everybody depending what situation you're in and what the rest of the portfolio looks like.
(52:13) >> It's hard though when you read Bessembinder's 4% study and you're like, okay, well, I'm just going to kill this one thing that's probably going to — imagine it's 1972 and you have Berkshire and it turns into — it's the lady who you sat next to on the airplane, and if she had been trimming that whole time, she probably cut her result by huge orders of magnitude.
(52:35) >> Huge. Yeah. And I remember there's an example with T. Rowe Price's fund where they had bought Walmart early on and it was in a small cap fund. [laughter] >> So they were constantly cutting it back and cutting. Yeah. And it was like if they had left that, it was worth more than the whole AUM of the fund today or whatever.
(52:59) So obviously those are huge huge mistakes. I love Bessembinder's studies. I love how they make you think about things like that. But that's the cost of investing. I think for most individuals, if they had to keep chopping back when something that was huge like that — they're still going to do very, very well.
(53:17) Maybe they wouldn't do as well if they left it alone. And then every once in a while, you're going to have something where it didn't work out or it turned out to be Polaroid and went to zero eventually or something like that. >> Right. >> And then you're glad that you at least took some off. So you always have to be vigilant about this stuff.
(53:33) And owning stuff for a long time doesn't mean you just ignore it completely. But it's not easy. Otherwise, a lot more people would do it. >> Yeah, power laws are hard to handle. >> Really hard. >> The corollary to the buying too early during 2008 was probably 2020, 2021 when the market went up very rapidly and a lot of these expensive names become very very expensive, and there was this meme that was doing the rounds on Twitter at least, the never — you want to be in club never. And around that time I did some research where I just said let's just take the
(54:05) cheapest free cash flow names from 1999, 2000, and then I did that for 10 years until like 2010, and then just hold them and never rebalance those portfolios. This is all back test of course. >> And then at the end of that period of time, you find that the portfolios start looking like — all the biggest names of course are the things that have done the best, that everybody agrees are the best stocks to hold.
(54:29) And so your portfolio becomes dominated by the big good things. But at the beginning of the period of time would have been very difficult to predict, >> and you look like a genius like, oh, this guy saw early. Yeah. >> I had it for 25 years. I knew 25 years ago that Steve Jobs was gonna come back [laughter]
(54:49) >> I knew Amazon wasn't going to just sell books [laughter] >> down 95%. That's a great example. You buy that down 95%. >> So being a collector of just snapshotting and then letting it roll forward, and whatever you get you get. >> Well, there's an error rate in your buying, there's an error rate in your selling.
(55:10) And you look at something like the S&P 500 committee famously underperforms — if the S&P 500 committee didn't exist and you just bought whatever the largest 500 were, you admitted things into the portfolio when they should. They've kept things out on occasion, they have some discretion. I think Tesla might have been kept out for a year or so >> is that they've reversed that decision with SpaceX and that's worked out spectacularly.
(55:34) >> [laughter] >> But it's a — I just think it's an error minimization. I don't do it, but I think it's an interesting idea that the never sell would be very very hard to do. But >> if you could persuade someone that it would work out in 25 years, >> but easy to market.
(55:51) Is that what you're saying? >> I think it'd be hard to market, but >> you think so. You get to look like a genius, >> but 25 years hence. You don't know if it's worked out. >> Yeah. >> Yeah. You should have started a while ago. Yeah. [laughter] >> How do you justify fees? Yeah, we're just going to do nothing. >> Well, to be fair, you get — because you're buying on a free cash flow basis and you're buying things that have a fat free cash flow yield, you get about a third of the capital back over the first five years. So you
(56:19) do have to reinvest. And I was just >> just saying you just do the same thing. You just buy the cheapest and then you never sell. And these are names that all have real problems. They're cheap because they're not doing very well. They're cheap on a free cash flow basis. So they're still pretty good businesses producing free cash flow, but there's no selectivity in buying them.
(56:41) You're not buying them because they're good businesses. They're pretty bad and something has to change for them to become good businesses. >> Yeah. You're making a call on terminal value basically, >> right? Or just playing a statistical game that at least some of them in there are going to work out. Once in a while you're going to find, I don't know, a Philip Morris that's trading and just continues to compound and do well.
(57:06) So yeah, there's always >> you get plenty of zeros, too. >> People say you're an idiot for holding that all the way to zero. >> Yeah. >> But the portfolio does pretty well and then it ends up like you're concentrated because you haven't changed anything in the biggest names. >> Yeah. That reminds me of the coffee can portfolio and other ideas where you're holding on and just tolerating that some of them are going to go to zero and suck but >> impossible >> statistically there's going to be
(57:34) something in there that makes the whole thing work. It's the interesting math behind investing, that the winners really make a huge difference. >> They can be the difference between beating the market or not. It's that one giant that takes over. >> That was true of Claude Shannon. Claude Shannon famously had that.
(57:52) He was the guy who invented the binary. >> Yeah. >> He had this portfolio of — he had a whole lot of VC investing that he had done through MIT or something like that, or Bell Labs, wherever that was based. And he ended up with Motorola and a few of these other absolute monsters. But he just didn't ever sell a share.
(58:11) And that was the secret, >> right? >> Yep. Yeah. And that was the coffee can thing Robert Kirby writes about, when he was managing this woman's account, and for however many years it went on, 10 years, 20 years, and then he finds out when the husband dies and he gets his account — his wife gives him the account to manage — he finds out that the husband has been piggybacking on all his ideas but never sold any.
(58:36) >> Yeah. [laughter] >> And so yeah, there was one stock worth more than all the money he was managing for the wife, which happened to be, I don't know, Polaroid or whatever it was, but some mega winner. And so exactly — there were a whole bunch that went to zero, but the winners more than made up for it.
(58:50) >> Hey, Chris, we're coming up on time. Tell us a little bit about the book and where folks can find it. >> Yeah. So the book, The Investor's Odyssey — what's the phrase, available where bookstores, or whatever. So Amazon is probably your best bet. You can get it there. And yeah, let me know what you think.
(59:11) Just came out. So >> I just bought Dear Fellow Time-Binders while we were on this call. So >> Okay. >> I'll get to that afterwards. >> It's rare where you get kind of new big ideas these days. And so to find one like that, I was very appreciative. >> And if someone wants to follow along with what you're doing or get in contact, what's the best way of doing that? >> Well, I'm not really on X very much anymore, but occasionally I'll put things on there about books and things like that.
(59:43) That's probably the best. Yeah. >> JT, any final words? >> No, just happy to have Chris on and good to catch up and >> yeah, >> looking forward to reading The Investor's Odyssey. >> Cool. Thank you. Thanks for having me on. Good fun conversation. >> Thanks, Chris.
(1:00:05) We'll be back next week. Same bat time, same bat channel. See you folks.