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Speaker 1: This is Mesters in Business with very Results on Blueberg Radio.

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Speaker 1: This week on the podcast, I have an extra special guest,

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Speaker 1: returning Champion, Michael Mobison. He is on the buy side

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Speaker 1: people thinking. When I say that Michael is at Counterpoint

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Speaker 1: Global at Morgan Stanley Investment Management, it's not Morgan Stanley,

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Speaker 1: the big seal side broker dealer. It's a totally different division,

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Speaker 1: their asset management division. We really spend a lot of

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Speaker 1: time talking about UH the new edition of his book,

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Speaker 1: Expectations Investing. What I love about Michael is how thoughtful

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Speaker 1: he is, how interesting his approach to investing, thinking about markets,

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Speaker 1: individual companies value UH and basically the approach he brings

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Speaker 1: to research to the investment community. He's been writing and

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Speaker 1: publishing about markets for literally decades, not just his many books,

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Speaker 1: which I'll include in my notes, but the research papers

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Speaker 1: he puts out and shares with the public. They're always

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Speaker 1: interesting and thoughtful, and I frequently find myself looking at these,

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Speaker 1: reading these and going huh cock in my head, I

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Speaker 1: hadn't thought about that. That's a really interesting take on

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Speaker 1: a very fundamental idea, and I'm I'm glad I had

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Speaker 1: the opportunity to give this a thought. I found this

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Speaker 1: to be a master class in valuation and how to

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Speaker 1: think about what a company's proper potential value is, what

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Speaker 1: your expectation for investing in that company should be. Uh,

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Speaker 1: it's no surprise he's been teaching at Columbia for I

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Speaker 1: don't know twenty years as an adjunct professor in the

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Speaker 1: Business School. I'm gonna stop babbling and say, with no

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Speaker 1: further ado, my conversation with Morgan Stanley's Michael Mobison. This

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Speaker 1: is Mesters in Business with very Results on Bloomberg Radio.

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Speaker 1: My very extra special guest this week is Michael Mobisan.

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Speaker 1: He is the head of Concealing It Research at Counterpoint Global,

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Speaker 1: which is part of Morgan Stanley's investment management group. It's

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Speaker 1: the bye side part, not the sell side part of

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Speaker 1: Morgan Stanley. Previously, he was head of Global financial Strategies

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Speaker 1: at Credit Swiss and before that, chief investment strategist at

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Speaker 1: leg Mason Capital Management, working with the famed investor Bill

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Speaker 1: Miller during his incredible streak. He is also a professor

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Speaker 1: of finance at Columbia Business School and Chairman of the

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Speaker 1: Board of Trustees at the Santa Fe Institute. Michael Mobisan,

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Speaker 1: Welcome back to Bloomberg. Awesome to be with you, Barry.

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Speaker 1: Always a blast, and it's so good to be with you.

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Speaker 1: You know, I don't do this. I don't throw my

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Speaker 1: arms up when I'm doing it at home remote using

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Speaker 1: a Comrex machine to jack into the Bloomberg machinery. It's

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Speaker 1: nice to be in the studio and look at you.

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Speaker 1: I I have four hours worth the questions for you,

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Speaker 1: so we'll we'll see if we can get through some

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Speaker 1: of them. But let's start talking about your most recent book,

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Speaker 1: which I hold in my hands, Expectations investing, reading stock

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Speaker 1: prices for better returns. So you first wrote this twenty

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Speaker 1: years ago with your co author is Alfred Rappaport. What

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Speaker 1: made you decide to um revisit this and reissue this

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Speaker 1: twenty something years later? Yeah? Why should first tell you

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Speaker 1: a little bit about the background of the first book?

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Speaker 1: And and I was, you know, I was a someone

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Speaker 1: had no business experience, coming onto Wall Street and read

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Speaker 1: a copy of Owl's book Creating Shoulder Value in the

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Speaker 1: late nineteen eighties, and it was for me a professional epiphany.

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Speaker 1: And I say that because it really everything I've done

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Speaker 1: professionally has come and rift off of this, so it's

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Speaker 1: really standing on the shoulder of giants. And so al

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Speaker 1: Rapaport has been an incredible mentor and collaborator for me.

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Speaker 1: So that book, by the way, it was aimed at

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Speaker 1: corporate executives about building value. And there were three lessons

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Speaker 1: in there that I always have taken with me. One

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Speaker 1: is it's about cash flows and not accounting numbers, you know.

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Speaker 1: And it's funny, Barry that we seem to relearn this

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Speaker 1: lesson from time and time, but we'll probably get into

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Speaker 1: it in some degree. But I think today we're at

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Speaker 1: a particularly interesting time and markets where because of the

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Speaker 1: way the accounting works and because of the way the

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Speaker 1: economy is moving, uh, you have to really follow the

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Speaker 1: cash trail more than ever. The second thing is he said,

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Speaker 1: you know, to do intelligent valuation work, you have to

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Speaker 1: understand both finance and strategy. And I think this is

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Speaker 1: something else people feel very free throwing multiples around and

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Speaker 1: so on and so forth without really doing the proper

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Speaker 1: strategy analysis. And I think that was a second lesson.

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Speaker 1: And then the third thing, this is my build up

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Speaker 1: to your answer your question. The third thing was the

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Speaker 1: original book catch chapter seven was called stock market signals

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Speaker 1: to managers, and the argument was, hey, executive, if you

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Speaker 1: want to understand how to allocate capital judiciously, even set

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Speaker 1: up incentives, you need to understand what the markets pricing

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Speaker 1: into your stock. So that obviously has direct implications for investors.

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Speaker 1: And that ended up, you know, we end up collaborating

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Speaker 1: on the original version aimed at investors called Expectations Investing.

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Speaker 1: Now I'm glad you're sitting down because the original version

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Speaker 1: came out September two thousand and one, like literally twenty

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Speaker 1: years ago, the day before the Greatest that the greatest

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Speaker 1: not natural tragedy, and by the way, much less significantly

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Speaker 1: in the midst of a three bear three year bear market. Right.

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Speaker 1: So um, And by the way, I thought, you know,

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Speaker 1: many of the ideas and the books stood up over time.

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Speaker 1: But I had been teaching from this these principles for

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Speaker 1: a long time and so you know, and part of

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Speaker 1: was COVID induced, but I was, you know, and I'm

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Speaker 1: in constant conversation with with Al, and I said to him,

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Speaker 1: you know, there's enough that's gone on in the world,

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Speaker 1: uh that I think we it's time for us to

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Speaker 1: take another swing at this, which is bring in what

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Speaker 1: is new, freshen up our case studies and uh like,

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Speaker 1: let's focus on what worked still and what's still is good,

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Speaker 1: but let's bring it in and make it more contemporary.

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Speaker 1: So I just thought it was and and and and

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Speaker 1: and in teaching it, what lessons have I learned? And

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Speaker 1: how to communicate some of the ideas effectively, what matters

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Speaker 1: and what. So we ended up only getting rid of

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Speaker 1: one chapter, replacing one chapter altogether. But there's so much

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Speaker 1: about it. So the bones are basically the same, but

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Speaker 1: there's so much about it that's brand new in terms

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Speaker 1: of the case studies and again reflecting how the world

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Speaker 1: has changed in the last twenty years. And for people

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Speaker 1: who may not be familiar with Alfred Rappaport, he's a

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Speaker 1: professor professor at Kellogg and and he's done a ton

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Speaker 1: of work on how to create long term value and

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Speaker 1: overcome short term is um amongst investors and corporate managers.

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Speaker 1: Tell us a little bit about that background. Yeah, I

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Speaker 1: mean that's it. I mean, look, I think that he

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Speaker 1: um is gonna he is one of the great sort

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Speaker 1: of academics, uh in this whole area in the last

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Speaker 1: half century. Um. His PhD is in accounting, by the way,

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Speaker 1: I should mention, but he's made very important contra usians

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Speaker 1: in accounting, in finance, and as I mentioned, was one

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Speaker 1: of the great synthesizers of bringing together strategy and finance.

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Speaker 1: He by the way, did a lot of the executive

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Speaker 1: programs a Kellogg and had a literally a world famous

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Speaker 1: program called Merger Week. And if you were anybody in

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Speaker 1: the mergers and acquisitions world, in the corporate world in

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Speaker 1: the eighties and nineties, you the mecca was to go

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Speaker 1: to Kellogg to do Merger Week and learn about this.

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Speaker 1: And so yeah, I mean, he's just a tremendous a

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Speaker 1: tremendous resource. Now he's in he is in his late eighties. Now,

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Speaker 1: he by the way, could not be better. I mean

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Speaker 1: I talked to him almost every single day and sharpened

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Speaker 1: and you know, just intellectually, so curious, a very avid reader.

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Speaker 1: I mean, so really going, still going, really really strong.

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Speaker 1: But um, and it's by the way, total delight. Is

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Speaker 1: always total delight working with him, in part for for

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Speaker 1: for the joint learning that goes on, but also to

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Speaker 1: have someone to be a reality check when you're sort

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Speaker 1: of going off a little bit and need a little

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Speaker 1: of course, correction. He's the old professor that sort of

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Speaker 1: brings you back into line, which is powerful. That's great.

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Speaker 1: Everybody needs a Jimney Cricket on their shoulder to make

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Speaker 1: sure they're not, you know, they're not going off to

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Speaker 1: never Neverland. So there are a bunch of quotes from

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Speaker 1: the book I want to get into, but I have

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Speaker 1: to start with one that that's so macro and all encompassing.

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Speaker 1: It really talks to the theme of the book, which

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Speaker 1: is when investors talk about expectations, they're usually talking about

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Speaker 1: the wrong expectations. Explain, well, I think that, Um, it's

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Speaker 1: interesting that when people talk about what a stock is worth,

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Speaker 1: and you should you should correct me if you hear

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Speaker 1: something differently, it's almost always some multiple of some sort

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Speaker 1: of earnings metric PE to say, the very least and

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Speaker 1: then a million variations r P E or e VD

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Speaker 1: or something like that. Right, And so the basic argument is,

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Speaker 1: at um, multiples are shorthand for what is a broader

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Speaker 1: valuation process, and multiples embedding them lots of assumptions about

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Speaker 1: how the world works, including assumptions about returns on capital

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Speaker 1: and growth and so forth. And then as we'll talk

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Speaker 1: about and probably more detail. Measures like earnings or even

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Speaker 1: even daw themselves um can be very unreliable measures or

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Speaker 1: metrics of value creation. So so when they say, oh,

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Speaker 1: at at X multiple, that it looks like low expectations,

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Speaker 1: or it's cheap in quotation marks, or it's expensive in

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Speaker 1: quotation marks, they're really they really don't know exactly. They

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Speaker 1: haven't unpacked exactly what the story is. Whereas I think

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Speaker 1: we'd all agree a lease in theory that the value

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Speaker 1: of the business is the present value the cash flows.

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Speaker 1: The argument I always like to make is, let's let's

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Speaker 1: lay the lay bare on the table what those assumptions are,

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Speaker 1: what the underlying economic assumptions are, and then debate them. Right,

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Speaker 1: we may not agree or come down in the same

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Speaker 1: place on these things, but at least we note we're

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Speaker 1: dealing with versus uh, these these these using putting together

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Speaker 1: a multiple, these two things that are neither which on

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Speaker 1: their own are very helpful. So so let me take

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Speaker 1: a swing at this and you tell me if I'm

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Speaker 1: on the money or or off. So when we look

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Speaker 1: at earnings, a lot of different things go into making

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Speaker 1: earnings high or low, cheaper, expensive, And if you're just

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Speaker 1: looking at that net number, you're blind to all the

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Speaker 1: moving parts that might be temporary one offs or potentially

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Speaker 1: reflect fast future growth. Just looking at the number itself

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Speaker 1: relative to price doesn't give you a complete picture. Is

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Speaker 1: that a fair? That? That for sure is true? And

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Speaker 1: then there's another component of it that's probably even more compelling,

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Speaker 1: which is growing earnings is not synonymous with growing value.

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Speaker 1: So saying that differently, if you invest in your business

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Speaker 1: and your earning exactly your cost to capital, growth doesn't

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Speaker 1: make any doesn't add any value whatsoever. And so uh

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Speaker 1: so so so earnings growth be good, it can be bad,

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Speaker 1: it can be indifferent, and so you just don't know that.

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Speaker 1: So saying this differently, company A and company be same

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Speaker 1: earnings growth rates, but the very different value creation prospects

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Speaker 1: you can't tell. You know, the multiples are going to

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Speaker 1: be different, and they should be different, but the earning

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Speaker 1: the earning number in and of itself doesn't tell you

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Speaker 1: the answer. And then the other question that comes out

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Speaker 1: of this that I felt was really fascinating, what sort

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Speaker 1: of expectations should investors have from reading stock prices? And

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Speaker 1: how can the ordinary investor read stock prices? Yeah? So

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Speaker 1: this I mean this gets to the heart of it.

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Speaker 1: And you know, Barry, I was trying to do a

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Speaker 1: little bit even and working on this book, I was

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Speaker 1: trying to get a little bit of the psychology of

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Speaker 1: this because it seems fascinating to me that people love

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Speaker 1: to think that they can figure out the value and

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Speaker 1: then they're going to compare the value to the price. Right,

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Speaker 1: So there they feel like their job is to figure

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Speaker 1: out why identifying intrinsic value sided discounts premium and what

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Speaker 1: this The premise of this book is obviously just to

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Speaker 1: reverse the whole process and just say, like, there's only

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Speaker 1: one thing we know for sure, and that's the stock price. Right. Again,

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Speaker 1: you may not may like it or not, like it

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Speaker 1: doesn't matter. It is something we know for sure. It's

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Speaker 1: an objective number versus what's essentially an opinion. So then

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Speaker 1: we say we're going to reverse engineer what has to

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Speaker 1: happen for that thing to make sense, and then try

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Speaker 1: to judge whether that is sensible. Now that to me,

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Speaker 1: the most powerful and vivid metaphor is the handicapper. So

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Speaker 1: you go off to the horse races to figure out

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Speaker 1: and and presuming you'd like to make money, there are

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Speaker 1: two things that are really important, right. One is the

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Speaker 1: odds on the toe board, which is telling you which

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Speaker 1: horse or horses are likely to win. The second is

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Speaker 1: how fast the horse is going to run, right, And

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Speaker 1: so it's the combination of these two things that it's important.

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Speaker 1: But often starting with the toe boards a very sensible

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Speaker 1: thing because you say, all right, well, you know, is

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Speaker 1: this a favorite or is this a long shot whatever

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Speaker 1: whatever it is. So this is basically saying, let's do

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Speaker 1: these things backwards. And by the way, there's a there's

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Speaker 1: a fascinating guy who you by the way, you should

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Speaker 1: get him on sometime. He's really interesting and a kind

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Speaker 1: of Stephen Christ. Do you know Steve? Do you know

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Speaker 1: Stephen Christ? And the way he's a he's an interesting guy,

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Speaker 1: um he and he's written a lot. By the way.

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Speaker 1: He wrote a chapter in a book called It's called

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Speaker 1: kristin Value is a thirteen page chapter which is one

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Speaker 1: of the best chapters about investing I've ever read, even

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Speaker 1: though it's about handicapping and Chris sort of talks about

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Speaker 1: you know, it's it's about this miss pricing between odds

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Speaker 1: and and and performance and so forth. But one of

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Speaker 1: the one of the lines in there he loves. He

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Speaker 1: says that I love. He says, you know, everybody thinks

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Speaker 1: that they're doing this thing, but very few people actually do.

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Speaker 1: And I think that's the same thing for for investing.

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Speaker 1: So when when you explain expectations investing the basic principle,

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Speaker 1: people go, yeah, well, it's kind of what I'm doing,

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Speaker 1: and isn't it And and the answer is that's known

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Speaker 1: and not really what you're doing. Right. So so there

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Speaker 1: are really three steps in the process, which you'll probably

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Speaker 1: get to you. But one is to say what has

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Speaker 1: to happen for today's stock price to make sense, right,

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Speaker 1: So that's just basically where is the bar set? And

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Speaker 1: then step two is introducing you know, sort of more

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Speaker 1: high falutin strategic and financial now to determine whether company's

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Speaker 1: gonna outperform, that expectations underperform, or g it's going to

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Speaker 1: be I have no strong opinion one or another. And

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Speaker 1: then step three is to make by cell and hold

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Speaker 1: uh uh decisions as a consequence. So in the book

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Speaker 1: you we're going to talk about earnings now, but I

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Speaker 1: have to bring up the broad changes you identify in

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Speaker 1: the market, and and we're going to circle back to

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Speaker 1: these but I want to reference these relative to earnings.

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Speaker 1: The shift from active to passive, the rise of intangibles,

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Speaker 1: the move from public investing to private investing, and then

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Speaker 1: the major changes in accounting rules. All four of those

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Speaker 1: things have changed since the first version of this book.

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Speaker 1: How significant are they to how earnings are accounted for

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Speaker 1: and valued by investors? So, I mean, maybe we can

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Speaker 1: part these a little bit because I think the active

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Speaker 1: too passive thing would affect you know, if it affects anything,

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Speaker 1: would be things like markets more or less efficient and

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Speaker 1: things like that. So I don't know that that's a

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Speaker 1: big thing for what we're talking about in terms of earnings.

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Speaker 1: And the other one is public to private And you know,

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Speaker 1: even since we wrote this book there I think there

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Speaker 1: are a third fewer public companies than there were twenty

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Speaker 1: years ago. But on that point, keep in minds someone

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Speaker 1: there's been some studies done and a huge swath of

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Speaker 1: those were penny stocks that got kicked off the nas

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Speaker 1: deck and onto the paint and nobody really cares about them. Yeah,

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Speaker 1: that's that is absolutely correct, and so but the nature

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Speaker 1: of these companies have changed, it in the market caps

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Speaker 1: and so forth. But again that I'm not sure that's

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Speaker 1: a big story for earnings. I think the one that

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Speaker 1: I mean accounting changes, but I think the really big

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Speaker 1: one is this rise of intangibles that you pointed out,

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Speaker 1: And just to give well, first of all, let's go

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Speaker 1: back a little bit even further in history. Back in

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Speaker 1: the nineteen seventies, tangible investment, so tangible physical things, think

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Speaker 1: tractor factories and truck machines, drills, all that, right, those

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Speaker 1: were twice the level of intangible investments and intangible by

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Speaker 1: definition non physical, so think now branding, software, code, all

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Speaker 1: these kinds. Okay, right, So two to one tangible to intangible.

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Speaker 1: At the time we wrote the book so called two

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Speaker 1: thousand and one, twenty years ago, they were the first

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Speaker 1: version of the book. They were at parody. And our

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Speaker 1: estimate is, for one that intangibles will be more than

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Speaker 1: two times tangible. So in the last twenty years alone,

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Speaker 1: they you could sort of say they started the race

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Speaker 1: basically neck and neck, and now it's much much more intangible.

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Speaker 1: So why why is that important? Like why do we

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Speaker 1: care about that? That's important because tangible assets, the way

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Speaker 1: the accounts record them is they're capitalized on the balance sheet, right,

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Speaker 1: it's a it's property, plant, and equipment, which we then

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Speaker 1: depreciate over time, so it does show up in the

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Speaker 1: income statement, but primarily in the form of depreciation. By contrast,

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Speaker 1: intangible investments are fully expensed. Now. The most famous of

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Speaker 1: these research and development, and we've known that since the

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Speaker 1: nineteen seventies, the fast we decided that that R and

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Speaker 1: D should be expensed, and there's debate about there's been

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Speaker 1: builby about that for a very long time. But now

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Speaker 1: R and D is only a quarter of total intangible investment.

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Speaker 1: So there's lots of other stuff that's going on that's

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Speaker 1: all expensed. So what does that mean, all things being equal,

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Speaker 1: The answer is earnings are a lot lower than they

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Speaker 1: would otherwise be. I always like to point out that, uh,

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Speaker 1: you know, great companies like Walmart, great companies like home Depot,

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Speaker 1: for the first ten or fifteen years, they republic had

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Speaker 1: negative free cash flow, right, which their investments were bigger

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Speaker 1: than their earnings. That they had positive earnings. Was that

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Speaker 1: a problem, No, it's fantastic, right, because their investments had

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Speaker 1: very high returns on on capital and so and by

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Speaker 1: the way, Walmart, for example, it's first fifte years tripled

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Speaker 1: the performance of the stock market. Right, it crushed it.

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Speaker 1: And when you do the that's a substantial compounding advantage. Right.

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Speaker 1: But the problem is now where we're conflating investments and

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Speaker 1: expenses on the income statement and don't see that. We

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Speaker 1: can't unpack those things and just to just to jump

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Speaker 1: in places like Walmart and Home Depot and those just

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Speaker 1: they're buying land, they're putting up new stores, they're expanding,

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Speaker 1: so that sort of tangibles does show up on the

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Speaker 1: balance sheet, that's right, and that's the whole point. So

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Speaker 1: they're there. And by the way, even Walmart, Walmart for

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Speaker 1: sure was an early user of technology. Right. If you

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Speaker 1: read Sam Walton's book, which probably everybody should, it's a

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Speaker 1: fantastic I reread that memoir just last year. It's just awesome.

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Speaker 1: You know, they were early users of technology, so they

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Speaker 1: were early intangible users as well. But you're exactly right.

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Speaker 1: The vast majority of their investments were physical you can

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Speaker 1: you can kick it and so and so forth, whereas

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Speaker 1: other other companies that is not the case. So yes,

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Speaker 1: so that to me is a that's a watershed change,

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Speaker 1: and that's why earnings Again, if you're focusing on cash flow,

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Speaker 1: these things become much less important because we're getting to

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Speaker 1: the ultimate route. Answer. Uh, but if you're simply using

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Speaker 1: multiples or some sort of shorthands, you're gonna just miss

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Speaker 1: this very significant development. So so people love to point

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Speaker 1: out how expensive the stock market is and how pricey

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Speaker 1: that we used to call them fang but now with Facebook,

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Speaker 1: I don't know what the alt call it anymore. But um,

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Speaker 1: if we look at the top ten or twenty technology companies,

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Speaker 1: or or the top of the S and P five

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Speaker 1: hundred by market cap, those appear to be pricey. But

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Speaker 1: these are all companies that have massive investments and intangibles.

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Speaker 1: So it's Google, it's Apple, it's Netflix, it's Microsoft, it's Facebook.

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Speaker 1: Go down the list, it's in Vidio. All these companies

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Speaker 1: are They own software, they own patents, they own processes.

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Speaker 1: Does this imply that these pricey technology companies I left

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Speaker 1: out Tesla from the list. Are are these so called

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Speaker 1: pricey tech companies that have so much sunk into intangibles?

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Speaker 1: Are these perhaps less pricey than the average stock analysts

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Speaker 1: believes if you're using additional multiple as, you get a

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Speaker 1: very different picture. And you know, we recently wrote a

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Speaker 1: report called uh, it's called classifying for clarity, where we

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Speaker 1: talked about argue. We argued that certain things should be

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Speaker 1: restated in the statement of cash flows. And we use

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Speaker 1: as our case study Amazon dot Com. Right, so, one

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Speaker 1: of these companies and Amazon back, our calculation or our

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Speaker 1: estimate is at Amazon's intangible investments in or forty four

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Speaker 1: billion dollars and you, uh, they're if you amortize, if

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Speaker 1: you build old schedule and advertise it, it it still comes

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Speaker 1: out to nineteen billion dollars of net profit increase. Now,

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Speaker 1: Amazon's profits last year about twenty billion. So so just

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Speaker 1: if you accept as you doubled the profits right now,

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Speaker 1: if you know you we could quibble about the details

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Speaker 1: of that so and so worth, but basically that is no,

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Speaker 1: that's exactly right. And the IBADAT numbers don't quite double,

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Speaker 1: but they close to double. And so now the flip

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Speaker 1: side of all that, that's you know, the earnings are better,

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Speaker 1: but let's also recognize the investments are a lot higher

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Speaker 1: than what is reported to And so I always say

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Speaker 1: the job of an investors to understand the magnitude of

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Speaker 1: investments and the return on investments, to understand future profits

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Speaker 1: and so for a company like Amazon, they're earning a

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00:21:10,680 --> 00:21:13,199
Speaker 1: lot more than people, at least what they seem to report,

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Speaker 1: but they're also investing a lot more. So you know,

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Speaker 1: there's still lots of judgment as to what those investments

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Speaker 1: will pay off and so and so forth. But you're

394
00:21:19,359 --> 00:21:24,320
Speaker 1: exactly right, it's a very distorted picture, you know, without commentary.

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Speaker 1: So this is the whole thing about you know, the

396
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Speaker 1: market used to trade at this multiple. You know, it's

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Speaker 1: just the underlying uh nature of our markets. Our businesses

398
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Speaker 1: are enterprises are so different today that I think that

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Speaker 1: those sort of comparisons seem to be very simplistic. And

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Speaker 1: then just throw in the whole interest rate thing as

401
00:21:43,560 --> 00:21:47,399
Speaker 1: another curveball to how do you calculate cause the capital

402
00:21:47,440 --> 00:21:51,679
Speaker 1: is so cheap, does that artificially does that artificially enhanced

403
00:21:51,680 --> 00:21:55,800
Speaker 1: ownings or our companies taking advantage of that. It's it's

404
00:21:55,880 --> 00:21:59,280
Speaker 1: not a one off. Oh look, capital is cheap, therefore

405
00:21:59,320 --> 00:22:04,360
Speaker 1: ownings are fire. It's our money is free, our companies

406
00:22:04,400 --> 00:22:08,920
Speaker 1: opportunistically taking advantage of that window when it presents itself. Right,

407
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Speaker 1: I don't if that's a common But on a related

408
00:22:13,359 --> 00:22:16,480
Speaker 1: note to the intangible, it's just popped into my head. Um.

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Speaker 1: Joe Davis, who is the chief economist at Vanguard, did

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Speaker 1: a research paper I don't know a year or two

411
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Speaker 1: ago discussing the rise of intangibles as a predictor of

412
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Speaker 1: which regions around the world, Which countries around the world

413
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Speaker 1: our prime for growth. When you start to see patents

414
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Speaker 1: and software processes and copyrights expand in a given nation,

415
00:22:39,040 --> 00:22:41,960
Speaker 1: you should expect their GDP and their stock market to

416
00:22:42,119 --> 00:22:46,560
Speaker 1: subsequently respond to that, usually uh, in a positive correlation.

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Speaker 1: I mean, I'd love to see that. It makes sense,

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Speaker 1: it makes complete sense to me. And again, it's just

419
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Speaker 1: another indication of how things have changed. Right Whereas we

420
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Speaker 1: may have said a generation or two before, if those

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Speaker 1: factories are popping up and those good blue color jobs

422
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Speaker 1: right now, you're something different is sort of a leading

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Speaker 1: indicator of future wealth creat That's right. If you have

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Speaker 1: a bunch of coders showing up in a particular area,

425
00:23:08,080 --> 00:23:10,840
Speaker 1: that might mean something positive for for the economy and

426
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Speaker 1: for that stock market. So so let's bring this back

427
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Speaker 1: to trying to figure out value. What do earnings actually

428
00:23:17,119 --> 00:23:20,560
Speaker 1: tell us about a company's value? Well, I mean, earnings

429
00:23:20,560 --> 00:23:23,240
Speaker 1: are just part of the equation, right, and so Uh.

430
00:23:23,640 --> 00:23:26,000
Speaker 1: We argue very early on the book that earnings telling

431
00:23:26,000 --> 00:23:28,480
Speaker 1: you very little. In fact, the appendix to chapter one

432
00:23:28,520 --> 00:23:32,920
Speaker 1: shows again that earnings by themselves do not even tell

433
00:23:32,920 --> 00:23:36,800
Speaker 1: you about value or value creation. So the what we

434
00:23:36,920 --> 00:23:39,200
Speaker 1: focus on is a very standard finance way to think

435
00:23:39,200 --> 00:23:41,600
Speaker 1: about this, which is free cash flow. And free cash

436
00:23:41,640 --> 00:23:44,760
Speaker 1: flow is the pool available pool of capital available to

437
00:23:44,800 --> 00:23:47,440
Speaker 1: all capital providers. You know, bury besides doing all the

438
00:23:47,440 --> 00:23:49,320
Speaker 1: stuff you do, you're a business owner, so you know

439
00:23:49,400 --> 00:23:51,480
Speaker 1: exactly how this. You have money coming in and money

440
00:23:51,480 --> 00:23:53,239
Speaker 1: going out, and you sort of know how you have

441
00:23:53,320 --> 00:23:55,120
Speaker 1: to think about this kind of stuff. We just roll

442
00:23:55,160 --> 00:23:58,399
Speaker 1: all our cash into uh, you know, shibu imu and

443
00:23:59,440 --> 00:24:01,879
Speaker 1: exactly dol coins and we just let it roll. So

444
00:24:01,920 --> 00:24:05,159
Speaker 1: we're that's it's been. It's been speculative, but it's been

445
00:24:05,160 --> 00:24:07,280
Speaker 1: working out. It's been working out great. And that's the

446
00:24:07,520 --> 00:24:12,520
Speaker 1: deep value analysis on that one. And so yeah, so um.

447
00:24:12,640 --> 00:24:16,879
Speaker 1: The so free cash flow is basically net operating profit

448
00:24:16,920 --> 00:24:19,640
Speaker 1: after taxes, which is a rough measure of earnings. Right,

449
00:24:19,640 --> 00:24:21,840
Speaker 1: it's a little bit more formal, but rough measure of earnings.

450
00:24:21,920 --> 00:24:26,399
Speaker 1: Net operating profits after taxes, and the key to net

451
00:24:26,400 --> 00:24:28,560
Speaker 1: this in the acronym is no pad. The key to

452
00:24:28,680 --> 00:24:32,480
Speaker 1: notepad is that it's the unlevered cash earnings of a business.

453
00:24:32,480 --> 00:24:35,600
Speaker 1: So unlevered means there's no reckoning for financial leverage at

454
00:24:35,640 --> 00:24:38,040
Speaker 1: this point. And it's cash earnings, right, so you're taking

455
00:24:38,080 --> 00:24:40,280
Speaker 1: out all the cash counts and that's a beautiful number

456
00:24:40,320 --> 00:24:42,760
Speaker 1: to know. And then investment is all the investments of

457
00:24:42,760 --> 00:24:45,399
Speaker 1: the company needs to make, including working capital changes and

458
00:24:45,480 --> 00:24:47,840
Speaker 1: cap backs and so on so forth. So so free

459
00:24:47,840 --> 00:24:50,199
Speaker 1: cash flow sort of the bottom line number. And by

460
00:24:50,240 --> 00:24:53,320
Speaker 1: the way, even when we make adjustments to intangibles, what

461
00:24:53,400 --> 00:24:56,119
Speaker 1: we're doing is essentially making earnings higher, no PAD higher,

462
00:24:56,119 --> 00:24:58,720
Speaker 1: and we're making investments higher. Free cash flow sort of

463
00:24:58,720 --> 00:25:01,760
Speaker 1: the bottom line that does and change, and that's the

464
00:25:01,840 --> 00:25:03,240
Speaker 1: number we try to keep our eye on. So you

465
00:25:03,280 --> 00:25:05,520
Speaker 1: remember your high school basketball coach, I keep your eye

466
00:25:05,520 --> 00:25:07,440
Speaker 1: on the hips, right, because everything's gonna follow the hips.

467
00:25:07,680 --> 00:25:10,000
Speaker 1: That is the hips of finance, right, which is free

468
00:25:10,040 --> 00:25:11,800
Speaker 1: cash flow. That's the number you want to keep your

469
00:25:11,800 --> 00:25:15,520
Speaker 1: eye on. So Howard Marks is fond of discussing second

470
00:25:15,600 --> 00:25:18,960
Speaker 1: level thinking, and so what I'm hearing from you is

471
00:25:19,080 --> 00:25:23,720
Speaker 1: that just looking at earnings or pe multiples is first

472
00:25:23,800 --> 00:25:27,240
Speaker 1: level thinking and second level thinking is putting this into

473
00:25:27,320 --> 00:25:31,160
Speaker 1: the broader context of earnings relatives to free cash flow

474
00:25:31,280 --> 00:25:35,840
Speaker 1: relative to intangibles and growth, Is that like a wild

475
00:25:35,920 --> 00:25:38,360
Speaker 1: overstatement or no, not at all. I mean I think

476
00:25:38,359 --> 00:25:41,400
Speaker 1: that the answer is the way I might say this differently,

477
00:25:41,480 --> 00:25:43,080
Speaker 1: is it free cash flow is the ultimate thing that

478
00:25:43,119 --> 00:25:45,560
Speaker 1: we care about. All the other stuff you just mentioned

479
00:25:45,680 --> 00:25:47,679
Speaker 1: terms of earnings and multiples and so forth, those are

480
00:25:47,680 --> 00:25:50,800
Speaker 1: all proxies that try to get to the same things.

481
00:25:50,800 --> 00:25:53,240
Speaker 1: So they're short of shorthands, right, And by the way,

482
00:25:53,280 --> 00:25:55,200
Speaker 1: I mean you've had you've had the great Danny Knemon

483
00:25:55,320 --> 00:25:58,720
Speaker 1: with you. Right. What's good about shorthands? What's good about

484
00:25:58,720 --> 00:26:01,320
Speaker 1: heuristics as they save your time? Right, So that's why

485
00:26:01,359 --> 00:26:04,840
Speaker 1: they're useful, and that's why we use them. What's limiting

486
00:26:04,880 --> 00:26:08,639
Speaker 1: about heuristics or shorthand as they have biases? Right, And

487
00:26:08,720 --> 00:26:10,800
Speaker 1: so the key is not to the key is not

488
00:26:10,840 --> 00:26:12,960
Speaker 1: to never use them, the keys to understand where their

489
00:26:13,000 --> 00:26:16,520
Speaker 1: limitations lie. And I think that's where people get a

490
00:26:16,560 --> 00:26:18,240
Speaker 1: little bit can be a little bit lazy around the

491
00:26:18,320 --> 00:26:20,320
Speaker 1: edges and just sort of say like it's this thing

492
00:26:20,359 --> 00:26:22,440
Speaker 1: has always traded at twenty times this, and so it

493
00:26:22,440 --> 00:26:25,040
Speaker 1: should be twenty times that's not really you want to

494
00:26:25,400 --> 00:26:28,480
Speaker 1: go back to the core, the core ideas. So so

495
00:26:28,560 --> 00:26:32,240
Speaker 1: here's the pushback that I think we would get from

496
00:26:32,280 --> 00:26:37,240
Speaker 1: a Robin Hood trader today who is looking at their

497
00:26:37,240 --> 00:26:39,360
Speaker 1: portfolio over the past couple of years. They would say

498
00:26:39,400 --> 00:26:44,439
Speaker 1: something like, hey, this sophistic hidden analysis is interesting, but

499
00:26:44,920 --> 00:26:48,040
Speaker 1: I haven't been using second level thinking. I've been picking

500
00:26:48,480 --> 00:26:51,680
Speaker 1: the fastest horse the past couple of years without looking

501
00:26:51,680 --> 00:26:54,440
Speaker 1: at the odds, without looking at anything else, and that's

502
00:26:54,440 --> 00:26:56,960
Speaker 1: been working out. And I joke about dose coins and

503
00:26:57,000 --> 00:27:00,600
Speaker 1: things like that, but if you bought the fair this back,

504
00:27:00,800 --> 00:27:07,480
Speaker 1: the fastest tech ev company, the fastest uh crypto coin,

505
00:27:07,560 --> 00:27:11,160
Speaker 1: it didn't matter. Momentum seems to be driving those fast

506
00:27:11,200 --> 00:27:14,240
Speaker 1: horses the highest. How do you respond to that sort

507
00:27:14,280 --> 00:27:17,120
Speaker 1: of of pushback, Well, they're there's sort of two levels

508
00:27:17,160 --> 00:27:20,119
Speaker 1: of comments. One is, UM, I think it's important. I'm

509
00:27:20,119 --> 00:27:22,679
Speaker 1: gonna sound like an old, an old fogi here, but

510
00:27:22,840 --> 00:27:25,560
Speaker 1: I think it's important to make a distinction between speculating

511
00:27:25,640 --> 00:27:28,840
Speaker 1: and investing. And um, by the way, and this is

512
00:27:28,880 --> 00:27:31,159
Speaker 1: without any sort of moral judgment. So this is just

513
00:27:31,240 --> 00:27:33,800
Speaker 1: you know, it's trying to make this demarcation without judgment,

514
00:27:33,920 --> 00:27:36,480
Speaker 1: right A speculator or someone who buys something in the

515
00:27:36,520 --> 00:27:39,920
Speaker 1: hope that it goes up. Uh an investor someone who

516
00:27:39,960 --> 00:27:42,879
Speaker 1: buys a partial stake in a business. Right, it's a

517
00:27:43,000 --> 00:27:45,680
Speaker 1: very different mindset. And so if the markets shut shut

518
00:27:45,720 --> 00:27:48,000
Speaker 1: down for three months, three years or whatever, you wouldn't

519
00:27:48,000 --> 00:27:50,239
Speaker 1: care because your own part of a business. Right. So

520
00:27:50,280 --> 00:27:52,720
Speaker 1: this is almost again the Barry Berry is the investor

521
00:27:53,040 --> 00:27:55,639
Speaker 1: and versus Barry as a proprietor of a business. Right

522
00:27:55,680 --> 00:27:57,280
Speaker 1: you think about the value of the business. It's a

523
00:27:57,400 --> 00:28:00,640
Speaker 1: very different as you know, it's a very different mindset. Now,

524
00:28:00,680 --> 00:28:03,000
Speaker 1: when I peer out of the world today, I guess

525
00:28:03,000 --> 00:28:04,560
Speaker 1: I would actually think that I sort of think of

526
00:28:04,600 --> 00:28:06,840
Speaker 1: the world in sort of three different buckets. The first

527
00:28:06,840 --> 00:28:09,280
Speaker 1: bucket is sort of the normal bucket, where I think that,

528
00:28:09,359 --> 00:28:12,000
Speaker 1: notwithstanding we have some obviously a little bit of zany stuff,

529
00:28:12,160 --> 00:28:14,720
Speaker 1: most of the stuff out there is pretty pretty solid, right,

530
00:28:14,760 --> 00:28:17,720
Speaker 1: like pretty normal. And then the second bucket might be

531
00:28:18,000 --> 00:28:20,200
Speaker 1: you know, where I put things like the meme stocks

532
00:28:20,200 --> 00:28:23,080
Speaker 1: and so forth. These are be sort of the momentum.

533
00:28:23,160 --> 00:28:25,800
Speaker 1: And you know, in our language we call these sort

534
00:28:25,800 --> 00:28:29,959
Speaker 1: of diversity breakdowns. People correlate their behaviors in certain ways

535
00:28:30,600 --> 00:28:32,439
Speaker 1: and by the way, there's there's some language in the

536
00:28:32,440 --> 00:28:35,040
Speaker 1: book that helps talk about these things like reflexivity and

537
00:28:35,080 --> 00:28:36,800
Speaker 1: so forth. You know, so this would be the game

538
00:28:36,840 --> 00:28:38,960
Speaker 1: stops of the world in a MCS and so forth.

539
00:28:39,240 --> 00:28:40,920
Speaker 1: And by the way, many of these companies have actually

540
00:28:41,000 --> 00:28:44,320
Speaker 1: done very sensible things, which is they've they've sold, they've

541
00:28:44,360 --> 00:28:47,200
Speaker 1: sold raised capital, which by the way, increases the intrinsic

542
00:28:47,280 --> 00:28:51,160
Speaker 1: value of the per share owners for ongoing holders. And

543
00:28:51,200 --> 00:28:57,640
Speaker 1: then the third area is um cryptos decentralized finance. Part

544
00:28:57,680 --> 00:28:59,200
Speaker 1: of what I think is going on with the electric

545
00:28:59,240 --> 00:29:02,920
Speaker 1: electric vehicle market and so forth, and there's a very there.

546
00:29:02,960 --> 00:29:05,560
Speaker 1: I think what we're seeing is a very very old

547
00:29:05,800 --> 00:29:10,320
Speaker 1: and very well known pattern, which is, as new industries

548
00:29:10,480 --> 00:29:14,360
Speaker 1: develop the very common patterns, you see a huge upswing

549
00:29:14,360 --> 00:29:18,040
Speaker 1: in the number of participants and really experiments. So it's

550
00:29:18,120 --> 00:29:21,640
Speaker 1: lots of new entrants, lots of money flows in, lots

551
00:29:21,680 --> 00:29:24,960
Speaker 1: of people trying out weird and wacky stuff. By the way,

552
00:29:25,000 --> 00:29:27,000
Speaker 1: it wasn't that long ago, Barry, you remember this and

553
00:29:27,040 --> 00:29:30,320
Speaker 1: the dot com the same kind of thing, right, and

554
00:29:30,360 --> 00:29:32,880
Speaker 1: you know, people go this is all crazy and wasteful, right, Okay.

555
00:29:32,920 --> 00:29:36,400
Speaker 1: What happens is then eventually the market sorts this out

556
00:29:36,440 --> 00:29:39,560
Speaker 1: almost think of it as a Darwinian process, and then

557
00:29:39,640 --> 00:29:42,080
Speaker 1: you have the come down the back end. So there's

558
00:29:42,080 --> 00:29:45,280
Speaker 1: a lot of exit through companies going bankrupt or consolidation

559
00:29:45,360 --> 00:29:48,280
Speaker 1: and so so for the market determines what is legitimate

560
00:29:48,320 --> 00:29:50,840
Speaker 1: what is not, and lots of things go to zero.

561
00:29:50,920 --> 00:29:53,480
Speaker 1: But at the end of it, what what distills out

562
00:29:53,720 --> 00:29:57,160
Speaker 1: is something new and something important. So until and this

563
00:29:57,240 --> 00:29:59,720
Speaker 1: is sort of standard setting as well. So I think

564
00:29:59,760 --> 00:30:02,200
Speaker 1: that a good example. What's going on crypto. I mean

565
00:30:02,960 --> 00:30:06,480
Speaker 1: to me, that whole complex something that's very real. It's

566
00:30:06,480 --> 00:30:09,680
Speaker 1: gonna be It's gonna be with us. Um, much of

567
00:30:09,720 --> 00:30:11,960
Speaker 1: what's going on out there is not going to survive,

568
00:30:12,080 --> 00:30:14,239
Speaker 1: but there will be things that survive and will be

569
00:30:14,280 --> 00:30:18,680
Speaker 1: making important contributions to our economy. You're about to say something, well,

570
00:30:18,760 --> 00:30:21,080
Speaker 1: I was gonna I just wanted to put into context

571
00:30:21,120 --> 00:30:25,000
Speaker 1: because you biased the audience like pulling yourself an old

572
00:30:25,000 --> 00:30:29,240
Speaker 1: fog and referencing the dot coms. But if you look

573
00:30:29,280 --> 00:30:32,120
Speaker 1: at the history of bubbles, and I don't mean that

574
00:30:32,200 --> 00:30:34,959
Speaker 1: in a negative way. In fact, um Dan Gross had

575
00:30:35,000 --> 00:30:38,480
Speaker 1: a great book pop Why Bubbles Are Great for the economy.

576
00:30:38,760 --> 00:30:42,239
Speaker 1: You can look at telegraphs and railroads and automobiles and

577
00:30:42,320 --> 00:30:46,000
Speaker 1: televisions and computers and fiber optic can go through every

578
00:30:46,000 --> 00:30:49,280
Speaker 1: one of these technological innovations and they all follow that

579
00:30:49,360 --> 00:30:54,480
Speaker 1: exact path. There's this Cambrian explosion of experimentation. Lots of companies,

580
00:30:54,640 --> 00:30:58,080
Speaker 1: most of which don't survive, but the ones that come

581
00:30:58,080 --> 00:31:01,960
Speaker 1: out of it are our game changes really move the needle, right,

582
00:31:02,240 --> 00:31:04,280
Speaker 1: and we and the and the point being we don't

583
00:31:04,600 --> 00:31:08,440
Speaker 1: know a priori which ones are going to succeed or fail, right,

584
00:31:08,440 --> 00:31:10,640
Speaker 1: it gets sorted out. It's a sort of a big,

585
00:31:10,680 --> 00:31:14,600
Speaker 1: messy sorting out process. So so I think that's a

586
00:31:14,640 --> 00:31:16,400
Speaker 1: little bit of that's a big part of what's going on.

587
00:31:16,440 --> 00:31:19,640
Speaker 1: So things like I mean, electric vehicles, this is this

588
00:31:19,720 --> 00:31:22,320
Speaker 1: is like the canonical example of how this works, right,

589
00:31:22,360 --> 00:31:24,560
Speaker 1: And you know, the main academic on this, I know,

590
00:31:24,680 --> 00:31:27,120
Speaker 1: Dance book is actually really good book and an interesting one,

591
00:31:27,160 --> 00:31:29,560
Speaker 1: but you know, sort of the canonical the main the

592
00:31:29,600 --> 00:31:32,520
Speaker 1: main academic on this a guy named Stephen Clepper who

593
00:31:32,600 --> 00:31:35,400
Speaker 1: was a professor at Carnegie Mellon. And Clepper has wrote

594
00:31:35,520 --> 00:31:37,880
Speaker 1: very seriously about this and documented, as you point out,

595
00:31:37,920 --> 00:31:41,760
Speaker 1: all these these basics. So it's the flow of of talent,

596
00:31:41,960 --> 00:31:45,560
Speaker 1: it's the flow of money. It's the flow of entrepreneurs

597
00:31:45,920 --> 00:31:48,440
Speaker 1: to try to solve problems with some new tools at

598
00:31:48,440 --> 00:31:51,920
Speaker 1: their disposal, not knowing in advance what's going to work.

599
00:31:52,240 --> 00:31:54,200
Speaker 1: By the way, I mean this is now will nerd

600
00:31:54,240 --> 00:31:57,120
Speaker 1: out for just one second. I the first time I

601
00:31:57,120 --> 00:31:59,560
Speaker 1: ever wrote about this, it was in actually the context

602
00:31:59,600 --> 00:32:02,280
Speaker 1: of ners old development of children. And it turns out

603
00:32:02,320 --> 00:32:04,320
Speaker 1: that like the number of neurons in your brain actually

604
00:32:04,320 --> 00:32:07,720
Speaker 1: don't change that much through your life. What changes radically

605
00:32:07,760 --> 00:32:10,720
Speaker 1: is a number of synaptic connections between their the neurons.

606
00:32:10,760 --> 00:32:12,400
Speaker 1: And so from the time you're borns some time you're

607
00:32:12,400 --> 00:32:14,840
Speaker 1: about two or three years old, there's this huge upswing

608
00:32:14,840 --> 00:32:17,560
Speaker 1: and synaptic connections. So a three year old, if you've

609
00:32:17,560 --> 00:32:20,320
Speaker 1: ever met them, you know they're not super efficient machines,

610
00:32:20,440 --> 00:32:23,280
Speaker 1: but they're they're really open to the world, so learning

611
00:32:23,360 --> 00:32:25,400
Speaker 1: language is a lot. They're very curious about the world

612
00:32:25,440 --> 00:32:27,320
Speaker 1: and so and so forth. But they're inefficient. And so

613
00:32:27,400 --> 00:32:29,760
Speaker 1: what happens then is this it's called the heavy and process.

614
00:32:29,800 --> 00:32:32,080
Speaker 1: You use it or lose it. If the connection works,

615
00:32:32,120 --> 00:32:34,000
Speaker 1: you use it, and if it doesn't, it gets pruned

616
00:32:34,040 --> 00:32:38,080
Speaker 1: away and you have this massive reduction number of synaptic connections.

617
00:32:38,080 --> 00:32:40,280
Speaker 1: So scientists were interested in this there, you know, so

618
00:32:40,320 --> 00:32:42,480
Speaker 1: they documented this whole process. They're like, well, this is

619
00:32:42,520 --> 00:32:44,640
Speaker 1: kind of weird though, right, because the brain is a

620
00:32:44,760 --> 00:32:47,680
Speaker 1: very costly mechanism. You know, it's twenty of your energy

621
00:32:47,800 --> 00:32:50,360
Speaker 1: usage and this big thing on top of your head,

622
00:32:50,400 --> 00:32:52,600
Speaker 1: and you're vulnerable and so and so forth. And it

623
00:32:52,680 --> 00:32:54,880
Speaker 1: turns out that they sort of simulated this, and it

624
00:32:54,920 --> 00:32:57,440
Speaker 1: turns out this idea of trying things out and then

625
00:32:57,520 --> 00:33:00,440
Speaker 1: winnowing is one of the best ways to learn about

626
00:33:00,480 --> 00:33:03,880
Speaker 1: an environment, Isn't that cool? Right? So in a sense,

627
00:33:03,920 --> 00:33:07,120
Speaker 1: what we're doing is these are these these Cambrian explosions.

628
00:33:07,160 --> 00:33:12,080
Speaker 1: You described our methods financial and technological and entrepreneurial methods

629
00:33:12,240 --> 00:33:15,240
Speaker 1: to learn about the world and figure out what works. So,

630
00:33:15,240 --> 00:33:17,840
Speaker 1: so two comments on your nerd ing out, which I'm

631
00:33:17,880 --> 00:33:22,760
Speaker 1: totally loving. First, if you haven't seen American Utopia David

632
00:33:22,760 --> 00:33:26,200
Speaker 1: Burns play, it actually starts out with that exact discussion

633
00:33:26,400 --> 00:33:31,000
Speaker 1: of the the childhood, the infant brain making all these

634
00:33:31,000 --> 00:33:34,760
Speaker 1: synaptic connections and then just letting a trophy that which

635
00:33:34,800 --> 00:33:37,440
Speaker 1: doesn't work. And so what you're left with is a

636
00:33:37,560 --> 00:33:41,200
Speaker 1: very efficient set of things that the brain knows actually

637
00:33:41,640 --> 00:33:45,720
Speaker 1: are successful connections. But second, there's a fascinating book which

638
00:33:45,880 --> 00:33:48,840
Speaker 1: you and I may have spoken about previously, called The

639
00:33:49,000 --> 00:33:53,840
Speaker 1: Last Ape Standing, and it talks about how Homo sapiens

640
00:33:54,560 --> 00:33:57,840
Speaker 1: came very close to not surviving the Last Ice Age

641
00:33:58,360 --> 00:34:04,040
Speaker 1: because of how energy intensive the brain is and what

642
00:34:04,280 --> 00:34:08,759
Speaker 1: works really well in most environments doesn't work in a

643
00:34:09,000 --> 00:34:13,200
Speaker 1: in a resource poor environment, and the Ice Age turned

644
00:34:13,200 --> 00:34:15,759
Speaker 1: out to be a very research poor so that the

645
00:34:15,840 --> 00:34:18,560
Speaker 1: book kind of says, hey, we were not We were

646
00:34:18,600 --> 00:34:22,719
Speaker 1: down to about ten thousand Homo sapiens a couple more

647
00:34:22,840 --> 00:34:27,960
Speaker 1: years of bad climate, and you know this might be uh,

648
00:34:28,400 --> 00:34:32,000
Speaker 1: this might be in neanderthal world, not not a human world,

649
00:34:32,000 --> 00:34:34,719
Speaker 1: which is kind of kind of interesting. Um, so we

650
00:34:34,760 --> 00:34:37,000
Speaker 1: could we could walk out about a bunch of other stuff.

651
00:34:37,000 --> 00:34:40,359
Speaker 1: But I want to bring it back to to um

652
00:34:40,480 --> 00:34:43,000
Speaker 1: earnings and value. One of the things in the book

653
00:34:43,040 --> 00:34:48,640
Speaker 1: you talk about is investors believe price earnings multiple determined value.

654
00:34:49,120 --> 00:34:52,800
Speaker 1: But you argue that sort of backwards price earning multiples

655
00:34:52,840 --> 00:34:55,800
Speaker 1: are a function of value. Am I Am I getting

656
00:34:55,800 --> 00:34:57,719
Speaker 1: that right? Yeah? I mean you are if you think

657
00:34:57,719 --> 00:35:01,000
Speaker 1: about it. I mean the formulation is the um E

658
00:35:01,640 --> 00:35:06,640
Speaker 1: times PE equals p right, right, And so what you're

659
00:35:06,680 --> 00:35:09,759
Speaker 1: saying is, in order to forecast price, you need to

660
00:35:09,760 --> 00:35:11,960
Speaker 1: know the price of which is the numerator of the

661
00:35:12,000 --> 00:35:13,759
Speaker 1: pe right. So in a sense there's a bit of

662
00:35:13,760 --> 00:35:16,879
Speaker 1: a circular argument. So um, I mean, I don't want

663
00:35:16,880 --> 00:35:18,680
Speaker 1: to dwell too much on that. We've beaten up a

664
00:35:18,719 --> 00:35:22,719
Speaker 1: little enough on multiples. But the point being again, multiples

665
00:35:22,840 --> 00:35:28,160
Speaker 1: are are not valuation their shorthand for the valuation process

666
00:35:28,800 --> 00:35:31,600
Speaker 1: and with that shorthand or all the good things about

667
00:35:31,600 --> 00:35:34,000
Speaker 1: saving time and with that shorthand or all the bad

668
00:35:34,040 --> 00:35:37,799
Speaker 1: things about limitations and biases and blind spots. And so

669
00:35:38,280 --> 00:35:40,759
Speaker 1: if you do not, if you are not aware of

670
00:35:40,800 --> 00:35:43,680
Speaker 1: those limitations and blind spots, you're going to be I

671
00:35:43,719 --> 00:35:48,720
Speaker 1: think ill served by using simplistic measures. Quite interesting, Let's

672
00:35:48,760 --> 00:35:51,799
Speaker 1: talk a little bit about modeling, and you use a

673
00:35:51,880 --> 00:35:55,799
Speaker 1: number of examples in the book. Companies like Shopify and

674
00:35:55,920 --> 00:36:00,399
Speaker 1: Dominoes couldn't be more different, but they each present a

675
00:36:00,480 --> 00:36:05,600
Speaker 1: challenge to traditional analysts ability to understand the business and

676
00:36:05,640 --> 00:36:11,840
Speaker 1: forecast using old approaches. Both companies have been incredible performers.

677
00:36:11,840 --> 00:36:15,520
Speaker 1: People don't realize Domino's is one of the best performers

678
00:36:15,520 --> 00:36:18,000
Speaker 1: of the past twenty years, if not the best, it

679
00:36:18,040 --> 00:36:21,160
Speaker 1: depends on where everything closes by the time people hear this.

680
00:36:21,280 --> 00:36:25,360
Speaker 1: But right, Domino's top five, maybe even highest performer in

681
00:36:25,400 --> 00:36:27,799
Speaker 1: the past twenty years, is that right? Actually don't know that,

682
00:36:27,800 --> 00:36:29,239
Speaker 1: but yeah, that sounds right. It's a great it's an

683
00:36:29,239 --> 00:36:32,640
Speaker 1: amazing business. So so let's talk about how do you

684
00:36:32,760 --> 00:36:35,680
Speaker 1: model these in a way that gives you a better

685
00:36:35,800 --> 00:36:40,040
Speaker 1: insight into their future prospects? And what's the difference between

686
00:36:40,800 --> 00:36:45,200
Speaker 1: expectations investing and the traditional way analysts have been modeling these? Right,

687
00:36:45,320 --> 00:36:47,080
Speaker 1: so maybe we should take those will take those in

688
00:36:47,080 --> 00:36:49,480
Speaker 1: turn because they're slightly different flavors of what we're trying

689
00:36:49,520 --> 00:36:52,160
Speaker 1: to do. So Domino's Pizza was the case study. So

690
00:36:52,239 --> 00:36:54,279
Speaker 1: the key is that when we go through the expectations

691
00:36:54,280 --> 00:36:58,440
Speaker 1: investing process, understanding price and pliant expectations step one. Step

692
00:36:58,480 --> 00:37:01,200
Speaker 1: two is doing strategic and finance analysis. Step three is

693
00:37:01,200 --> 00:37:04,239
Speaker 1: making buying cell decisions. It's really nice to have a

694
00:37:04,360 --> 00:37:08,359
Speaker 1: case study to make it concrete. Now. Um, the case

695
00:37:08,400 --> 00:37:14,319
Speaker 1: study for the original book was Gateway two thousands, which

696
00:37:14,440 --> 00:37:19,840
Speaker 1: lasted for like three years after the boxes that were directed.

697
00:37:20,640 --> 00:37:23,640
Speaker 1: Seemed like a good idea at the time. It was

698
00:37:23,719 --> 00:37:29,040
Speaker 1: called so anyway, um, that's execution risk gateway and Dell

699
00:37:29,120 --> 00:37:32,759
Speaker 1: had similar models. Dell just executed, they did, they did,

700
00:37:32,800 --> 00:37:35,680
Speaker 1: and so so the idea, so the truth of the

701
00:37:35,719 --> 00:37:38,320
Speaker 1: matter is very like, this is how the smoke filled rooms,

702
00:37:38,320 --> 00:37:41,320
Speaker 1: how decisions get made. We're like, let's find a business

703
00:37:41,440 --> 00:37:44,440
Speaker 1: that's pretty straightforward to understand that we hope will be

704
00:37:44,480 --> 00:37:47,239
Speaker 1: around for a while, right, and if they leave it'll

705
00:37:47,239 --> 00:37:49,080
Speaker 1: be for reasons like they get bought out or so

706
00:37:49,600 --> 00:37:53,120
Speaker 1: pizza pretty understanding. So so the nature of the business

707
00:37:53,200 --> 00:37:56,399
Speaker 1: is pretty understandable, and it is a franchise business. It's

708
00:37:56,400 --> 00:37:58,680
Speaker 1: a it is a very beautiful business, and it's a

709
00:37:58,760 --> 00:38:04,160
Speaker 1: nice business to explaining. Also strategically, because they made they

710
00:38:04,200 --> 00:38:07,080
Speaker 1: have another they made a number of strategic decisions along

711
00:38:07,120 --> 00:38:09,879
Speaker 1: the way that allow us to explain why their their

712
00:38:09,880 --> 00:38:12,520
Speaker 1: business has been good in their strategic behaviors. And and

713
00:38:12,560 --> 00:38:15,480
Speaker 1: by the way, strategy often boils down to things like

714
00:38:15,920 --> 00:38:17,880
Speaker 1: it boils down to trade offs. And one of the

715
00:38:17,880 --> 00:38:21,200
Speaker 1: big tradeoffs that Domino's made early on which they were

716
00:38:21,200 --> 00:38:23,040
Speaker 1: took to they've been taken a task from from time

717
00:38:23,040 --> 00:38:24,840
Speaker 1: to time is that you don't eat there, right, you

718
00:38:24,840 --> 00:38:28,560
Speaker 1: don't go to Domino's two. I mean, there's there's some

719
00:38:28,640 --> 00:38:30,520
Speaker 1: there's some minor exceptions to that, but that's for the

720
00:38:30,560 --> 00:38:33,600
Speaker 1: most part true. And so what are the upsides and

721
00:38:33,640 --> 00:38:36,960
Speaker 1: downsides to that basic thing? So Domino's was, and and

722
00:38:37,000 --> 00:38:41,240
Speaker 1: again they are. They are a very uh intangible intensive

723
00:38:41,280 --> 00:38:45,200
Speaker 1: business in the sense that the business we're looking at

724
00:38:45,280 --> 00:38:48,239
Speaker 1: is the owns the essentially is the franchise or right,

725
00:38:48,280 --> 00:38:50,880
Speaker 1: so they own all these things and their their primary

726
00:38:50,880 --> 00:38:54,279
Speaker 1: function is basically to get ingredients and boxes to the

727
00:38:54,320 --> 00:38:57,880
Speaker 1: different franchisees and then to advertise for everybody. So essentially

728
00:38:57,880 --> 00:39:02,080
Speaker 1: they're an advertising machine and that's what they do. Um

729
00:39:02,120 --> 00:39:05,800
Speaker 1: it sounds like a lot of brands, marketing special not

730
00:39:06,120 --> 00:39:08,960
Speaker 1: specialty know how, a lot of intangibles, and a lot

731
00:39:09,000 --> 00:39:11,160
Speaker 1: of technology. So the the other thing is they're very

732
00:39:11,160 --> 00:39:13,759
Speaker 1: good at technology and have always been very good at technology.

733
00:39:13,840 --> 00:39:16,600
Speaker 1: So for instance, if I can help my franchise the

734
00:39:17,000 --> 00:39:21,319
Speaker 1: understand their labor demands, their product demands, if I can

735
00:39:21,360 --> 00:39:23,480
Speaker 1: make things uniform, in fact, they do a lot of

736
00:39:23,600 --> 00:39:26,839
Speaker 1: stuff that everything becomes very uniform in the kitchen. That

737
00:39:27,000 --> 00:39:30,759
Speaker 1: allows for them to deliver efficiently, to work the work

738
00:39:30,800 --> 00:39:33,439
Speaker 1: the kitchen, fit, to hire people efficiently, all those kinds

739
00:39:33,440 --> 00:39:36,360
Speaker 1: of things, and those are those are really difficult advantages

740
00:39:36,600 --> 00:39:39,680
Speaker 1: to to take away. And then they've been digital early,

741
00:39:39,719 --> 00:39:41,839
Speaker 1: so what you know, ordering online, so and so forth.

742
00:39:42,719 --> 00:39:45,520
Speaker 1: The second example is Shopify, and that's a little bit

743
00:39:45,520 --> 00:39:48,120
Speaker 1: of a different thing. So we have a chapter dedicated

744
00:39:48,200 --> 00:39:51,759
Speaker 1: to US chapter eight, and it was called beyond discounted

745
00:39:51,800 --> 00:39:54,840
Speaker 1: cash Flow. And so the the idea is that sometimes

746
00:39:54,880 --> 00:39:56,560
Speaker 1: you you know, you look at the businesses you can

747
00:39:56,600 --> 00:39:58,920
Speaker 1: touch and feel, and you run the numbers on it,

748
00:39:58,960 --> 00:40:01,239
Speaker 1: and it just have a hard time coming up with

749
00:40:01,239 --> 00:40:03,440
Speaker 1: anything close to the current stock price. And you might

750
00:40:03,480 --> 00:40:06,200
Speaker 1: immediately say, okay, well this is this doesn't make any sense.

751
00:40:06,719 --> 00:40:09,480
Speaker 1: What we what we suggest is that for certain types

752
00:40:09,520 --> 00:40:13,480
Speaker 1: of businesses they may be candidates for having some real

753
00:40:13,600 --> 00:40:16,640
Speaker 1: option value. So what is a real option When we

754
00:40:16,680 --> 00:40:18,880
Speaker 1: know about financial options, right, these are the right but

755
00:40:18,920 --> 00:40:21,880
Speaker 1: not the obligations. Typically, for example, a call option is

756
00:40:21,920 --> 00:40:24,080
Speaker 1: to buy a particular stock at a particular price at

757
00:40:24,080 --> 00:40:28,160
Speaker 1: a particular time. A real option is analogously for a

758
00:40:28,239 --> 00:40:30,960
Speaker 1: real investment in a business. Right, So this is for companies.

759
00:40:31,560 --> 00:40:33,480
Speaker 1: And so what we argue is at certain types of

760
00:40:33,520 --> 00:40:35,799
Speaker 1: businesses and the conditions are things like you wanted to

761
00:40:35,800 --> 00:40:38,239
Speaker 1: be an uncertain business, right, because where there's a lot

762
00:40:38,239 --> 00:40:40,520
Speaker 1: of certainty, there's not a lot of option value. Rights

763
00:40:40,560 --> 00:40:43,440
Speaker 1: of volatility is good for options. You want to management

764
00:40:43,480 --> 00:40:45,600
Speaker 1: team that's thoughtful, so they need to know how to

765
00:40:45,760 --> 00:40:50,280
Speaker 1: identify and cultivate and ultimately execute on those options. Market

766
00:40:50,400 --> 00:40:53,239
Speaker 1: leaders tend to be good because often when there are opportunities,

767
00:40:53,320 --> 00:40:56,120
Speaker 1: the market leader gets the first call. And then finally,

768
00:40:56,120 --> 00:40:58,360
Speaker 1: you need access to capital. When you say to we

769
00:40:58,440 --> 00:41:00,520
Speaker 1: have a great option, we want to X your size it,

770
00:41:00,560 --> 00:41:03,000
Speaker 1: you need to be able to finance it, right, And

771
00:41:03,040 --> 00:41:06,640
Speaker 1: so when those characteristics, those sort of boxes get checked,

772
00:41:06,920 --> 00:41:09,280
Speaker 1: you may have a business with some real options value.

773
00:41:09,280 --> 00:41:13,239
Speaker 1: Now in that case, our our our study from twenty

774
00:41:13,320 --> 00:41:15,600
Speaker 1: years ago was Amazon dot Com and that was probably

775
00:41:15,600 --> 00:41:17,719
Speaker 1: just dumb luck that we picked Amazon, but that was

776
00:41:18,000 --> 00:41:19,960
Speaker 1: that turned out to be sort of one of the

777
00:41:20,000 --> 00:41:22,799
Speaker 1: great examples of a real options company. And just think

778
00:41:22,840 --> 00:41:26,319
Speaker 1: about a WS wasn't even a twinkle on anybody's eye

779
00:41:26,360 --> 00:41:28,239
Speaker 1: in two thousand and one when we wrote that version

780
00:41:28,280 --> 00:41:31,319
Speaker 1: of it, but but we did identify it as a

781
00:41:31,360 --> 00:41:33,960
Speaker 1: company that had a lot of uncertainty and what was

782
00:41:34,000 --> 00:41:36,360
Speaker 1: going on on an executive who seemed to be pretty

783
00:41:36,400 --> 00:41:39,760
Speaker 1: astute at figuring these things out, and along the way

784
00:41:39,840 --> 00:41:42,839
Speaker 1: he I mean he made many many mistakes, Jeff Bezos did,

785
00:41:42,880 --> 00:41:46,400
Speaker 1: but along the way he actually made a lot of

786
00:41:46,440 --> 00:41:49,799
Speaker 1: really interesting, good capital allocation decisions. So so that just

787
00:41:49,840 --> 00:41:51,960
Speaker 1: shows like for all the mistakes that he made and

788
00:41:52,360 --> 00:41:56,479
Speaker 1: many even great executives make that they they are able

789
00:41:56,480 --> 00:42:00,520
Speaker 1: to allocate capital effectively. And Bezos talks about, hey, we're

790
00:42:00,560 --> 00:42:03,280
Speaker 1: not making enough mistakes. If there aren't enough fire phones

791
00:42:03,360 --> 00:42:07,680
Speaker 1: that are disasters, we're not trying enough new things. You know,

792
00:42:07,920 --> 00:42:13,279
Speaker 1: Amazon Prime, next day delivery, Amazon Music and video and

793
00:42:13,600 --> 00:42:16,560
Speaker 1: web services were all because they were throwing stuff up

794
00:42:16,560 --> 00:42:19,120
Speaker 1: against the wall to see what happens. If you're not

795
00:42:19,239 --> 00:42:22,839
Speaker 1: taking a risk, you're not getting the upside. That's exactly right.

796
00:42:22,880 --> 00:42:26,960
Speaker 1: And so so the question would be something like, um,

797
00:42:27,000 --> 00:42:29,680
Speaker 1: if if you think that as a potential for a business,

798
00:42:29,920 --> 00:42:32,120
Speaker 1: and it's obviously not in the touch and field today

799
00:42:32,160 --> 00:42:34,760
Speaker 1: with things you can see today, should you be willing

800
00:42:34,800 --> 00:42:36,520
Speaker 1: to pay for that? And how should you be willing

801
00:42:36,520 --> 00:42:38,040
Speaker 1: to pay for that? So we have a little section

802
00:42:38,520 --> 00:42:40,680
Speaker 1: on real options, and we talked about how to value

803
00:42:40,680 --> 00:42:43,759
Speaker 1: those and some more formal techniques. But but that is

804
00:42:44,880 --> 00:42:47,439
Speaker 1: leaving aside all the numbers and all that the keys.

805
00:42:47,480 --> 00:42:49,960
Speaker 1: It's a mindset, right and and so there may be

806
00:42:50,040 --> 00:42:51,920
Speaker 1: you know, are especially although if you can get this

807
00:42:51,960 --> 00:42:56,160
Speaker 1: optionality for free, that's fantastic, but even if you're willing to,

808
00:42:56,160 --> 00:42:57,480
Speaker 1: if you need to pay a little bit for it.

809
00:42:57,480 --> 00:42:58,640
Speaker 1: By the way, there are other people like you know,

810
00:42:58,640 --> 00:43:00,759
Speaker 1: the Bary dealers of the world. He's just another guy.

811
00:43:00,800 --> 00:43:02,360
Speaker 1: I just think of Barry Diller, and I think that

812
00:43:02,400 --> 00:43:05,319
Speaker 1: guy understands options as well as anybody out there, right

813
00:43:05,719 --> 00:43:08,480
Speaker 1: and for the businesses, so there there's certain executives that

814
00:43:08,520 --> 00:43:10,920
Speaker 1: tend to do it, really because he's managed to assemble

815
00:43:11,000 --> 00:43:15,000
Speaker 1: a conglomerate of very disparate businesses that all seem to

816
00:43:15,160 --> 00:43:19,360
Speaker 1: work very well in his portfolio. For lack of a

817
00:43:19,400 --> 00:43:22,839
Speaker 1: better better word. Um, but he's not running a mutual fund.

818
00:43:22,920 --> 00:43:26,120
Speaker 1: He's running a real operating business. So that's how you

819
00:43:26,120 --> 00:43:31,480
Speaker 1: would define optionality. Real optionality is identifying those opportunities and

820
00:43:31,520 --> 00:43:34,000
Speaker 1: then taking advantage of it. I think that's right. And

821
00:43:34,480 --> 00:43:36,440
Speaker 1: he's been doing this for a long time. By the way,

822
00:43:36,480 --> 00:43:38,160
Speaker 1: this is not like a recent thing. This for a

823
00:43:38,200 --> 00:43:41,560
Speaker 1: long time. So by the way, I previously had one

824
00:43:41,600 --> 00:43:44,040
Speaker 1: of your colleagues on as a guest. Dennis Lynch was

825
00:43:44,160 --> 00:43:49,719
Speaker 1: quite fascinating. He's really an interesting individual. Dennis is awesome. Yeah,

826
00:43:49,880 --> 00:43:52,120
Speaker 1: not just is not only a great investor, but a

827
00:43:52,160 --> 00:43:54,919
Speaker 1: great a great guy. And this is these are things

828
00:43:54,920 --> 00:43:56,880
Speaker 1: that tend to get underestimated, by the way, is that

829
00:43:57,960 --> 00:44:01,120
Speaker 1: organizational cultures are really important. And you know, he's just

830
00:44:01,239 --> 00:44:04,160
Speaker 1: created an environment that I think is is about a

831
00:44:04,200 --> 00:44:07,000
Speaker 1: good environment for an investment organization as possible, and that's

832
00:44:07,160 --> 00:44:10,200
Speaker 1: very challenging to do. People don't realize how hard, especially

833
00:44:10,360 --> 00:44:14,200
Speaker 1: during COVID and everybody remote, maintaining that is really difficult.

834
00:44:14,400 --> 00:44:16,480
Speaker 1: I agree, and I think part of it is that,

835
00:44:17,160 --> 00:44:19,440
Speaker 1: I mean, there are two things that I particularly admire.

836
00:44:19,520 --> 00:44:23,279
Speaker 1: One is there's a clear drive towards learning, being a

837
00:44:23,360 --> 00:44:26,520
Speaker 1: learning organization, so there's a premium on people thinking and

838
00:44:26,600 --> 00:44:29,239
Speaker 1: learning and so forth. And second is I think he

839
00:44:29,280 --> 00:44:31,400
Speaker 1: thinks a lot about trying to put people in a

840
00:44:31,440 --> 00:44:34,200
Speaker 1: position to be as effective as they can be, so

841
00:44:34,239 --> 00:44:36,440
Speaker 1: putting people in a position to do what they do

842
00:44:36,520 --> 00:44:40,239
Speaker 1: well and what they're passionate about. And yeah, great, great guy,

843
00:44:40,320 --> 00:44:42,359
Speaker 1: and I loved I mean, I love that episode, by

844
00:44:42,360 --> 00:44:44,680
Speaker 1: the way, and I think that he's he's really and

845
00:44:44,680 --> 00:44:46,479
Speaker 1: he doesn't and he doesn't do a lot of those things.

846
00:44:46,520 --> 00:44:48,560
Speaker 1: So it's great for you. That was No, that was

847
00:44:48,600 --> 00:44:50,640
Speaker 1: a lot of That was a lot of fun. Um.

848
00:44:50,680 --> 00:44:54,320
Speaker 1: So let's talk a little bit about market efficiency. Uh.

849
00:44:54,480 --> 00:44:57,400
Speaker 1: One of the questions that when we were kicking around

850
00:44:57,400 --> 00:45:00,359
Speaker 1: some ideas for this is have the markets got more

851
00:45:00,360 --> 00:45:04,600
Speaker 1: efficient over the last twenty years, given given the speed

852
00:45:04,640 --> 00:45:09,920
Speaker 1: information moves around, given the integration of technology and unofficial intelligence,

853
00:45:10,520 --> 00:45:14,360
Speaker 1: is the market the same market as the two thousand

854
00:45:14,360 --> 00:45:16,600
Speaker 1: and one market? Yeah? I think the answer those questions

855
00:45:16,640 --> 00:45:19,759
Speaker 1: are always know that they're not the same markets, and

856
00:45:19,800 --> 00:45:23,640
Speaker 1: they're almost always grinding toward more efficiency. Right. So, and

857
00:45:23,680 --> 00:45:27,520
Speaker 1: I think that if you did one versus one and

858
00:45:27,560 --> 00:45:29,680
Speaker 1: go back over time, right and for all the reasons

859
00:45:29,719 --> 00:45:34,239
Speaker 1: you decided that information is is nearly cost costless to

860
00:45:34,520 --> 00:45:38,319
Speaker 1: acquire and so forth. Now, um, the one thing else

861
00:45:38,360 --> 00:45:41,680
Speaker 1: to say that And and I, by the way, I

862
00:45:41,680 --> 00:45:46,480
Speaker 1: am very enthusiastic about systematic strategies and quantitatives tools. I

863
00:45:46,520 --> 00:45:49,400
Speaker 1: think these are all things that even as discretionary investors,

864
00:45:49,400 --> 00:45:52,000
Speaker 1: we need to integrate these things in a very thoughtful way.

865
00:45:52,480 --> 00:45:56,719
Speaker 1: All that said that, in active management, the notion of

866
00:45:56,800 --> 00:45:59,400
Speaker 1: judgment is not going to go away anytime soon, and

867
00:45:59,480 --> 00:46:03,040
Speaker 1: so and and judgment as distinct from like I'm just

868
00:46:03,120 --> 00:46:05,920
Speaker 1: forecasting or you know that kind of stuff. But judgments

869
00:46:05,960 --> 00:46:08,000
Speaker 1: are not going to go away, and so we need that.

870
00:46:08,560 --> 00:46:11,600
Speaker 1: So when you think about in the very short term,

871
00:46:11,920 --> 00:46:14,920
Speaker 1: so short term trading, were systematic strategies are just gonna

872
00:46:14,920 --> 00:46:17,120
Speaker 1: be They're just so powerful. You know, if tomorrow is

873
00:46:17,160 --> 00:46:19,000
Speaker 1: gonna be a lot like today, or day after tomorrow

874
00:46:19,000 --> 00:46:21,719
Speaker 1: a lot like today, systematic things are gonna are going

875
00:46:21,760 --> 00:46:24,520
Speaker 1: to be much better than humans and those kinds of environments.

876
00:46:25,000 --> 00:46:26,759
Speaker 1: But again, if you look out five or ten or

877
00:46:26,800 --> 00:46:29,480
Speaker 1: fifteen years, and we talked before about these patterns of

878
00:46:29,520 --> 00:46:32,960
Speaker 1: how industries evolve and so forth, machines have a very

879
00:46:32,960 --> 00:46:35,839
Speaker 1: difficult time understanding those kinds of things, and humans can

880
00:46:35,880 --> 00:46:38,600
Speaker 1: be I think a little bit more thoughtful about understanding

881
00:46:39,239 --> 00:46:42,319
Speaker 1: who might win, who might lose for what reasons, things

882
00:46:42,360 --> 00:46:45,239
Speaker 1: like measuring culture and so on and so forth. So yeah,

883
00:46:45,239 --> 00:46:47,919
Speaker 1: I mean, it's always it's always a tough game, and uh,

884
00:46:48,880 --> 00:46:50,759
Speaker 1: I just don't see it's gonna be getting a ton

885
00:46:51,040 --> 00:46:55,600
Speaker 1: ton easier over time. So so the markets becoming more efficient,

886
00:46:56,760 --> 00:46:59,719
Speaker 1: Let me, let me ask that slightly differently? Are are

887
00:46:59,760 --> 00:47:04,359
Speaker 1: we quicker today to discount the future? So and and

888
00:47:04,400 --> 00:47:08,560
Speaker 1: we briefly talked about, uh, the electric vehicle makers. If

889
00:47:08,600 --> 00:47:11,560
Speaker 1: we're looking at Lucid and Tesla and Rivian in those

890
00:47:11,600 --> 00:47:16,480
Speaker 1: companies footnote A hundred years ago, there were ninety different

891
00:47:16,560 --> 00:47:19,879
Speaker 1: automobile companies before we were left with five and then

892
00:47:20,000 --> 00:47:23,319
Speaker 1: three and now in the US and now um, here

893
00:47:23,360 --> 00:47:26,400
Speaker 1: we are with everybody rolling out e vs and lots

894
00:47:26,640 --> 00:47:32,160
Speaker 1: of new EV companies coming out. Are we better able

895
00:47:32,200 --> 00:47:35,440
Speaker 1: to look at this group and say, oh, yeah, eventually

896
00:47:35,440 --> 00:47:38,360
Speaker 1: everything's gonna be e V and therefore these companies have value?

897
00:47:39,000 --> 00:47:41,759
Speaker 1: Or are these closer to the meme stocks? And and

898
00:47:41,800 --> 00:47:45,080
Speaker 1: I'm not asking for an opinion about any specific company,

899
00:47:45,120 --> 00:47:51,160
Speaker 1: I mean, how are investors generalizing with any of these sectors?

900
00:47:51,200 --> 00:47:54,000
Speaker 1: But we can use evs as an example. I mean,

901
00:47:54,360 --> 00:47:56,560
Speaker 1: it's a great question baring and a few things come

902
00:47:56,560 --> 00:47:58,879
Speaker 1: to mind. Um, the first thing I should mention, there's

903
00:47:58,880 --> 00:48:01,920
Speaker 1: a fairly recent academic paper. This is within the last

904
00:48:01,960 --> 00:48:04,439
Speaker 1: few months, and we can maybe posted on the show

905
00:48:04,480 --> 00:48:06,960
Speaker 1: notes or something to this effect. And it was its

906
00:48:06,960 --> 00:48:10,239
Speaker 1: studied about ten thousand I p o s since nine

907
00:48:12,360 --> 00:48:15,040
Speaker 1: and and then it actually went and tracked the future

908
00:48:15,080 --> 00:48:17,560
Speaker 1: earnings and discounted them back to a present value and

909
00:48:17,600 --> 00:48:19,920
Speaker 1: said how close was the I p O price to

910
00:48:20,040 --> 00:48:24,080
Speaker 1: the actual performance of the business over time? And it

911
00:48:24,120 --> 00:48:28,320
Speaker 1: turns out, I mean probably not shockingly. Is it on average?

912
00:48:28,400 --> 00:48:32,920
Speaker 1: It was about right? But there is massive variants and

913
00:48:33,000 --> 00:48:36,759
Speaker 1: there is massive skew. Right. So the answer is the

914
00:48:36,800 --> 00:48:39,200
Speaker 1: market tends to get this broadly speaking, but for any

915
00:48:39,200 --> 00:48:44,400
Speaker 1: particular company, uh, not particularly well. So that's my first comment, say,

916
00:48:44,239 --> 00:48:46,680
Speaker 1: I will enter all this into with some some degree

917
00:48:46,680 --> 00:48:49,600
Speaker 1: of humility. The second thing is there's a concept and

918
00:48:49,640 --> 00:48:51,120
Speaker 1: we talked about in this the book as well, but

919
00:48:51,120 --> 00:48:54,000
Speaker 1: it's a very well known concept of reflexivity, right, And

920
00:48:54,040 --> 00:48:58,840
Speaker 1: so we tend to think about fundamentals and price action

921
00:48:59,000 --> 00:49:01,759
Speaker 1: as two separate things. Right, So people always draw that,

922
00:49:01,760 --> 00:49:03,919
Speaker 1: you know, the prices that thing that's squiggling all over

923
00:49:03,920 --> 00:49:06,920
Speaker 1: the place, and fundamentals is things sort applauds along, you know,

924
00:49:07,000 --> 00:49:10,040
Speaker 1: and and so prices sort of chasing around. Um. What

925
00:49:10,080 --> 00:49:12,120
Speaker 1: George Soros and many others have talked about in the

926
00:49:12,120 --> 00:49:15,040
Speaker 1: concept of reflectivity is these two things feed back to

927
00:49:15,120 --> 00:49:17,400
Speaker 1: one another. So the very fact that when price is

928
00:49:17,480 --> 00:49:20,279
Speaker 1: up and a company can sell equity that allows it

929
00:49:20,400 --> 00:49:23,000
Speaker 1: resources to pursue fundamentals in a way that it may

930
00:49:23,000 --> 00:49:25,440
Speaker 1: not have been able to otherwise. Right, and so on

931
00:49:25,480 --> 00:49:27,160
Speaker 1: and so forth. Right, and by the way, the positive

932
00:49:27,160 --> 00:49:29,120
Speaker 1: feedback works on the way up, and it also couldn't

933
00:49:29,160 --> 00:49:31,279
Speaker 1: work on the way down, just to be clear. So

934
00:49:31,320 --> 00:49:34,080
Speaker 1: I think even in electric vehicles we've seen a big

935
00:49:34,160 --> 00:49:38,279
Speaker 1: dose of this reflexivity. And then the other thing that

936
00:49:38,360 --> 00:49:40,799
Speaker 1: makes it this all very complicated is what's going on

937
00:49:40,840 --> 00:49:44,399
Speaker 1: with interest rates and discount rates, right, and just I'll

938
00:49:44,400 --> 00:49:47,200
Speaker 1: just spend one moment to explain this, because it's actually

939
00:49:47,280 --> 00:49:49,279
Speaker 1: quite interesting if you go back and look at the

940
00:49:49,360 --> 00:49:52,480
Speaker 1: history of interest rates, so on the X axis you

941
00:49:52,480 --> 00:49:55,759
Speaker 1: would draw, you know, the history of real interest rates,

942
00:49:55,800 --> 00:49:58,880
Speaker 1: so adjusted for inflation. And then on the y axis,

943
00:49:58,920 --> 00:50:00,840
Speaker 1: the price earning is multip so we can use like

944
00:50:00,840 --> 00:50:04,440
Speaker 1: a Chiller cyclically adjusted price earnings multiple as an example.

945
00:50:04,880 --> 00:50:07,920
Speaker 1: If you plot what PE multiples do, it's actually an

946
00:50:07,960 --> 00:50:11,239
Speaker 1: inverted you. So saying this differently, high interest rates are

947
00:50:11,239 --> 00:50:15,440
Speaker 1: associated with low multiples. That's that makes sense. Kind of

948
00:50:15,480 --> 00:50:19,480
Speaker 1: medium ones are associated with higher multiples. Yeah, that seems okay,

949
00:50:19,520 --> 00:50:22,120
Speaker 1: but you would expect this to be a continued linear relationship.

950
00:50:22,200 --> 00:50:25,000
Speaker 1: So the low interest rates effective really high multiples, and

951
00:50:25,000 --> 00:50:27,920
Speaker 1: in fact that's how it happened. Occurs back down and

952
00:50:28,040 --> 00:50:33,160
Speaker 1: low interest rates are again effectively low uh low price

953
00:50:33,160 --> 00:50:36,040
Speaker 1: earnings multiples. So what's going on here? And the answer

954
00:50:36,160 --> 00:50:40,600
Speaker 1: is usually historically low interest rates have been associated with

955
00:50:40,680 --> 00:50:45,319
Speaker 1: sluggish growth because the fet is usually cutting rates in

956
00:50:45,400 --> 00:50:48,719
Speaker 1: response to a succession. It's it's when you look at

957
00:50:48,719 --> 00:50:52,680
Speaker 1: the causal relationship, how you end up at low rates. Well,

958
00:50:52,719 --> 00:50:55,640
Speaker 1: in this case it's following the financial crisis or a pandemic,

959
00:50:55,920 --> 00:50:59,040
Speaker 1: but historically most cases are because the economy is in

960
00:50:59,120 --> 00:51:01,280
Speaker 1: the test sluggish, and go back to the late nineteen

961
00:51:01,320 --> 00:51:04,200
Speaker 1: thirties and early nineteen forties was another episode of hilary

962
00:51:04,200 --> 00:51:07,239
Speaker 1: low very low discarnage. So so the argument here is

963
00:51:07,280 --> 00:51:10,879
Speaker 1: if you have companies that can grow strongly through this

964
00:51:10,960 --> 00:51:14,320
Speaker 1: low interest rate or low discount rate environment, they actually

965
00:51:14,320 --> 00:51:17,080
Speaker 1: get the double positives, right. One is the growth actually

966
00:51:17,200 --> 00:51:19,759
Speaker 1: they do put up the numbers, and second is they

967
00:51:19,760 --> 00:51:22,399
Speaker 1: get the benefit of a low discount rate. So so

968
00:51:22,520 --> 00:51:24,680
Speaker 1: it's a combination. You sort of throw those things into

969
00:51:24,719 --> 00:51:26,520
Speaker 1: the mix and you get sort of these these sort

970
00:51:26,520 --> 00:51:30,080
Speaker 1: of somewhat weird outcum but but again, um, early whenever

971
00:51:30,120 --> 00:51:32,239
Speaker 1: you're in early days, as we talked about before, for

972
00:51:32,280 --> 00:51:34,600
Speaker 1: electrical vehicles or anything else, when you're in early days,

973
00:51:34,640 --> 00:51:37,680
Speaker 1: you're gonna there's there's there's a lot of jocking around

974
00:51:37,680 --> 00:51:41,080
Speaker 1: for a position, and it's often not crystal clear who

975
00:51:41,120 --> 00:51:43,839
Speaker 1: the ultimate winners or losers will be. So so let's

976
00:51:43,840 --> 00:51:45,719
Speaker 1: talk a little bit about share buy backs. You have

977
00:51:45,760 --> 00:51:50,640
Speaker 1: a whole chapter on that. There is some controversy about

978
00:51:50,680 --> 00:51:53,960
Speaker 1: share buy backs. Some people like them, some people think

979
00:51:54,000 --> 00:51:57,919
Speaker 1: it's a waste of money. Um, I'm gonna disclose my take.

980
00:51:58,520 --> 00:52:01,040
Speaker 1: I think as long as they were showing share buy backs,

981
00:52:01,520 --> 00:52:04,080
Speaker 1: it creates an advantage for the most of the companies

982
00:52:04,120 --> 00:52:06,920
Speaker 1: doing the share buy backs, first of the companies that can't.

983
00:52:07,440 --> 00:52:11,319
Speaker 1: Whether that's a cause or effect is arguable. Hey, if

984
00:52:11,360 --> 00:52:13,200
Speaker 1: you can't afford to do the share buy backs, you're

985
00:52:13,200 --> 00:52:17,200
Speaker 1: probably doing something else wrong. But that said, tell us

986
00:52:17,200 --> 00:52:20,080
Speaker 1: a little bit about share buy backs, what's the significance

987
00:52:20,120 --> 00:52:23,759
Speaker 1: of them, and what does it mean for performance? But

988
00:52:23,960 --> 00:52:26,600
Speaker 1: by the way, I just can't get over this controversial

989
00:52:27,120 --> 00:52:29,600
Speaker 1: for some reason. I don't I just don't understand why

990
00:52:29,680 --> 00:52:31,960
Speaker 1: people seem so flummix by this issue because It seems

991
00:52:32,000 --> 00:52:35,080
Speaker 1: pretty straightforward to me. Because of the anecdotes. There are terrible,

992
00:52:35,280 --> 00:52:40,160
Speaker 1: terrible anecdotes. General Electric bought a ton of of stock

993
00:52:40,200 --> 00:52:42,759
Speaker 1: back on its way towards this, and I could give

994
00:52:42,760 --> 00:52:47,880
Speaker 1: you a dozen other memorable examples, but that's a little

995
00:52:47,880 --> 00:52:51,680
Speaker 1: bit of availability bias. You know, we we that's what

996
00:52:51,719 --> 00:52:55,319
Speaker 1: we've seen, and so people always tried out the worst anecdote.

997
00:52:55,680 --> 00:53:00,000
Speaker 1: I think that my favorite anecdote is Dell spent more

998
00:53:00,040 --> 00:53:03,560
Speaker 1: money when it was a freestanding public company on stock

999
00:53:03,640 --> 00:53:07,520
Speaker 1: buy backs. Then they earned in profits their entire existence

1000
00:53:07,640 --> 00:53:10,319
Speaker 1: up until they were take in private, you know, a

1001
00:53:10,360 --> 00:53:13,200
Speaker 1: decade ago. Well, now now we're going back to the

1002
00:53:13,280 --> 00:53:16,120
Speaker 1: intangible argument and there versus their cash lows. By the way,

1003
00:53:16,160 --> 00:53:17,719
Speaker 1: can I tell I'll just tell a little maybe this

1004
00:53:17,760 --> 00:53:19,239
Speaker 1: a little bit out of school, but it's okay, I'll

1005
00:53:19,239 --> 00:53:22,720
Speaker 1: tell this a little lot of school story. So, um,

1006
00:53:22,760 --> 00:53:26,640
Speaker 1: in two thousand and ten, I was invited to give

1007
00:53:26,680 --> 00:53:29,880
Speaker 1: a talk to the senior executive team at General Electric,

1008
00:53:32,600 --> 00:53:35,400
Speaker 1: and uh, this is right on the heels of the

1009
00:53:35,400 --> 00:53:38,560
Speaker 1: financial crisis, right, so this is a near death experience, right,

1010
00:53:38,680 --> 00:53:41,360
Speaker 1: especially for GEF the financial services division and so and

1011
00:53:41,400 --> 00:53:43,680
Speaker 1: so forth. Right, So, this is not a good you know,

1012
00:53:43,719 --> 00:53:48,000
Speaker 1: it's a very challenging time. And um, I don't wear

1013
00:53:48,000 --> 00:53:50,160
Speaker 1: the stock was at the time is all pre split stuff,

1014
00:53:50,160 --> 00:53:53,160
Speaker 1: but it was probably in the low teen something like that, right,

1015
00:53:53,760 --> 00:53:56,440
Speaker 1: And um so I'm getting a cup of coffee before

1016
00:53:56,480 --> 00:53:59,800
Speaker 1: my presentation, and I bumped into the chief financial officer

1017
00:54:00,040 --> 00:54:04,000
Speaker 1: and he says, Man, the worst thing and idea that

1018
00:54:04,120 --> 00:54:07,680
Speaker 1: we did is we bought backstock in the thirties. And

1019
00:54:07,719 --> 00:54:10,600
Speaker 1: I looked at him directly and nice, like, dude, You've

1020
00:54:10,600 --> 00:54:12,840
Speaker 1: done a lot worse stuff than that, right, And so

1021
00:54:13,000 --> 00:54:17,160
Speaker 1: you might say, Okay, it's not the accounting and all

1022
00:54:17,200 --> 00:54:20,120
Speaker 1: the everything energy capital. It was the share of what

1023
00:54:20,480 --> 00:54:22,719
Speaker 1: is even all that now? And I just said, leaving

1024
00:54:22,760 --> 00:54:25,120
Speaker 1: aside all that stuff, Actually, I thought my my thought

1025
00:54:25,520 --> 00:54:27,080
Speaker 1: was going on the back of my head was much

1026
00:54:27,080 --> 00:54:30,239
Speaker 1: more about broader capital allocation and M and A activity

1027
00:54:30,640 --> 00:54:32,040
Speaker 1: M and A like what they bought and what they

1028
00:54:32,040 --> 00:54:34,279
Speaker 1: sold versus the buybacks and what they didn't buy, the

1029
00:54:34,320 --> 00:54:37,520
Speaker 1: opportunities they chose to pass on. So let me let

1030
00:54:37,560 --> 00:54:39,879
Speaker 1: me okay, if I'm allowed to nerd out just a bit.

1031
00:54:40,000 --> 00:54:42,680
Speaker 1: First of all, there there is some there should be

1032
00:54:42,719 --> 00:54:46,120
Speaker 1: some people should have some psychological equivalence between dividends and

1033
00:54:46,120 --> 00:54:49,480
Speaker 1: buy backs and and and execution. They're different and distinct,

1034
00:54:49,920 --> 00:54:52,920
Speaker 1: and if done properly, buy backs can be slightly beneficial

1035
00:54:53,000 --> 00:54:55,279
Speaker 1: relative to dividends. But let's just say that these are

1036
00:54:55,320 --> 00:54:59,080
Speaker 1: a mechanism return capital the shareholders, albeit only those people

1037
00:54:59,120 --> 00:55:01,360
Speaker 1: who are sellers are willing to take it. So here's

1038
00:55:01,400 --> 00:55:03,960
Speaker 1: here's my nerd my nerd out moment, which is I

1039
00:55:04,000 --> 00:55:06,840
Speaker 1: called the value conservation concept, and this is really the

1040
00:55:06,920 --> 00:55:09,640
Speaker 1: key point. So let's say you have a company that's

1041
00:55:09,640 --> 00:55:11,719
Speaker 1: worth a thousand I'm just making this up, and you

1042
00:55:11,760 --> 00:55:14,759
Speaker 1: have you know, X number of shares outstanding, and they

1043
00:55:14,800 --> 00:55:17,480
Speaker 1: decide they're going to return two hundred dollars to shareholders,

1044
00:55:17,640 --> 00:55:19,400
Speaker 1: right of the thousand, two hundreds going to go to

1045
00:55:19,400 --> 00:55:22,279
Speaker 1: shahoulders um. By the way, they could it could be

1046
00:55:22,280 --> 00:55:23,719
Speaker 1: a dividend, it could be a buy back, it could

1047
00:55:23,760 --> 00:55:25,279
Speaker 1: be anything. It could be they could burn the cash

1048
00:55:25,280 --> 00:55:27,400
Speaker 1: in the parking lot. Right. So the point is that

1049
00:55:27,480 --> 00:55:30,080
Speaker 1: the value of the firm after this is executed will

1050
00:55:30,120 --> 00:55:33,239
Speaker 1: go from a thousand to eight hundred period. Right. Again,

1051
00:55:33,239 --> 00:55:34,720
Speaker 1: it doesn't matter what they did with the doing, it's

1052
00:55:34,719 --> 00:55:36,680
Speaker 1: gonna go from a thousand to eight hundreds. Okay, so

1053
00:55:36,719 --> 00:55:39,480
Speaker 1: now let's walk through three scenarios. One scenario is the

1054
00:55:39,520 --> 00:55:43,400
Speaker 1: stock is over valued. Right, Let's pretend that was the

1055
00:55:43,440 --> 00:55:46,680
Speaker 1: g E situation. The stock is overvalued, so they buy

1056
00:55:46,680 --> 00:55:50,200
Speaker 1: back over valued stock. Well, the value the firm we

1057
00:55:50,320 --> 00:55:53,319
Speaker 1: just established goes from a thousand to eight hundred. That

1058
00:55:53,360 --> 00:55:58,319
Speaker 1: doesn't change. What does change is the relative positioning of

1059
00:55:58,360 --> 00:56:01,440
Speaker 1: the selling shoulders versus going shoulders. In this case, the

1060
00:56:01,480 --> 00:56:04,320
Speaker 1: selling shoulders are getting in quotes more than they should

1061
00:56:04,880 --> 00:56:08,759
Speaker 1: so they're benefiting and the ongoing shoulders are suffering. Right,

1062
00:56:08,760 --> 00:56:11,319
Speaker 1: so they're getting their per share value just went down, right,

1063
00:56:11,320 --> 00:56:13,000
Speaker 1: And we can demonstrate, and we actually do in the book.

1064
00:56:13,000 --> 00:56:16,239
Speaker 1: We can demonstrate that mathematicad scenario too is a buy

1065
00:56:16,239 --> 00:56:19,759
Speaker 1: back undervalued stock. Right, So what's happened now is the

1066
00:56:19,800 --> 00:56:22,320
Speaker 1: sellers are getting less than what they're supposed to be getting,

1067
00:56:22,320 --> 00:56:24,560
Speaker 1: so they're in a sense getting ripped off. And what's

1068
00:56:24,640 --> 00:56:27,400
Speaker 1: left is the per share value goes up, the interns

1069
00:56:27,400 --> 00:56:30,960
Speaker 1: of guide goes up for the ongoing shareholders. Right, So

1070
00:56:31,160 --> 00:56:32,920
Speaker 1: one of the points always make the money managers I

1071
00:56:33,120 --> 00:56:35,760
Speaker 1: as I said, well, I presume you own stocks because

1072
00:56:35,760 --> 00:56:39,640
Speaker 1: you believe that they're undervalued. Is that a fair assessment.

1073
00:56:40,320 --> 00:56:43,320
Speaker 1: If the answer to that is yes, then you always

1074
00:56:43,360 --> 00:56:46,160
Speaker 1: want companies to buy back stock because the presumption is

1075
00:56:46,200 --> 00:56:48,839
Speaker 1: somebody is selling for too low a price and your

1076
00:56:48,880 --> 00:56:50,959
Speaker 1: per share value is going to go up. Now, Diven,

1077
00:56:51,040 --> 00:56:52,759
Speaker 1: end of course, just goes to everybody and leaving a

1078
00:56:52,840 --> 00:56:57,399
Speaker 1: side tax effects, everyone gets treated completely equally. So now

1079
00:56:57,400 --> 00:56:59,000
Speaker 1: what we talked about in the book is this thing

1080
00:56:59,040 --> 00:57:01,440
Speaker 1: called the Golden rule buy backs, which basically says you

1081
00:57:01,440 --> 00:57:03,440
Speaker 1: should buy back of stock only one is below fair

1082
00:57:03,520 --> 00:57:08,160
Speaker 1: value and basically all other operational initiatives have been met. Right.

1083
00:57:08,480 --> 00:57:10,759
Speaker 1: And again, Barry, I'll just let you put on your

1084
00:57:10,880 --> 00:57:13,200
Speaker 1: sort of owner of a business hat as well. You

1085
00:57:13,239 --> 00:57:16,000
Speaker 1: probably think to yourself, all right, we've got financials. What

1086
00:57:16,040 --> 00:57:17,880
Speaker 1: I want to do is invest in all the ways

1087
00:57:17,880 --> 00:57:20,120
Speaker 1: in the business that I think would add value to

1088
00:57:20,160 --> 00:57:23,640
Speaker 1: our organization. And then there's something left after left over

1089
00:57:23,680 --> 00:57:25,680
Speaker 1: after all that, then we'll think about what to do

1090
00:57:25,720 --> 00:57:28,840
Speaker 1: with that money. Right, But your first inclination is let's

1091
00:57:28,880 --> 00:57:32,120
Speaker 1: invest back into ways to build value for our our

1092
00:57:32,120 --> 00:57:35,560
Speaker 1: long term value for our franchise. Right, So so to me,

1093
00:57:35,800 --> 00:57:38,000
Speaker 1: uh okay, And then the last thing I'll say about

1094
00:57:38,000 --> 00:57:40,400
Speaker 1: buy backs versus dividends, which is really interesting and I

1095
00:57:40,480 --> 00:57:43,640
Speaker 1: think this is a very real thing, which is it's

1096
00:57:43,680 --> 00:57:47,240
Speaker 1: a completely different psychological thing for executives. So when they

1097
00:57:47,280 --> 00:57:50,840
Speaker 1: pay a dividend, they deem that to be a quasi contract,

1098
00:57:51,080 --> 00:57:54,120
Speaker 1: and they are loath to cut dividends. They want to

1099
00:57:54,240 --> 00:57:57,080
Speaker 1: raise the dividend, and the dividend is the sacred thing

1100
00:57:57,200 --> 00:58:00,880
Speaker 1: that we want to preserve at all costs. As a consequence,

1101
00:58:00,920 --> 00:58:02,320
Speaker 1: by the way, if you look at a long term

1102
00:58:02,400 --> 00:58:05,760
Speaker 1: series of dividends versus other capital allocation things like M

1103
00:58:05,840 --> 00:58:09,360
Speaker 1: and A or CAPEX, where dividends are really stable series,

1104
00:58:09,360 --> 00:58:11,439
Speaker 1: I mean they do go down in recessions and so forth,

1105
00:58:11,440 --> 00:58:14,160
Speaker 1: but it's a super smooth series, right, because companies are

1106
00:58:14,240 --> 00:58:17,120
Speaker 1: really reticent to to cut them in there, and they're

1107
00:58:17,160 --> 00:58:20,960
Speaker 1: pretty conservative about growing them. By contrast, buy backs are

1108
00:58:21,000 --> 00:58:23,760
Speaker 1: deemed to be sort of this residual. Right. Yeah, we

1109
00:58:23,840 --> 00:58:26,200
Speaker 1: paid all our bills, we made all the investments. We like,

1110
00:58:26,360 --> 00:58:28,760
Speaker 1: we got some money sitting around. What do we do

1111
00:58:28,840 --> 00:58:31,960
Speaker 1: with it. Let's buy back stock, right, and so the

1112
00:58:31,960 --> 00:58:35,240
Speaker 1: the the volatility of buy backs from one year to

1113
00:58:35,240 --> 00:58:38,320
Speaker 1: the next. So, so we went through COVID. What was

1114
00:58:38,360 --> 00:58:40,720
Speaker 1: the first thing that got cut. It wasn't dividends. I mean,

1115
00:58:40,760 --> 00:58:43,480
Speaker 1: dividends went down, but it was it was buy backs

1116
00:58:43,520 --> 00:58:45,680
Speaker 1: went from a lot to very little in a very

1117
00:58:45,680 --> 00:58:49,200
Speaker 1: short period time. Someone did research showing that something like

1118
00:58:50,320 --> 00:58:54,520
Speaker 1: announced by backs are executed, a big swath never get

1119
00:58:54,680 --> 00:58:59,040
Speaker 1: completed because the world intervenes and sometimes you can't do

1120
00:58:59,120 --> 00:59:01,120
Speaker 1: what you said you were gonna do. Now that one

1121
00:59:01,160 --> 00:59:03,040
Speaker 1: we I checked that because that was true. That was

1122
00:59:03,280 --> 00:59:05,840
Speaker 1: that was true back in the nine nineties. That is

1123
00:59:05,920 --> 00:59:08,720
Speaker 1: not really in the United States. In the United States,

1124
00:59:08,760 --> 00:59:11,400
Speaker 1: most pro programs get executed. But that was that was

1125
00:59:11,480 --> 00:59:14,240
Speaker 1: a true thing, and it's more true internationally than it

1126
00:59:14,320 --> 00:59:17,439
Speaker 1: is in the United States. Yeah. So so to me

1127
00:59:17,760 --> 00:59:19,120
Speaker 1: and bo Way, this is a whole another thing going

1128
00:59:19,120 --> 00:59:20,560
Speaker 1: back to how to think about markets. I mean, I

1129
00:59:20,760 --> 00:59:23,040
Speaker 1: actually think one of the very simple and many quantitative

1130
00:59:23,080 --> 00:59:24,919
Speaker 1: guys do this, but one of the very simplistic ways

1131
00:59:24,920 --> 00:59:27,760
Speaker 1: to think about markets is simply take take the SMPI,

1132
00:59:28,160 --> 00:59:31,040
Speaker 1: for instance, take the dividend yield plus the buy back

1133
00:59:31,160 --> 00:59:35,200
Speaker 1: yield and quotation marks, and then um and then that

1134
00:59:35,200 --> 00:59:37,640
Speaker 1: that yield ends up being your return. Now, the last

1135
00:59:37,680 --> 00:59:39,480
Speaker 1: thing I'll say before I don't want to there's an

1136
00:59:39,480 --> 00:59:42,360
Speaker 1: e T f for that sharehold of value. That's a

1137
00:59:42,440 --> 00:59:44,720
Speaker 1: that's a good one. So the last thing lest I

1138
00:59:44,720 --> 00:59:47,600
Speaker 1: come across is totally like favorable by buy backs. Let

1139
00:59:47,640 --> 00:59:49,440
Speaker 1: me just say, and you alluded to Dell. I mean,

1140
00:59:49,560 --> 00:59:53,360
Speaker 1: there are companies of buy back stock for the wrong reasons, right,

1141
00:59:53,720 --> 00:59:55,960
Speaker 1: and so the wrong reason would be something like to

1142
00:59:56,440 --> 01:00:00,400
Speaker 1: increase earnings per share or two offset to ocean from

1143
01:00:00,440 --> 01:00:03,080
Speaker 1: stock based compensation programs, and so and so from those

1144
01:00:03,120 --> 01:00:06,120
Speaker 1: net aren't necessarily bad, but those are not the proper motivation.

1145
01:00:06,160 --> 01:00:09,280
Speaker 1: The last one, it seems that because of the way

1146
01:00:09,320 --> 01:00:12,720
Speaker 1: we treat stock options as a sort of non cost

1147
01:00:13,720 --> 01:00:17,480
Speaker 1: and it takes real capital buy back shares to make

1148
01:00:17,560 --> 01:00:21,120
Speaker 1: up for that, it kind of can mislead investors or

1149
01:00:21,120 --> 01:00:24,120
Speaker 1: it looks like you're hiding this dilutive thing you're doing

1150
01:00:24,120 --> 01:00:27,880
Speaker 1: in a way that isn't always transparent. And some companies

1151
01:00:27,920 --> 01:00:30,080
Speaker 1: have been more egregious than all, and I agree with that.

1152
01:00:30,840 --> 01:00:34,480
Speaker 1: So I would just say that the stock based compensation discussion,

1153
01:00:34,640 --> 01:00:37,840
Speaker 1: and by the way, some SPC programs are great and

1154
01:00:37,880 --> 01:00:39,600
Speaker 1: others are not as good and so forth, so there's

1155
01:00:39,640 --> 01:00:43,080
Speaker 1: no one size fits all. But that's a separate discussion

1156
01:00:43,120 --> 01:00:45,040
Speaker 1: from the buy back discussion. So I would just say

1157
01:00:45,080 --> 01:00:47,720
Speaker 1: that those two things should not be intermingled with to

1158
01:00:47,760 --> 01:00:50,480
Speaker 1: one another. And I think your observation is exactly correct.

1159
01:00:50,480 --> 01:00:52,480
Speaker 1: They're are sometimes brought together in a way that may

1160
01:00:52,480 --> 01:00:56,360
Speaker 1: not be productive. H But but you were gonna say

1161
01:00:56,440 --> 01:00:59,160
Speaker 1: one more thing about stock buy backs, which generally is

1162
01:00:59,760 --> 01:01:03,400
Speaker 1: you think it's a net positive for for shareholder price. Well,

1163
01:01:03,400 --> 01:01:06,160
Speaker 1: I think that that's uh, that's an empirical question which

1164
01:01:06,160 --> 01:01:08,560
Speaker 1: has been answered for decades. So the answer is that

1165
01:01:08,640 --> 01:01:11,120
Speaker 1: is yes. And and by the way, people have this

1166
01:01:11,120 --> 01:01:13,320
Speaker 1: perception companies pie back stock when they're high and they

1167
01:01:13,680 --> 01:01:15,560
Speaker 1: don't buy it when it's low. That's actually not true.

1168
01:01:15,560 --> 01:01:19,200
Speaker 1: I mean, because I remember in oh eight No. Nine,

1169
01:01:19,680 --> 01:01:22,760
Speaker 1: nobody was announcing stock by backs, but I do have

1170
01:01:22,880 --> 01:01:27,440
Speaker 1: a vivid recollection of everybody was buying backs. Yeah, but

1171
01:01:27,480 --> 01:01:29,280
Speaker 1: if you do the numbers, the aggriant numbers are actually

1172
01:01:29,280 --> 01:01:31,480
Speaker 1: pretty good. And by the way, it's this is now

1173
01:01:31,560 --> 01:01:35,640
Speaker 1: not another a very big macro comment, which is companies

1174
01:01:35,680 --> 01:01:37,960
Speaker 1: are pretty good at selling stock when it's high and

1175
01:01:38,040 --> 01:01:42,280
Speaker 1: buying it when it's bad when it's cheap. Posatively, I mean, empirically,

1176
01:01:42,320 --> 01:01:44,320
Speaker 1: we know that for a fact. That's so they're pretty good.

1177
01:01:44,320 --> 01:01:46,880
Speaker 1: So when they're retiring act, Yes, that's that's an interesting

1178
01:01:46,920 --> 01:01:49,280
Speaker 1: And by the way, that the reason I bring that

1179
01:01:49,360 --> 01:01:51,800
Speaker 1: up and why that's important is that when we talk

1180
01:01:51,840 --> 01:01:55,120
Speaker 1: about alpha, for example, alpha is a measure of excess returns,

1181
01:01:55,560 --> 01:01:57,880
Speaker 1: and of course it nets to zero by definition within

1182
01:01:57,920 --> 01:01:59,919
Speaker 1: a closed group of the problems. Markets are not clos

1183
01:02:00,000 --> 01:02:02,360
Speaker 1: post right, They're actually open, and they're open that there

1184
01:02:02,360 --> 01:02:06,200
Speaker 1: are other entities interacting, and the biggest other entity interacting

1185
01:02:06,280 --> 01:02:09,640
Speaker 1: is actually corporation. So if companies are selling expensive and

1186
01:02:09,720 --> 01:02:13,320
Speaker 1: buying cheap, that means there's companies are gathering somebody alpha.

1187
01:02:13,840 --> 01:02:15,480
Speaker 1: So it's funny you say that, because I'm trying to

1188
01:02:15,520 --> 01:02:17,760
Speaker 1: remember which quant said it, and I don't want to

1189
01:02:17,800 --> 01:02:21,360
Speaker 1: put words into Cliff Fastness his mouth. Somebody had written

1190
01:02:21,880 --> 01:02:24,720
Speaker 1: stock buy backs are legal insider trade, and you know

1191
01:02:24,840 --> 01:02:27,480
Speaker 1: how well the company is going to do. So if

1192
01:02:27,560 --> 01:02:31,400
Speaker 1: you're selling stock, it's you're less confident. And if you're

1193
01:02:31,440 --> 01:02:35,560
Speaker 1: a buyer, as a corporate entity, you should be doing

1194
01:02:35,600 --> 01:02:38,640
Speaker 1: so because you think the company's future prospects are bright

1195
01:02:38,720 --> 01:02:40,760
Speaker 1: and you know exactly why. Yeah. And by the way,

1196
01:02:40,760 --> 01:02:42,200
Speaker 1: the on thing I'll just mentioned is it just to

1197
01:02:42,240 --> 01:02:44,760
Speaker 1: be clear that buy backs were, I mean they're there.

1198
01:02:44,760 --> 01:02:47,200
Speaker 1: There are histories like Telen's very famous for having brought

1199
01:02:47,240 --> 01:02:49,160
Speaker 1: back stock in the nineteen seventies and so forth. But

1200
01:02:49,520 --> 01:02:52,840
Speaker 1: buy backs were the wild West in the nineteen seventies, right,

1201
01:02:52,880 --> 01:02:55,560
Speaker 1: because you could be you could be charged with stock

1202
01:02:55,560 --> 01:02:58,640
Speaker 1: price manipulation. So the s the SEC put in a

1203
01:02:58,680 --> 01:03:02,040
Speaker 1: safe harbor provision in nine teen eight two, so there's

1204
01:03:02,080 --> 01:03:04,160
Speaker 1: no real discussion. And by the way, people don't know

1205
01:03:04,280 --> 01:03:06,440
Speaker 1: right at the start of the best ball market, right

1206
01:03:07,480 --> 01:03:09,600
Speaker 1: is a really important day because you if you're thinking

1207
01:03:09,600 --> 01:03:12,880
Speaker 1: about returning capital to shareholders and you're thinking about the

1208
01:03:12,920 --> 01:03:16,640
Speaker 1: complete dividend plus buy back picks nothing before. There's no

1209
01:03:16,720 --> 01:03:19,160
Speaker 1: comparability before and after eight two. So the safe harbord

1210
01:03:19,200 --> 01:03:22,680
Speaker 1: is important. So so even if companies have a symmetric information,

1211
01:03:22,720 --> 01:03:26,200
Speaker 1: the safe harbor assures that if they execute their trades

1212
01:03:26,240 --> 01:03:28,520
Speaker 1: in a certain way with particular volumes and stakes and

1213
01:03:28,520 --> 01:03:31,000
Speaker 1: all that, hence the legal insider hence they're good to go.

1214
01:03:31,400 --> 01:03:34,360
Speaker 1: They listen. Who better to know a company's own prospect

1215
01:03:34,400 --> 01:03:38,640
Speaker 1: than the management of the company. And that's probably a

1216
01:03:38,640 --> 01:03:42,840
Speaker 1: reason why quantz like buy backs and a lot of

1217
01:03:42,880 --> 01:03:48,320
Speaker 1: people track insider buying and corporate buy backs, because theoretically

1218
01:03:48,720 --> 01:03:51,520
Speaker 1: there should be a pretty solid signal in there. Absolutely,

1219
01:03:52,080 --> 01:03:56,040
Speaker 1: So we talked about Shopify, we talked about Domino's I

1220
01:03:56,120 --> 01:04:00,760
Speaker 1: want to bring corporate management back to something you out

1221
01:04:00,800 --> 01:04:04,720
Speaker 1: and in a previous book, the paradox of skill, which

1222
01:04:04,760 --> 01:04:08,560
Speaker 1: states the higher level the level of competition, the more

1223
01:04:08,800 --> 01:04:13,240
Speaker 1: luck improves events. Now we know how that works in sports.

1224
01:04:13,960 --> 01:04:16,240
Speaker 1: How does that work in investing? How does that work

1225
01:04:16,280 --> 01:04:19,360
Speaker 1: in business? Well, I mean I think it works across

1226
01:04:19,360 --> 01:04:22,160
Speaker 1: all these domains and just too And I'll just restate

1227
01:04:22,160 --> 01:04:23,880
Speaker 1: what you just said. The paradox of skill. Seys and

1228
01:04:23,960 --> 01:04:26,480
Speaker 1: activities were both skill and luck and tribute to outcomes

1229
01:04:27,000 --> 01:04:32,640
Speaker 1: most things. As skill improves, luck becomes a more important

1230
01:04:32,640 --> 01:04:34,600
Speaker 1: determinant of the outcome. So that doesn't seem to make

1231
01:04:34,640 --> 01:04:36,480
Speaker 1: any sense. And so the key is to think about

1232
01:04:36,520 --> 01:04:41,160
Speaker 1: skill in two different dimensions. The first dimensions absolute skill.

1233
01:04:41,840 --> 01:04:43,360
Speaker 1: And I think we'd all agree, and that was really

1234
01:04:43,360 --> 01:04:45,440
Speaker 1: our comment about market efficiency. We look around the world,

1235
01:04:45,480 --> 01:04:47,560
Speaker 1: I mean, just look at the technology at our fingertips,

1236
01:04:47,600 --> 01:04:50,560
Speaker 1: that best practices of training for athletes and so over.

1237
01:04:50,760 --> 01:04:52,960
Speaker 1: I mean, I think we'd all agree without a doubt

1238
01:04:53,040 --> 01:04:55,760
Speaker 1: that it's as good as it's ever been in terms

1239
01:04:55,760 --> 01:04:59,000
Speaker 1: of how we are absolute levels of skill. And we

1240
01:04:59,040 --> 01:05:01,760
Speaker 1: can document that when we measure things versus a clock,

1241
01:05:02,000 --> 01:05:04,160
Speaker 1: so for example of running and so and so forth.

1242
01:05:05,360 --> 01:05:07,360
Speaker 1: But the second dimension is really the key one for

1243
01:05:07,440 --> 01:05:11,120
Speaker 1: the paradox of skill, which is a relative skill, and

1244
01:05:11,120 --> 01:05:13,120
Speaker 1: the paradise the key to the whole paradox, as it

1245
01:05:13,120 --> 01:05:16,080
Speaker 1: says something like when what has happened over time is

1246
01:05:16,120 --> 01:05:19,120
Speaker 1: the difference between the very best and the average shrinks

1247
01:05:19,160 --> 01:05:22,320
Speaker 1: over time, so everyone becomes a little bit closer to

1248
01:05:22,320 --> 01:05:25,680
Speaker 1: one another. Standard deviation of performance shrinks over time, and

1249
01:05:25,760 --> 01:05:28,400
Speaker 1: so um, you know, you know, because there you're a

1250
01:05:28,400 --> 01:05:30,760
Speaker 1: car guy, right, so you might you might appreciate this,

1251
01:05:30,800 --> 01:05:33,280
Speaker 1: but you know my understanding because I read one or

1252
01:05:33,320 --> 01:05:36,240
Speaker 1: two papers about this. But you know, the differential in

1253
01:05:36,360 --> 01:05:39,960
Speaker 1: the quality of car finishes in the nineteen sixties and seventies,

1254
01:05:40,000 --> 01:05:42,360
Speaker 1: I guess, was very high. So you know, you bought

1255
01:05:42,360 --> 01:05:47,600
Speaker 1: a Mercedes Benz it really was a better, right bank

1256
01:05:48,960 --> 01:05:53,240
Speaker 1: versus you bought some other cheaper right exactly. And so

1257
01:05:53,440 --> 01:05:55,760
Speaker 1: now it turns out that you can buy pretty much

1258
01:05:55,760 --> 01:05:57,800
Speaker 1: any car. They may not they may not be bank vaults,

1259
01:05:57,840 --> 01:06:00,840
Speaker 1: but they're all pretty well put together. And they're finishes

1260
01:06:00,880 --> 01:06:02,480
Speaker 1: are all pretty good, and so on and so forth.

1261
01:06:02,480 --> 01:06:04,520
Speaker 1: So there's a good example of how you might envision

1262
01:06:05,040 --> 01:06:07,640
Speaker 1: how that's changed over time. So I think, yeah, in

1263
01:06:07,760 --> 01:06:10,000
Speaker 1: terms of you just when if you want applied to

1264
01:06:10,040 --> 01:06:14,000
Speaker 1: businesses best practices and businesses tend to be embraced until autuma.

1265
01:06:14,040 --> 01:06:18,040
Speaker 1: Manufacturing might be one example that managerial best practices tend

1266
01:06:18,080 --> 01:06:21,440
Speaker 1: to find their ways and to keep people's minds training

1267
01:06:21,440 --> 01:06:23,840
Speaker 1: through school, business schools and so on and so forth.

1268
01:06:24,120 --> 01:06:26,400
Speaker 1: So yeah, I think broad broadly speaking, this is a

1269
01:06:27,400 --> 01:06:31,040
Speaker 1: universal concert. So let me have you addressed two examples

1270
01:06:31,040 --> 01:06:35,840
Speaker 1: of this that I think are instructive. One is allocators

1271
01:06:35,840 --> 01:06:40,120
Speaker 1: of capital. If you're in the investment management business, there

1272
01:06:40,200 --> 01:06:45,480
Speaker 1: really shouldn't be giant outliers and performance over long periods

1273
01:06:45,480 --> 01:06:47,320
Speaker 1: of time. There might be over a couple of quarters

1274
01:06:47,360 --> 01:06:50,160
Speaker 1: or even a year, but for the most part, and

1275
01:06:50,200 --> 01:06:52,520
Speaker 1: we've seen some of this over the past couple of years,

1276
01:06:52,760 --> 01:06:55,800
Speaker 1: a few funds have exploded, done really well, some some

1277
01:06:55,880 --> 01:06:58,200
Speaker 1: hedge funds that have been all in on crypto, some

1278
01:06:58,320 --> 01:07:01,640
Speaker 1: people that were way early to the work at home

1279
01:07:01,960 --> 01:07:06,400
Speaker 1: work from homestocks or Tesla or what have you. Is

1280
01:07:06,400 --> 01:07:10,080
Speaker 1: the expectation when you see like somebody leading the pack

1281
01:07:10,200 --> 01:07:14,160
Speaker 1: by an extraordinary amount that eventually that just mean reverts

1282
01:07:14,240 --> 01:07:17,120
Speaker 1: and and there's a degree of lucky timing. I'm not

1283
01:07:17,160 --> 01:07:22,760
Speaker 1: even saying luck, but just lucky timing versus skill. How

1284
01:07:22,760 --> 01:07:25,440
Speaker 1: do we look at outliers and market performance? Yeah, I

1285
01:07:25,480 --> 01:07:27,440
Speaker 1: mean part of the the the answer if you're if

1286
01:07:27,480 --> 01:07:31,440
Speaker 1: you're trying to avoid getting uh consumed by the paradox

1287
01:07:31,440 --> 01:07:33,880
Speaker 1: of skill in other words, where is the answer is

1288
01:07:33,920 --> 01:07:36,840
Speaker 1: to look for easy games? Right, So you're if you

1289
01:07:36,880 --> 01:07:39,560
Speaker 1: think you're a skillful person, you don't want to compete

1290
01:07:39,600 --> 01:07:43,560
Speaker 1: with other super skillful people. You want to find games

1291
01:07:43,600 --> 01:07:46,880
Speaker 1: where your skill tends to be the highest. Right, so

1292
01:07:47,360 --> 01:07:49,840
Speaker 1: you know you're Annie Duke. Instead of playing with a

1293
01:07:49,960 --> 01:07:52,640
Speaker 1: high stakes table, maybe you play at the next leg

1294
01:07:52,760 --> 01:07:57,400
Speaker 1: stakes down where your profit per hours higher because your

1295
01:07:57,440 --> 01:07:59,600
Speaker 1: skill differentials higher. So you may be making less money,

1296
01:07:59,600 --> 01:08:02,520
Speaker 1: but you're higher skill differential. And so that might be

1297
01:08:02,720 --> 01:08:05,320
Speaker 1: that this is the interesting uh sort of almost come

1298
01:08:05,360 --> 01:08:08,480
Speaker 1: back to that idea that when there are new markets

1299
01:08:08,560 --> 01:08:11,360
Speaker 1: that open up, it can be the case that lots

1300
01:08:11,360 --> 01:08:13,080
Speaker 1: of people are sitting at the table. Right. If you

1301
01:08:13,120 --> 01:08:16,000
Speaker 1: wanted to continue with this poker analogy, lots of different

1302
01:08:16,000 --> 01:08:18,920
Speaker 1: people with varied skills are sitting at the tables, some

1303
01:08:19,000 --> 01:08:21,120
Speaker 1: of who know what's going on in others who don't,

1304
01:08:21,520 --> 01:08:24,120
Speaker 1: and that those those those can be actually easy games,

1305
01:08:24,280 --> 01:08:26,439
Speaker 1: and that's an opportunity set that you're not gonna get

1306
01:08:26,560 --> 01:08:28,800
Speaker 1: if you're only buying sp FI fund, that's right. And

1307
01:08:28,800 --> 01:08:31,479
Speaker 1: then that those those eventually get tightened up, right, so

1308
01:08:31,520 --> 01:08:34,040
Speaker 1: the people that are not the losers end up losing

1309
01:08:34,080 --> 01:08:36,479
Speaker 1: money and then they leave the table for for whatever reason,

1310
01:08:36,720 --> 01:08:39,519
Speaker 1: and then those seats get filled by more skillful players

1311
01:08:39,520 --> 01:08:41,599
Speaker 1: and so on and so forth. So eventually that gets

1312
01:08:41,640 --> 01:08:45,400
Speaker 1: sorted out. But you there's there's a long history of markets,

1313
01:08:45,439 --> 01:08:49,080
Speaker 1: even as we've evolved toward more efficient broadly speaking, where

1314
01:08:49,080 --> 01:08:52,120
Speaker 1: they're these easy games have popped up, and you know,

1315
01:08:52,520 --> 01:08:54,439
Speaker 1: even like I was talking to Buddy Mine, who was

1316
01:08:54,439 --> 01:08:57,599
Speaker 1: an early options trader, and he's like, you know, they

1317
01:08:57,680 --> 01:09:00,840
Speaker 1: was like, it's like the eight is like the eighties, Yeah,

1318
01:09:00,840 --> 01:09:03,240
Speaker 1: the nineteen eighties, And he's like, oh, they introduced options

1319
01:09:03,280 --> 01:09:06,439
Speaker 1: on certain commodities, right, and they hadn't traded them before

1320
01:09:06,439 --> 01:09:08,880
Speaker 1: in this particular exchange. He's like going, and he's like,

1321
01:09:09,439 --> 01:09:11,479
Speaker 1: so people start putting up you know, bids and offers

1322
01:09:11,479 --> 01:09:13,080
Speaker 1: for things, and he's like, you start figuring out he

1323
01:09:13,080 --> 01:09:15,439
Speaker 1: could put he could put on like these costless spreads

1324
01:09:15,479 --> 01:09:19,519
Speaker 1: at a guaranteed profit, and he's like, this doesn't make

1325
01:09:19,520 --> 01:09:21,720
Speaker 1: any sense. It's not gonna last very long. But like

1326
01:09:22,000 --> 01:09:24,160
Speaker 1: my HP twelve C works. And I remember, I'm just

1327
01:09:24,200 --> 01:09:26,320
Speaker 1: take advantage of it while I while I get it right.

1328
01:09:26,360 --> 01:09:28,920
Speaker 1: So part of the answer, I think that the response,

1329
01:09:29,000 --> 01:09:31,519
Speaker 1: the careful response, would be something like, don't don't say

1330
01:09:31,560 --> 01:09:35,160
Speaker 1: like these people are automatically just lucky people. The question

1331
01:09:35,200 --> 01:09:37,920
Speaker 1: is are they playing in an easy game where they

1332
01:09:38,360 --> 01:09:40,519
Speaker 1: bring something to the table that others don't have. They've

1333
01:09:40,600 --> 01:09:43,800
Speaker 1: identified the opportunity and I'm taking advantage of it. Now.

1334
01:09:43,800 --> 01:09:46,519
Speaker 1: The problem with the problem with easy games, generally speaking,

1335
01:09:46,600 --> 01:09:48,920
Speaker 1: is that the last well, ay, they don't last, but

1336
01:09:49,000 --> 01:09:51,000
Speaker 1: be if they do last, they tend not to be

1337
01:09:51,080 --> 01:09:54,040
Speaker 1: super scalable. So in other words, it's often you can't

1338
01:09:54,080 --> 01:09:56,599
Speaker 1: make billions and billions and billions of dollars in an

1339
01:09:56,600 --> 01:09:58,760
Speaker 1: easy game because people you know figure it out, so

1340
01:09:59,080 --> 01:10:02,000
Speaker 1: they usually tend to be smaller and nicheer and so forth.

1341
01:10:02,160 --> 01:10:03,960
Speaker 1: I mean, I remember I was talking to a quant

1342
01:10:03,960 --> 01:10:06,960
Speaker 1: firm based in London, and they said that there they

1343
01:10:06,960 --> 01:10:11,920
Speaker 1: had some little strategy that was dealing with Chinese retail investors,

1344
01:10:12,320 --> 01:10:14,759
Speaker 1: and they said, this thing is like a little money machine,

1345
01:10:14,840 --> 01:10:17,360
Speaker 1: like every day we money on this thing, he goes,

1346
01:10:17,400 --> 01:10:20,240
Speaker 1: but little it just can't. We just can't do it

1347
01:10:20,280 --> 01:10:22,240
Speaker 1: in bigger size. But it's such a nice little thing.

1348
01:10:22,240 --> 01:10:25,120
Speaker 1: We just let it keep going, buddy like. But but

1349
01:10:25,200 --> 01:10:28,160
Speaker 1: it's it's just like, you know, steady, steady drum beat

1350
01:10:28,160 --> 01:10:31,120
Speaker 1: of process. So we see that all the time when

1351
01:10:32,360 --> 01:10:36,080
Speaker 1: a firm returns capital to their limited partners and say

1352
01:10:36,320 --> 01:10:40,840
Speaker 1: they say, hey, we have enough money to mind this inefficiency,

1353
01:10:40,880 --> 01:10:43,679
Speaker 1: but it's not big enough to share with other people.

1354
01:10:44,240 --> 01:10:48,360
Speaker 1: And the exceptions when people try and push the envelope

1355
01:10:48,960 --> 01:10:51,639
Speaker 1: is you get a long term capital management situation where

1356
01:10:51,640 --> 01:10:55,439
Speaker 1: they leveraged up these inefficiencies and eventually the chickens come

1357
01:10:55,439 --> 01:10:59,799
Speaker 1: home to roost. So let's put aside the investment side

1358
01:11:00,000 --> 01:11:04,760
Speaker 1: of luck versus skill and talk about how does this

1359
01:11:04,840 --> 01:11:08,599
Speaker 1: manifest in business. You you have you have lots of

1360
01:11:08,680 --> 01:11:12,840
Speaker 1: professional consultant companies mackenzie and go down the list. You

1361
01:11:12,920 --> 01:11:17,679
Speaker 1: have all these great business schools turning out these nbas

1362
01:11:17,720 --> 01:11:21,320
Speaker 1: and these j ds that are super smart, super insightful.

1363
01:11:21,439 --> 01:11:25,439
Speaker 1: They're they're steeped in all the wisdom of the business

1364
01:11:25,439 --> 01:11:29,160
Speaker 1: cases that have happened into that environment. Do we really

1365
01:11:29,240 --> 01:11:32,040
Speaker 1: see luck playing a role in what companies end up

1366
01:11:32,600 --> 01:11:36,320
Speaker 1: being successful? Maybe for a few quarters or years? Um

1367
01:11:36,640 --> 01:11:40,000
Speaker 1: does that happen? Oh? Yeah, I mean I think for sure.

1368
01:11:40,479 --> 01:11:43,560
Speaker 1: It's actually funny because I are participated in an academic

1369
01:11:43,640 --> 01:11:47,360
Speaker 1: seminar where like like a non academic but academic seminar,

1370
01:11:47,439 --> 01:11:49,760
Speaker 1: and it was literally about this exact topic, which is

1371
01:11:49,760 --> 01:11:52,519
Speaker 1: how much what is the roles skill and luck in

1372
01:11:52,920 --> 01:11:56,240
Speaker 1: UM corporate strategy setting? And and interestingly there is an

1373
01:11:56,240 --> 01:11:58,600
Speaker 1: academic that took this luck side and academic took the

1374
01:11:58,640 --> 01:12:01,080
Speaker 1: skill side. And you know, answer, of course is somewhere

1375
01:12:01,080 --> 01:12:03,679
Speaker 1: in between those two. I think your question you're asking

1376
01:12:03,720 --> 01:12:05,439
Speaker 1: is a slightly more subtle one, which is has it

1377
01:12:05,560 --> 01:12:08,280
Speaker 1: changed over time? And I think that the answer is um.

1378
01:12:08,320 --> 01:12:13,439
Speaker 1: That again as right, that's the basic principle is there's

1379
01:12:13,439 --> 01:12:16,760
Speaker 1: more uniformity in the skill levels it becomes. Now. Look,

1380
01:12:16,800 --> 01:12:22,360
Speaker 1: the answer is for many famous business breakthroughs, and clearly

1381
01:12:22,439 --> 01:12:25,719
Speaker 1: this is very true for example, drug development, that almost

1382
01:12:25,760 --> 01:12:27,400
Speaker 1: seems like it's I mean those are I mean, I

1383
01:12:27,439 --> 01:12:29,840
Speaker 1: don't call them luck, but we'd say that there's a

1384
01:12:29,880 --> 01:12:33,679
Speaker 1: high degree of randomness and it's not exactly And so

1385
01:12:33,960 --> 01:12:36,840
Speaker 1: you know, you think about although, to be fair, some

1386
01:12:36,920 --> 01:12:40,160
Speaker 1: of the new technologies the I forgot what it's called

1387
01:12:40,240 --> 01:12:44,120
Speaker 1: the nose on a chip that allows the testing of

1388
01:12:44,240 --> 01:12:49,639
Speaker 1: these jillion variations using semiconductors and software rather than RNA.

1389
01:12:49,720 --> 01:12:52,920
Speaker 1: What about RNA was an amazing You think about that's

1390
01:12:52,960 --> 01:12:55,519
Speaker 1: a decade plus in the making. Two people think it's

1391
01:12:55,560 --> 01:12:59,000
Speaker 1: a recent discovery. It's almost twenty years. That's amazing, Like

1392
01:12:59,040 --> 01:13:01,479
Speaker 1: you said, twenty years of and d sort of ready

1393
01:13:01,680 --> 01:13:04,040
Speaker 1: like all dressed up with no place to go just yet.

1394
01:13:04,040 --> 01:13:06,080
Speaker 1: And then when it became time to go, it went.

1395
01:13:06,520 --> 01:13:08,679
Speaker 1: And by the way, that thing, I mean, my understanding

1396
01:13:08,720 --> 01:13:10,479
Speaker 1: is that these guys had that thing ready to go

1397
01:13:10,640 --> 01:13:14,280
Speaker 1: probably a couple of years ago, March of April four,

1398
01:13:14,400 --> 01:13:19,040
Speaker 1: specifically March of April and other words, it was ready

1399
01:13:19,080 --> 01:13:21,960
Speaker 1: to go, like almost right away, which is astounding. It's

1400
01:13:22,200 --> 01:13:23,880
Speaker 1: you know, there's one o the thing I'll just add

1401
01:13:23,960 --> 01:13:25,680
Speaker 1: Barry that you know, and we wrote a piece. It

1402
01:13:25,720 --> 01:13:28,120
Speaker 1: was in a slightly different context, but the piece was

1403
01:13:28,120 --> 01:13:30,240
Speaker 1: called Turn and Face the Strange, and it was about

1404
01:13:30,280 --> 01:13:34,000
Speaker 1: why organizations are slow to change. By the way, the

1405
01:13:34,040 --> 01:13:37,519
Speaker 1: inspiration for that piece was actually a presentation that Dick

1406
01:13:37,560 --> 01:13:41,519
Speaker 1: Taylor gave at the m I. T. Sloan Sports Analytics Conference.

1407
01:13:41,560 --> 01:13:42,880
Speaker 1: So this is going to seem a little bit weird

1408
01:13:42,880 --> 01:13:46,639
Speaker 1: that a behavioral economist talking into sports conference. But Dick's

1409
01:13:46,640 --> 01:13:49,200
Speaker 1: point was something like this, which is, there are certain

1410
01:13:49,240 --> 01:13:52,640
Speaker 1: things that we know work analytically is sports, You know,

1411
01:13:52,720 --> 01:13:55,960
Speaker 1: the three point shot, going forward on fourth down, stuff

1412
01:13:56,040 --> 01:13:59,599
Speaker 1: like that, and uh, it seemed to have taken forever

1413
01:14:00,040 --> 01:14:04,040
Speaker 1: ever for teams to actually embrace. And the question is

1414
01:14:04,080 --> 01:14:08,920
Speaker 1: why don't teams do it much faster? So this is

1415
01:14:08,960 --> 01:14:10,880
Speaker 1: an interesting one where they're there, you know, we know

1416
01:14:11,000 --> 01:14:14,920
Speaker 1: that it works in quotes, we know and um, and

1417
01:14:15,000 --> 01:14:17,960
Speaker 1: yet the answer is it's not part of the conventional wisdom,

1418
01:14:18,040 --> 01:14:20,160
Speaker 1: and it's not part of the coaching guild. It's not

1419
01:14:20,240 --> 01:14:22,479
Speaker 1: what you learn as a player when you've grown up.

1420
01:14:22,960 --> 01:14:25,479
Speaker 1: And so it takes almost a generation or two for

1421
01:14:25,560 --> 01:14:27,560
Speaker 1: these things to get woven. And now, now if you

1422
01:14:27,640 --> 01:14:29,800
Speaker 1: watch the NFL going forward on fourth down for the

1423
01:14:29,840 --> 01:14:32,200
Speaker 1: most part, and it's not it's for the most part,

1424
01:14:32,200 --> 01:14:35,479
Speaker 1: but it's happening much much more frequently so people have

1425
01:14:35,520 --> 01:14:37,759
Speaker 1: gotten the memo on these things, especially the younger coaches

1426
01:14:37,800 --> 01:14:43,360
Speaker 1: and so forth. The interesting story UM about Taylor is

1427
01:14:43,400 --> 01:14:47,519
Speaker 1: when Michael Lewis's money Ball came out, Taylor sends Lewis

1428
01:14:47,520 --> 01:14:51,200
Speaker 1: an email or letter saying, Hey, you're talking about Knomen

1429
01:14:51,280 --> 01:14:54,880
Speaker 1: and Diversky's work. This is all behavioral finance, which is

1430
01:14:54,920 --> 01:14:59,160
Speaker 1: what eventually led to Lewis's book ten years later. Uh.

1431
01:14:59,200 --> 01:15:02,880
Speaker 1: The undoing part objects because he didn't realize he had

1432
01:15:02,960 --> 01:15:06,519
Speaker 1: really written a work, a book about their work. Kind

1433
01:15:06,520 --> 01:15:10,479
Speaker 1: of fascinating. And even with money Ball, it still took

1434
01:15:11,040 --> 01:15:16,000
Speaker 1: a decade or two UM for teams like the Boston

1435
01:15:16,080 --> 01:15:20,719
Speaker 1: Red Sox to start using things that Lewis had written

1436
01:15:20,760 --> 01:15:23,880
Speaker 1: about years and years before and ultimately led the Red

1437
01:15:23,920 --> 01:15:27,400
Speaker 1: Sox to a championship. You know, it's interesting. My understanding

1438
01:15:27,439 --> 01:15:30,880
Speaker 1: is that UM, Danny and Thinking and writing Thinking Fast

1439
01:15:30,920 --> 01:15:33,679
Speaker 1: and Slow, which came out about a decade ago, talked

1440
01:15:33,720 --> 01:15:38,040
Speaker 1: to a number of other writers to potentially collaborate with

1441
01:15:38,120 --> 01:15:41,000
Speaker 1: him on that. One was Jason's Why, and I think

1442
01:15:41,040 --> 01:15:44,000
Speaker 1: Jason's edited about three quoters of Jason I spent thinking

1443
01:15:44,080 --> 01:15:46,200
Speaker 1: that thinks about a long time with him on it.

1444
01:15:46,560 --> 01:15:49,680
Speaker 1: But the other one was Michael Lewis. Interestingly, so I

1445
01:15:49,680 --> 01:15:52,599
Speaker 1: think he actually spent a fair bit of time with

1446
01:15:52,640 --> 01:15:55,360
Speaker 1: Michael back when he was thinking about this, and I

1447
01:15:55,360 --> 01:15:57,120
Speaker 1: think he obviously went to ultimately and by the way,

1448
01:15:57,160 --> 01:15:59,640
Speaker 1: Danny is a beautiful writer, so I recounted writer to

1449
01:16:00,040 --> 01:16:01,760
Speaker 1: and ended up doing it on his own. But that's

1450
01:16:01,800 --> 01:16:04,960
Speaker 1: an interesting but I think, like you said, between the

1451
01:16:05,000 --> 01:16:08,080
Speaker 1: failure thing and spending time with Conum and himself, I

1452
01:16:08,120 --> 01:16:11,080
Speaker 1: think that that sort of made it clear to Michael Lewis,

1453
01:16:11,080 --> 01:16:15,000
Speaker 1: who's just, by the way, insanely I love that podcast

1454
01:16:15,000 --> 01:16:17,960
Speaker 1: you did with him too. He's just an insanely talented guy.

1455
01:16:18,040 --> 01:16:22,280
Speaker 1: And anyway, yeah, he's he's been a regular. Some of

1456
01:16:22,320 --> 01:16:26,879
Speaker 1: the stuff he's written about finance is just so unique

1457
01:16:27,080 --> 01:16:32,120
Speaker 1: and comes from such a special angle. There's there's nobody

1458
01:16:32,160 --> 01:16:38,559
Speaker 1: else who has his ability to identify the consensus, find

1459
01:16:38,640 --> 01:16:42,160
Speaker 1: the band of misfits that are challenging the consensus and

1460
01:16:42,200 --> 01:16:46,640
Speaker 1: are ultimately proven right. And it's just such a great story.

1461
01:16:46,360 --> 01:16:49,559
Speaker 1: It works so well whether you're talking about baseball or

1462
01:16:49,640 --> 01:16:54,640
Speaker 1: finance or um high high freakingcy trading, or like I

1463
01:16:54,720 --> 01:16:59,880
Speaker 1: love that in premonition, it's the Bush white House's strike

1464
01:17:00,280 --> 01:17:05,440
Speaker 1: team that essentially creates all of the uh COVID response

1465
01:17:05,680 --> 01:17:09,479
Speaker 1: that was ready to go, and we really this really

1466
01:17:09,600 --> 01:17:12,040
Speaker 1: didn't need to be three quarters of a million deaths.

1467
01:17:12,080 --> 01:17:16,479
Speaker 1: It's quite fascinating. Well, we are way off topic, and

1468
01:17:16,520 --> 01:17:21,080
Speaker 1: I've allowed you to allow me to, um disrupt myself.

1469
01:17:21,160 --> 01:17:24,799
Speaker 1: So why don't I jump since we're since we're already

1470
01:17:24,840 --> 01:17:27,320
Speaker 1: talking about all sorts of other things, why don't I

1471
01:17:27,360 --> 01:17:31,639
Speaker 1: jump to our favorite podcast questions? Um, and since we're

1472
01:17:31,680 --> 01:17:35,040
Speaker 1: talking about COVID, what have you been doing to keep

1473
01:17:35,080 --> 01:17:39,559
Speaker 1: yourself entertained during lockdown? Tell us what Netflix or Amazon

1474
01:17:39,680 --> 01:17:43,599
Speaker 1: Prime shows or podcasts you've been been staying busy with

1475
01:17:44,439 --> 01:17:49,800
Speaker 1: besides masters and business besides Yeah, um so so Bury

1476
01:17:49,880 --> 01:17:52,720
Speaker 1: My My, My confession is what you probably know is

1477
01:17:52,800 --> 01:17:55,120
Speaker 1: I don't watch a lot of tea and so I

1478
01:17:55,400 --> 01:17:57,680
Speaker 1: don't have any good answers on that. Um I do.

1479
01:17:57,800 --> 01:18:00,120
Speaker 1: I do enjoy podcasts, and you know, I probably the

1480
01:18:00,120 --> 01:18:03,240
Speaker 1: one I listened to the most frequently is um is

1481
01:18:03,280 --> 01:18:05,920
Speaker 1: Patrick O'Shaughnessy's invest like the best, and I think he's

1482
01:18:05,920 --> 01:18:08,200
Speaker 1: done it really nice. But I'm also a big fan

1483
01:18:08,240 --> 01:18:12,880
Speaker 1: of like Tyler Tyler Cowen's conversations with tyler Um, Russ Reynolds,

1484
01:18:13,080 --> 01:18:14,920
Speaker 1: Russ Roberts, part of me. Rus Roberts. By the way,

1485
01:18:14,960 --> 01:18:18,400
Speaker 1: Russ Roberts has been doing these sorts of interviews before

1486
01:18:18,439 --> 01:18:20,280
Speaker 1: any of us started. He's been doing it for like

1487
01:18:20,320 --> 01:18:22,519
Speaker 1: twenty years. So I'm fans of all those guys, but

1488
01:18:22,560 --> 01:18:25,040
Speaker 1: I usually go. I usually go if there's someone who

1489
01:18:25,040 --> 01:18:27,599
Speaker 1: pops up who I find to be an interesting guest.

1490
01:18:27,800 --> 01:18:30,559
Speaker 1: That's usually what motivates me to do that. So that's

1491
01:18:30,600 --> 01:18:32,519
Speaker 1: mostly what I do. And then I do. I have

1492
01:18:32,560 --> 01:18:35,160
Speaker 1: to say that I it's been an odd thing. I'm

1493
01:18:35,040 --> 01:18:37,040
Speaker 1: a I'm a fairly big sports fan, so I do

1494
01:18:37,200 --> 01:18:40,400
Speaker 1: enjoy watching sports. So I have to say that one

1495
01:18:40,439 --> 01:18:43,760
Speaker 1: for example, even the NFL, I've enjoyed it probably more

1496
01:18:43,840 --> 01:18:45,599
Speaker 1: this year than I have in a very long time,

1497
01:18:45,720 --> 01:18:49,080
Speaker 1: just for whatever reasons, you know, because there, I mean,

1498
01:18:49,120 --> 01:18:50,559
Speaker 1: they play at the games, but it was a very

1499
01:18:50,560 --> 01:18:52,639
Speaker 1: different environment. Just feels like it's a little bit back

1500
01:18:52,640 --> 01:18:55,280
Speaker 1: to normal, and I've really enjoyed that. So let's talk

1501
01:18:55,280 --> 01:19:00,360
Speaker 1: a little bit about your mentors who helped shape your career. Well,

1502
01:19:00,439 --> 01:19:01,920
Speaker 1: there are a number of them that come to mind.

1503
01:19:02,400 --> 01:19:04,760
Speaker 1: Many many of them are great. H Well, I'll rap

1504
01:19:04,760 --> 01:19:06,640
Speaker 1: report we've already talked about, and that I mean he

1505
01:19:06,640 --> 01:19:10,120
Speaker 1: would be sort of first and foremost, UM, and not

1506
01:19:10,240 --> 01:19:13,080
Speaker 1: just as someone who was a mentor and a teacher

1507
01:19:13,120 --> 01:19:16,320
Speaker 1: to me, but also a collaborator. And you know, so

1508
01:19:16,400 --> 01:19:20,720
Speaker 1: that that's the whole package. Bill Miller has to be

1509
01:19:20,840 --> 01:19:23,839
Speaker 1: up there. You know, Bill, Bill introduced me, for example,

1510
01:19:23,880 --> 01:19:25,800
Speaker 1: to the Santa Fe Institute. And I don't know if

1511
01:19:25,800 --> 01:19:29,639
Speaker 1: you saw this, but Bill Um recently made very large

1512
01:19:30,520 --> 01:19:33,519
Speaker 1: a million dollars, which is a transformational gift to that

1513
01:19:33,560 --> 01:19:36,479
Speaker 1: place because it's a relatively small place. And uh so

1514
01:19:36,520 --> 01:19:39,880
Speaker 1: that will ensure that sort of complexity science is on

1515
01:19:39,920 --> 01:19:42,080
Speaker 1: the scene for for for a long time to come,

1516
01:19:42,080 --> 01:19:45,320
Speaker 1: which was really extraordinary. But you know it's not just Um.

1517
01:19:45,360 --> 01:19:46,960
Speaker 1: I mean, I think Bill is another one of these guys.

1518
01:19:47,000 --> 01:19:52,400
Speaker 1: It has uh an insatiable intellectual curiosity, has a good

1519
01:19:52,479 --> 01:19:56,120
Speaker 1: has good taste for ideas UM and just a very

1520
01:19:56,120 --> 01:19:58,960
Speaker 1: thoughtful guy. Um. The two other come to mind, you know,

1521
01:19:59,040 --> 01:20:00,640
Speaker 1: it's gonna sun a little it on. One is a

1522
01:20:00,640 --> 01:20:03,479
Speaker 1: guy named Brady Dougan, who is was a former CEO

1523
01:20:03,520 --> 01:20:06,480
Speaker 1: of Credit sweetz Um now has his own firm. But Brady,

1524
01:20:06,560 --> 01:20:08,640
Speaker 1: when I was growing up in at equities in the

1525
01:20:09,280 --> 01:20:11,120
Speaker 1: Credit Sweeze back in the day. It was always a

1526
01:20:11,120 --> 01:20:13,519
Speaker 1: big supporter. And when I went back to Credit Switez

1527
01:20:13,520 --> 01:20:15,840
Speaker 1: in two thousand and thirteen, it was because he was

1528
01:20:15,920 --> 01:20:18,920
Speaker 1: there and he offered me a really exciting opportunity to

1529
01:20:18,920 --> 01:20:21,760
Speaker 1: do that. And probably the last guy I've mentioned is

1530
01:20:21,840 --> 01:20:24,559
Speaker 1: Dennis Lynch. You know, I think Dennis. You know, it's

1531
01:20:24,600 --> 01:20:26,759
Speaker 1: interesting as I was thinking about what to do next

1532
01:20:26,800 --> 01:20:28,640
Speaker 1: and just sat down with Dennis, and you know, he

1533
01:20:28,680 --> 01:20:31,040
Speaker 1: had been early reader of a lot of stuff we've done,

1534
01:20:31,080 --> 01:20:35,479
Speaker 1: and again creates an incredible environment for work. Every day

1535
01:20:35,560 --> 01:20:38,160
Speaker 1: is super exciting for me to get going. I can't

1536
01:20:38,160 --> 01:20:40,639
Speaker 1: wait to get going every single day. And as usual,

1537
01:20:40,720 --> 01:20:43,120
Speaker 1: I have a longer list of things to do than

1538
01:20:43,160 --> 01:20:45,360
Speaker 1: the things I can get done. It's just it's like

1539
01:20:45,400 --> 01:20:47,760
Speaker 1: one of these really fun sensations. So those are people

1540
01:20:47,800 --> 01:20:50,800
Speaker 1: I would mention that's fantastic. So I know you read

1541
01:20:50,840 --> 01:20:53,920
Speaker 1: a lot, So let's talk about what you're reading now

1542
01:20:54,040 --> 01:20:57,040
Speaker 1: and what some of your favorite books are. Oh Man alive,

1543
01:20:57,120 --> 01:20:59,400
Speaker 1: So I should be more prepared for this one. Uh

1544
01:20:59,479 --> 01:21:02,799
Speaker 1: and p Us. I normally shoot these questions over to people,

1545
01:21:03,280 --> 01:21:08,120
Speaker 1: but you've you've been through this fourth appearance, so I

1546
01:21:08,200 --> 01:21:10,920
Speaker 1: figured out, let me, let me just wing it. I

1547
01:21:11,000 --> 01:21:14,200
Speaker 1: know that's bad. So well, what I'm reading right now

1548
01:21:14,360 --> 01:21:17,759
Speaker 1: is actually two books simultaneously. Richard Rhodes has a new

1549
01:21:18,680 --> 01:21:23,400
Speaker 1: biography of E. O. Wilson, the very famous biologist and

1550
01:21:23,400 --> 01:21:26,960
Speaker 1: and you know Richard Rose a very famous, talented biographer.

1551
01:21:26,960 --> 01:21:29,920
Speaker 1: And it's just a beautiful book. And I'm familiar with

1552
01:21:29,960 --> 01:21:32,120
Speaker 1: el Wilson. By the way. El Wilson wrote a book

1553
01:21:32,160 --> 01:21:36,759
Speaker 1: called Consilience, which is you mentioned you keep saying consilient

1554
01:21:37,240 --> 01:21:39,960
Speaker 1: head of Concilient Research. And I assume every time everybody

1555
01:21:40,000 --> 01:21:41,200
Speaker 1: hears that, they go, I don't know what the heck

1556
01:21:41,240 --> 01:21:45,000
Speaker 1: he's talking about. So that's where let's connect that. We'll

1557
01:21:45,040 --> 01:21:48,720
Speaker 1: close that and we'll close that loop right there. And

1558
01:21:48,800 --> 01:21:52,040
Speaker 1: so I really enjoy that book. Um. I'm also reading

1559
01:21:52,080 --> 01:21:55,479
Speaker 1: a book now by Page Harden called The Genetic Lottery,

1560
01:21:55,760 --> 01:21:59,280
Speaker 1: So this is really about genetics and uh what we've

1561
01:21:59,360 --> 01:22:03,680
Speaker 1: learned about and edics, inequality. A couple of books I

1562
01:22:03,680 --> 01:22:06,519
Speaker 1: really enjoyed this year. Stephen Pinker's book on Rationality has

1563
01:22:06,560 --> 01:22:08,200
Speaker 1: been wonderful. I don't know if you read that or

1564
01:22:08,240 --> 01:22:11,880
Speaker 1: know it, but I was familiar with many of the

1565
01:22:11,920 --> 01:22:13,920
Speaker 1: ideas in there. So it wasn't like it was brand

1566
01:22:13,960 --> 01:22:17,160
Speaker 1: new stuff. But he presents ideas in a very clear

1567
01:22:17,240 --> 01:22:19,800
Speaker 1: and compelling way and ways actually would help me even

1568
01:22:19,840 --> 01:22:23,240
Speaker 1: pedagogically as a teacher to explain it to other people.

1569
01:22:23,640 --> 01:22:25,679
Speaker 1: And probably I would say my favorite book this year

1570
01:22:25,920 --> 01:22:30,080
Speaker 1: was a book by Fred Lodge of all On. It's

1571
01:22:30,080 --> 01:22:33,800
Speaker 1: a biography of JFK. So. He's embarked on a two

1572
01:22:33,960 --> 01:22:37,760
Speaker 1: part series on JFK. So this is the first one

1573
01:22:37,800 --> 01:22:40,000
Speaker 1: from the time he was born in v to when

1574
01:22:40,000 --> 01:22:42,840
Speaker 1: he wins the Senate in the nineteen fifties, and then

1575
01:22:42,840 --> 01:22:45,200
Speaker 1: the second piece will be obviously sort of packed in

1576
01:22:45,240 --> 01:22:48,479
Speaker 1: the last roughly eight or ten years of his life.

1577
01:22:48,960 --> 01:22:51,439
Speaker 1: But uh, I you know, and I obviously knew the

1578
01:22:51,439 --> 01:22:54,320
Speaker 1: basic profile of Kennedy, but it was I learned just

1579
01:22:54,360 --> 01:22:56,960
Speaker 1: a ton about him. I learned a ton about his family,

1580
01:22:57,439 --> 01:22:59,280
Speaker 1: which I found to be and it's just beautifully written.

1581
01:22:59,280 --> 01:23:01,120
Speaker 1: So I let you learned a lot about world history

1582
01:23:01,120 --> 01:23:02,519
Speaker 1: along the way, and so and so forth. So those

1583
01:23:02,520 --> 01:23:05,880
Speaker 1: are someone's el mentioned. So those last two scientists EO.

1584
01:23:05,920 --> 01:23:09,439
Speaker 1: Wilson A Life and Nature and then JFK. Coming of

1585
01:23:09,479 --> 01:23:12,519
Speaker 1: Age in the American Century. Um, those are the two

1586
01:23:12,560 --> 01:23:16,639
Speaker 1: books you just you just mentioned. So our final two questions,

1587
01:23:16,680 --> 01:23:19,000
Speaker 1: what sort of advice would you give to a recent

1588
01:23:19,080 --> 01:23:23,040
Speaker 1: college grad who was interested in a career in either

1589
01:23:23,160 --> 01:23:27,599
Speaker 1: investment management or a financial research Well, by the way,

1590
01:23:27,960 --> 01:23:29,800
Speaker 1: in many ways, it's a very exciting time. And when

1591
01:23:29,800 --> 01:23:32,519
Speaker 1: we talked about these intangibles and you know, from a

1592
01:23:32,640 --> 01:23:35,160
Speaker 1: point from the point of view understanding how those work

1593
01:23:35,240 --> 01:23:37,280
Speaker 1: and so forth, it it would be pretty exciting. But

1594
01:23:37,640 --> 01:23:39,840
Speaker 1: you know, the key for me is always too there.

1595
01:23:39,880 --> 01:23:42,519
Speaker 1: There's sort of two things and they're probably a little

1596
01:23:42,520 --> 01:23:45,080
Speaker 1: bit hokey, but the first thing is you really have

1597
01:23:45,200 --> 01:23:47,080
Speaker 1: to set out to try to learn as much as

1598
01:23:47,120 --> 01:23:49,320
Speaker 1: you can, and so a lot of that is reading

1599
01:23:49,320 --> 01:23:53,280
Speaker 1: and studying on your own. You know, whenever my kids

1600
01:23:53,320 --> 01:23:55,040
Speaker 1: graduated from college and I sort of give him a

1601
01:23:55,080 --> 01:23:58,280
Speaker 1: hug and say congratulations and then say recognized. Tomorrow morning

1602
01:23:58,320 --> 01:24:01,000
Speaker 1: you wake up and your education begins, right, So it's

1603
01:24:01,040 --> 01:24:03,479
Speaker 1: it's an ongoing process. Um. And then the second thing

1604
01:24:03,600 --> 01:24:05,960
Speaker 1: is it's it's tricky to do when you're young, but

1605
01:24:06,120 --> 01:24:08,400
Speaker 1: the key is as soon as you can is defined

1606
01:24:08,640 --> 01:24:11,800
Speaker 1: an organization where you feel comfortable and can contribute, and

1607
01:24:11,840 --> 01:24:14,080
Speaker 1: just the culture of the organization where you work is

1608
01:24:14,200 --> 01:24:17,639
Speaker 1: such a big deal. I guess the thing that maybe

1609
01:24:17,680 --> 01:24:20,120
Speaker 1: is what people are asking for more overtly is it

1610
01:24:20,160 --> 01:24:21,840
Speaker 1: would go back to this thing on easy games. So

1611
01:24:21,840 --> 01:24:24,719
Speaker 1: if you're st of saying, like where where are things exciting?

1612
01:24:24,760 --> 01:24:26,879
Speaker 1: The answer is to try not to do what everybody

1613
01:24:26,880 --> 01:24:29,599
Speaker 1: else is doing. Are their pockets or areas where you

1614
01:24:29,720 --> 01:24:31,960
Speaker 1: can do something it's a little bit different, a little

1615
01:24:31,960 --> 01:24:34,720
Speaker 1: bit maybe more niche for now, or something that might

1616
01:24:34,760 --> 01:24:38,719
Speaker 1: grow or evolve quite quite interesting. And our final question,

1617
01:24:39,400 --> 01:24:42,200
Speaker 1: what do you know about the world of investing management

1618
01:24:42,280 --> 01:24:45,240
Speaker 1: today that you wish you knew thirty or so years

1619
01:24:45,280 --> 01:24:47,559
Speaker 1: ago when you were first getting started. Yeah, I mean

1620
01:24:47,600 --> 01:24:49,720
Speaker 1: I think that the I mean I don't think that

1621
01:24:49,800 --> 01:24:52,120
Speaker 1: I didn't know this, but I think I underappreciate it,

1622
01:24:52,160 --> 01:24:54,840
Speaker 1: which is just the central importance of people for all

1623
01:24:54,880 --> 01:24:58,160
Speaker 1: this stuff. And you know, one of the pieces that's

1624
01:24:58,160 --> 01:25:00,320
Speaker 1: in one of our cues, so something we're or write

1625
01:25:00,320 --> 01:25:03,200
Speaker 1: on I've very fully outlined in this report is a

1626
01:25:03,280 --> 01:25:07,000
Speaker 1: report about feedback. So one of the really difficult things

1627
01:25:07,040 --> 01:25:09,599
Speaker 1: in our world is to get feedback, especially in investing.

1628
01:25:09,640 --> 01:25:13,080
Speaker 1: So if you're buying selling stocks, ultimately it's about the

1629
01:25:13,080 --> 01:25:16,160
Speaker 1: stocks performing well. But you know, there's there's very little

1630
01:25:16,280 --> 01:25:21,000
Speaker 1: quality intermediate feedback. But I start the piece off by

1631
01:25:21,040 --> 01:25:24,280
Speaker 1: talking about riffing off of work by Phil Tetlock at

1632
01:25:24,280 --> 01:25:27,600
Speaker 1: the University of Pennsylvania on the super forecasters, and what

1633
01:25:27,640 --> 01:25:29,400
Speaker 1: they were able to sort of figure out is that

1634
01:25:29,439 --> 01:25:33,800
Speaker 1: these super forecasters have certain profiles and those profiles tend

1635
01:25:33,840 --> 01:25:36,280
Speaker 1: to be a key part of what makes them effective

1636
01:25:36,320 --> 01:25:38,559
Speaker 1: at what they do. And there are lots of aspects

1637
01:25:38,600 --> 01:25:40,439
Speaker 1: of it, but one only one that I'll mention it's

1638
01:25:40,479 --> 01:25:43,719
Speaker 1: really important, which is this idea of being actively open minded.

1639
01:25:43,880 --> 01:25:46,920
Speaker 1: Active open mindedness is the key, right, and that means

1640
01:25:46,960 --> 01:25:50,280
Speaker 1: that you're not not only willing to pursue contemplate points

1641
01:25:50,320 --> 01:25:52,439
Speaker 1: of view that are different than yours, but you're actually

1642
01:25:52,479 --> 01:25:55,800
Speaker 1: willing to seek them out. And I'll just say that,

1643
01:25:55,840 --> 01:25:57,640
Speaker 1: you know, we all like to think that we do this.

1644
01:25:57,800 --> 01:26:01,120
Speaker 1: The answer is no, not really right, Because once you've

1645
01:26:01,120 --> 01:26:03,360
Speaker 1: made up your mind about something, the past least path

1646
01:26:03,479 --> 01:26:06,280
Speaker 1: least resistance is just like look for stuff that confirms

1647
01:26:06,280 --> 01:26:08,960
Speaker 1: it and just keep on moving, right, because if you're

1648
01:26:08,960 --> 01:26:10,800
Speaker 1: confronted if you have to change your mind or two things,

1649
01:26:10,840 --> 01:26:12,400
Speaker 1: when you have to change your mind, which itself is

1650
01:26:12,400 --> 01:26:13,840
Speaker 1: a pain, and then you may have to change what

1651
01:26:13,880 --> 01:26:16,680
Speaker 1: you do right, which is actually another pain right, so

1652
01:26:16,800 --> 01:26:19,000
Speaker 1: most of us would rather not be bothered with that.

1653
01:26:19,160 --> 01:26:20,800
Speaker 1: So so that would be the one thing I just say,

1654
01:26:20,800 --> 01:26:24,040
Speaker 1: investment management is is it's this drum beat, but it's

1655
01:26:24,080 --> 01:26:27,519
Speaker 1: the people and the culture is becoming really the secret

1656
01:26:27,520 --> 01:26:30,800
Speaker 1: sauce to long term success. Really good answer. It's funny

1657
01:26:30,880 --> 01:26:35,040
Speaker 1: we we started talking about we started talking out about

1658
01:26:35,120 --> 01:26:39,559
Speaker 1: inflation and never got back to it. When I have

1659
01:26:39,840 --> 01:26:44,680
Speaker 1: my um, I have my expectations in my viewpoint, and

1660
01:26:44,760 --> 01:26:48,360
Speaker 1: whenever I want to share a chart that supports that

1661
01:26:48,479 --> 01:26:53,360
Speaker 1: view part I that viewpoint, I always try to describe

1662
01:26:53,400 --> 01:26:58,479
Speaker 1: it on Twitter as today in confirmed firming my priors here,

1663
01:26:58,640 --> 01:27:01,840
Speaker 1: something that agrees with something already said. So at least

1664
01:27:01,880 --> 01:27:05,920
Speaker 1: there's a tiny hint of recognition that hey, you're not thinking,

1665
01:27:06,040 --> 01:27:09,160
Speaker 1: you're just you're just finding something that agrees with you

1666
01:27:09,200 --> 01:27:12,280
Speaker 1: and sharing it, which we all do. But at least

1667
01:27:12,320 --> 01:27:14,519
Speaker 1: if you admit it, you're you're part of the way

1668
01:27:14,560 --> 01:27:16,719
Speaker 1: to us. I would say, Barry, I don't know exactly

1669
01:27:16,720 --> 01:27:19,040
Speaker 1: when we met, but I remember one It was an

1670
01:27:19,040 --> 01:27:22,000
Speaker 1: early instance where I wrote something and then you wrote

1671
01:27:22,040 --> 01:27:24,640
Speaker 1: me back and you said, hey, man, you wrote this

1672
01:27:24,720 --> 01:27:27,120
Speaker 1: thing and it's not right, and you know you you

1673
01:27:27,200 --> 01:27:29,080
Speaker 1: got this basically thinking wrong. I think it was like

1674
01:27:29,400 --> 01:27:33,160
Speaker 1: weapons of mass destruction, you know, prediction market or something

1675
01:27:33,280 --> 01:27:35,519
Speaker 1: that there was something like a weapons and I wrote

1676
01:27:35,520 --> 01:27:37,920
Speaker 1: something about weapons of mass destruction and and you know

1677
01:27:38,040 --> 01:27:40,040
Speaker 1: Iraq or something. You go that this is not right.

1678
01:27:40,520 --> 01:27:43,200
Speaker 1: And I thought to myself, like, you know, first of all,

1679
01:27:43,240 --> 01:27:46,000
Speaker 1: so of course it's someone's telling you're not right, But

1680
01:27:46,000 --> 01:27:47,880
Speaker 1: but the way you did it was really interesting, and

1681
01:27:47,920 --> 01:27:50,400
Speaker 1: I thought, really constructive. So it was not like, dude,

1682
01:27:50,439 --> 01:27:53,880
Speaker 1: you're an idiot. It was It was not like that day.

1683
01:27:54,160 --> 01:27:56,439
Speaker 1: Maybe maybe maybe at least that's not the way you

1684
01:27:56,479 --> 01:27:58,720
Speaker 1: came across. You came across saying like, hey, I think

1685
01:27:58,760 --> 01:28:01,000
Speaker 1: you got this thing wrong. Are some other stuff that

1686
01:28:01,040 --> 01:28:03,240
Speaker 1: you may want to consider when you think about this

1687
01:28:03,280 --> 01:28:05,280
Speaker 1: topic going forward. And I was like, you know what,

1688
01:28:05,680 --> 01:28:08,840
Speaker 1: I'm down with that that that's good. Like right, it

1689
01:28:08,880 --> 01:28:10,880
Speaker 1: was shot. Maybe it was shockingly diplomatic, but that's the

1690
01:28:10,880 --> 01:28:13,000
Speaker 1: way to do it. So it wasn't about you took

1691
01:28:13,000 --> 01:28:16,160
Speaker 1: it immediately away from being about me and immediately about

1692
01:28:16,400 --> 01:28:18,479
Speaker 1: there might be other ways to Did it change your minds?

1693
01:28:18,520 --> 01:28:20,280
Speaker 1: Did it affect you? It did change. I think it

1694
01:28:20,280 --> 01:28:22,320
Speaker 1: did change my mind because I think and I think

1695
01:28:22,360 --> 01:28:24,080
Speaker 1: you end up being sort of the front end of

1696
01:28:24,080 --> 01:28:27,760
Speaker 1: what ended up being the correct interpretation what was going on,

1697
01:28:28,400 --> 01:28:30,519
Speaker 1: and well that I do all the time. It's a

1698
01:28:30,560 --> 01:28:35,800
Speaker 1: lonely place, but I will tell you the challenge is

1699
01:28:36,680 --> 01:28:40,320
Speaker 1: when you're right about that sort of front end stuff,

1700
01:28:40,360 --> 01:28:43,080
Speaker 1: you're not always right. We thought you're gonna be wrong frequently.

1701
01:28:43,640 --> 01:28:46,599
Speaker 1: You have to be ready to say and that's why

1702
01:28:46,680 --> 01:28:48,680
Speaker 1: every year I put out, hey, this is what I

1703
01:28:48,720 --> 01:28:50,920
Speaker 1: got wrong here in my mea culpus, because if you

1704
01:28:51,000 --> 01:28:53,439
Speaker 1: don't do that, you have no right to say to

1705
01:28:53,600 --> 01:28:57,160
Speaker 1: a Michael Mobson, hey dude, I think you're not right

1706
01:28:57,200 --> 01:29:00,519
Speaker 1: about this. Take a look at a look at that anyway.

1707
01:29:00,520 --> 01:29:02,840
Speaker 1: I appreciate that because you actually did it in a

1708
01:29:02,840 --> 01:29:07,760
Speaker 1: way that was tactful and respectful, and that was I

1709
01:29:07,760 --> 01:29:17,200
Speaker 1: think just take credit for I'll definitely, uh, I definitely will. Hey, Mike,

1710
01:29:17,520 --> 01:29:20,599
Speaker 1: this has been just fantastic. Thank you so much for

1711
01:29:21,040 --> 01:29:24,479
Speaker 1: being so generous with your time. We have been speaking

1712
01:29:24,479 --> 01:29:29,080
Speaker 1: with Michael Mobison. He is the head of Concilient Research

1713
01:29:29,680 --> 01:29:35,280
Speaker 1: at Counterpoint Global at Morgan Stanley's investment management group. If

1714
01:29:35,320 --> 01:29:38,040
Speaker 1: you enjoy this conversation, Well, be sure and check out

1715
01:29:38,080 --> 01:29:41,080
Speaker 1: any of the previous three hundred and nine two ones

1716
01:29:41,160 --> 01:29:44,080
Speaker 1: we've done prior. I promised by the time we get

1717
01:29:44,120 --> 01:29:46,680
Speaker 1: to four hundred, will we'll start getting these right. We

1718
01:29:46,800 --> 01:29:50,400
Speaker 1: love your comments, feedback and suggestions right to us at

1719
01:29:51,080 --> 01:29:54,120
Speaker 1: m IB podcast at Bloomberg dot net. You can follow

1720
01:29:54,160 --> 01:29:56,479
Speaker 1: me on Twitter at rid Halts. Sign up for a

1721
01:29:56,560 --> 01:29:59,760
Speaker 1: daily reading list at rialts dot com. I would be

1722
01:30:00,000 --> 01:30:01,880
Speaker 1: miss if I did not thank the crack staff that

1723
01:30:01,960 --> 01:30:05,080
Speaker 1: helps us put these together each week. Paris World is

1724
01:30:05,120 --> 01:30:09,400
Speaker 1: my producer, Michael Batnick is my head of research. Atika

1725
01:30:09,479 --> 01:30:13,519
Speaker 1: val Bron is our project manager. I'm Barry Rihults. You've

1726
01:30:13,520 --> 01:30:17,000
Speaker 1: been listening to my student business on Bloomberg Radio.