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Speaker 1: Brought to you by Bank of America. Merrill Lynch seeing

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Speaker 1: what others have seen, but uncovering what others may not.

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Speaker 1: Global Research that helps you harness disruption. Voted top global

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Speaker 1: research firm five years running. Merrill Lynch, Pierce, Fenner and

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Speaker 1: Smith Incorporated. This is Masters in Business with Barry Ridholds

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Speaker 1: on Bloomberg Radio. This week on the podcast, we have

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Speaker 1: n y u sterns Aswath Dama Doren. He is a

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Speaker 1: professor at n y U Stern and specializes in valuation

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Speaker 1: and let me spend a moment or two just explaining

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Speaker 1: exactly what that means. Whenever you make a purchase, be

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Speaker 1: it a private, non publicly traded company, it could be

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Speaker 1: private equity, it could be venture capital, or a public company,

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Speaker 1: or a hedge fond or a mutual fund or anything

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Speaker 1: like that, or a market or an index, you are

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Speaker 1: making a valuation decision. You are basically saying this company

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Speaker 1: is worth paying this much money for because I expect

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Speaker 1: there to be gains in the future, and there are

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Speaker 1: a few people in the world who are better at

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Speaker 1: making that assessment than than our guest. Um. I'll leave

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Speaker 1: out all the specific details of his curriculum VITAE. It's

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Speaker 1: too long to list. I need. All I need really

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Speaker 1: need to say is he's a rock star professor um.

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Speaker 1: Incredibly insightful, eloquent, intelligent, uh sort of guy, really really

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Speaker 1: interesting conversation. I this was one of those interviews where

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Speaker 1: I expected it to be good and I was delighted

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Speaker 1: with how absolutely fascinating the guests was. So with no

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Speaker 1: further ado, my conversation with n y U S. Aswath Damadharin.

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Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: My special guest today is n y U S Professor

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Speaker 1: Aswath Demodrin. He is the Kirshner Family Chair in Finance

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Speaker 1: Education at the n y U Stern School of Business.

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Speaker 1: I'm only going to give you an abbreviated version of

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Speaker 1: his curriculum vita. Otherwise we'll take up the full hour.

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Speaker 1: A b. S. At Madras University and MS from the

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Speaker 1: Indian Institute of Management N B A. And PhD from

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Speaker 1: the University of California at Los Angeles. He is the

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Speaker 1: recipient of too many fellowships and awards for teaching excellence

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Speaker 1: to to note other than to point out he is

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Speaker 1: the five time winner of the Professor of the Year

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Speaker 1: at n y U. He is also the author of

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Speaker 1: several highly regarded academic texts on corporate finance, valuation, and

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Speaker 1: investment management. He's written so many books. Let me just

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Speaker 1: reference a few, The Little of Stock Valuation, investment valuation

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Speaker 1: tools and techniques, corporate finance theory and practice, investment philosophies,

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Speaker 1: successful strategies, and the investors who made them work, and

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Speaker 1: on and on us with Delmaradin. Welcome to Bloomberg. Thank you,

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Speaker 1: I'm glad to be here. I'm I'm excited to have

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Speaker 1: you because this is an area that is so important

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Speaker 1: to investors and we really don't dive as deeply into

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Speaker 1: it as we probably should. So so let's just start

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Speaker 1: right from the beginning. Where should investors be thinking when

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Speaker 1: they look at the question of valuation. I think the

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Speaker 1: first thing investors have to be asking is whether they

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Speaker 1: should be doing evaluation in the first place, because valuation

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Speaker 1: requires two things. One is it requires a willingness to

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Speaker 1: learn the basic tools. And many investors want to do valuation,

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Speaker 1: but they don't want to learn about accounting. They want

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Speaker 1: to do evaluation, but they don't want to learn about

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Speaker 1: present value. They want to do valuation, but they don't

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Speaker 1: want to understand the basics that drive risk and return.

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Speaker 1: And that's lazy. It's lazy, and it leads to you

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Speaker 1: spending a lot of time thinking your value companies and

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Speaker 1: you walk away with actually nothing to show for it.

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Speaker 1: So the first question I would ask you is do

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Speaker 1: you have time to be willing to do things you

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Speaker 1: might not enjoy doing learning those twos? What is the

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Speaker 1: alternative to not learning to do just put your money

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Speaker 1: in an index. Fun in the world would be far

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Speaker 1: better off if you just put your money in the

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Speaker 1: next one, went back to doing what you did with

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Speaker 1: the rest of your life. Be a doctor, be an engineer,

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Speaker 1: be a plumber, do what you do in your regular life.

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Speaker 1: You investing for what it's meant to do, which is

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Speaker 1: to preserve and grow what you save from your job.

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Speaker 1: And I think for a lot And that's the other

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Speaker 1: point it emphasized, because a lot of people invest for

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Speaker 1: the wrong reason. They want to get rich. I mean

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Speaker 1: that's the reality. We invest because we want to beat

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Speaker 1: the market. It's a lottery ticket. We're gonna We're gonna

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Speaker 1: find the next to Apple, the next Google, and I

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Speaker 1: think in the process we make mistakes, and then we

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Speaker 1: beat ourselves up about the mistakes we make, and that

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Speaker 1: leads to more mistakes. Sounds fairly like a familiar story.

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Speaker 1: So so let's talk about the accounting work. I assume

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Speaker 1: you're talking about looking at account statements, looking at balance sheets,

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Speaker 1: looking at cash flows. How does the average investor wrap

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Speaker 1: their head around. I would say keep it simple, because

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Speaker 1: I think if you take an accounting class, the problem

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Speaker 1: is an accounting class is all about debit and credit

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Speaker 1: and getting you into the nitty gritty of the footnotes.

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Speaker 1: I actually did an exercise where I do these webcasts

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Speaker 1: where I actually took the propran gamble ten K, and

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Speaker 1: what I did was I did a webcast where I

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Speaker 1: talked about how little of the ten K is actually

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Speaker 1: useful in valuation and how much of it is noise.

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Speaker 1: Accountants throw in small stuff with the big stuffy. They're

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Speaker 1: incapable of telling the difference the stuff that maddens the

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Speaker 1: stuff that doesn't because there accountants the detail oriented, they've

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Speaker 1: focused on it. I was about to say, not only

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Speaker 1: the detail oriented, but when it comes to a ten K,

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Speaker 1: the typical responses, hey disclose it all better to get

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Speaker 1: it out than be accused of hiding something later. In fact,

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Speaker 1: that the point I make is data is not information.

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Speaker 1: We're mistaking the two. We have a lot more data

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Speaker 1: than we used to have thirty five years ago. Typical

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Speaker 1: ten K now is five times longer than it used

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Speaker 1: to be thirty years ago. Really, that's fascinating because of

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Speaker 1: all the disclosure requirements. And that's part of the problem

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Speaker 1: is people think that if you throw everything into a

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Speaker 1: ten K, you are being more informative. I actually suggest

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Speaker 1: that maybe we should go back to fifteen page ten ks,

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Speaker 1: where you focus on the things that really matter, like

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Speaker 1: a nutrition statement on the side of the canda soda.

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Speaker 1: Here's the important metrics that you need to know. I'll

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Speaker 1: give you an example of a completely useless section of

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Speaker 1: a ten K where they describe the risks that they

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Speaker 1: might face, like twenty pages. It's exactly it's a lawyer

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Speaker 1: writing that stuff. Right in case we get suited. We

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Speaker 1: told you competition could come in and take the market,

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Speaker 1: as if you didn't know that when you invested. So

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Speaker 1: I would actually argue that we're moving in the wrong

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Speaker 1: direction by pushing more and more disclosure requirements When In fact,

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Speaker 1: we need more focus in financial statements, better and more

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Speaker 1: focused exactly releases as opposed to just to your volume.

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Speaker 1: In fact, I do a fifteen minute webcast and exactly

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Speaker 1: the accounting. I need you to know things like do

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Speaker 1: you know the difference between gross income operating income in

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Speaker 1: net income? If you don't, you're in trouble, right, So

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Speaker 1: the basic stuff so that once you can, it's more

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Speaker 1: financial statement analysis and accounting that I want you to know.

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Speaker 1: Can you read a statement of cash flows? That's that's

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Speaker 1: quite quite interesting. So when we talk about non cash

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Speaker 1: charges and amorization and appreciation and goodwill and all these

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Speaker 1: fun geeky terms, are these of any assistance to the

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Speaker 1: average investor? Most of them are not. I'll tell you

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Speaker 1: I've described good will as the most destructive accounting item

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Speaker 1: ever created in history. So let's let's delve into that.

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Speaker 1: Define goodwill for people who may not be familiar with

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Speaker 1: the phrase goodwill is the accountant admitting he screwed up.

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Speaker 1: Because that's the way to think about it. Right. So

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Speaker 1: if you have a company with the four billion dollar

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Speaker 1: book bellion, I pay ten billion the accounting, there's a

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Speaker 1: six billion dollar problem to explain away, so he shows

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Speaker 1: it as good will. So it's very squishy, it's very elastic.

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Speaker 1: You could put whatever you want into that. It's it's

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Speaker 1: a plug variable. You put it in because your balance

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Speaker 1: sheet has to balance. It is a very unpleasant requirement.

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Speaker 1: So good will exist for one reason alone. It balances

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Speaker 1: balance sheets. It's a plug variable. In fact, I send

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Speaker 1: these suggestions to I F R S and to GAP

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Speaker 1: that they never seem to take. And one of the

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Speaker 1: suggestions I've sent a few years ago was, let's rename

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Speaker 1: goodwill as an algebra when you have a missing barriable

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Speaker 1: to make X, because then we wouldn't do crazy things

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Speaker 1: like paying for goodwill. I'm Barry Ridlts. You're listening to

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Speaker 1: Masters in Business on Bloomberg Radio. My guest today is

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Speaker 1: Professor as Damadarn of n y U Stern School of Business.

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Speaker 1: He is an expert in the valuation of corporations. Let's

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Speaker 1: talk about two of the hottest companies out there, both

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Speaker 1: of which seem to have a somewhat ambiguous valuation, Tesla

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Speaker 1: and Amazon. So let's let's talk about Tesla. You've done

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Speaker 1: so much work on that. What is the bottom line

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Speaker 1: with the valuation of Tesla. I mean, with all young companies,

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Speaker 1: it's a story that you tell about the company that

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Speaker 1: drives your evaluation. The narrative, and with Tesla, the narrative

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Speaker 1: can lead you to different places. I mean, I'll give

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Speaker 1: you I'll give you an example. When I first valued

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Speaker 1: Tesla was about three years ago. I value them as

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Speaker 1: a luxury automobile company. I gave them the margins of

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Speaker 1: luxury automobile companies, and I gave them the growth you

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Speaker 1: can get as a luxury automobile company. Came up with

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Speaker 1: about a hundred dollars per share. Okay, the problem with

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Speaker 1: Tesla is you've got, for lack of a better word,

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Speaker 1: a delusional CEO who kind of keeps expanding the story

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Speaker 1: on you. Right because in a sense, today he's an

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Speaker 1: automobile company. Tomorrow is a clean energy company. They have

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Speaker 1: tomorrow god only knows where. So the story for Tesla

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Speaker 1: I call it the ultimate story stock, which is people

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Speaker 1: have stopped talking about numbers. The numbers really, and that's

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Speaker 1: why Tesla stock price can take all this punish mint

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Speaker 1: of deliveries not being on time. It's still hang in

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Speaker 1: there because the people who invest in Tesla have bought

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Speaker 1: into Elon Musk's story. Is there an Elon Musk premium?

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Speaker 1: I think there's an Elon Musk fandom, which essentially Tesla

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Speaker 1: investors are not investors like other companies. They don't invest

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Speaker 1: in the company for the earnings that cash flows. They're

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Speaker 1: not worried about delivery schedules not being met. They're buying

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Speaker 1: the Elon Musk story. So there was There was just

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Speaker 1: an article out the other day, that Bloomberg release that showed,

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Speaker 1: amongst luxury automakers BMW, Audie, Mercedes and others plus Tesla

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Speaker 1: in terms of sheer volume last quarter, Tesla's beating everybody.

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Speaker 1: And is that a fair statement? And I think Tesla

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Speaker 1: has some incredible strengths, which is I mean, can you

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Speaker 1: imagine another automobile company telling the world that they're going

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Speaker 1: to come out with a new model in two thousand

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Speaker 1: and eighteen and four hundred thousand people signing up today

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Speaker 1: and putting down insane It's nobody else will do it.

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Speaker 1: So how about just the idea of someone saying, I

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Speaker 1: have an idea, let's make a new automobile company that

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Speaker 1: has hasn't been done? And how many And that's why

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Speaker 1: I feel frustrated with Tesla because at the core, it

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Speaker 1: has something very powerful, this connection to customers, that no

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Speaker 1: other automobile company does. And the reason I'm frustrated is

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Speaker 1: I want them to build this incredible, great automobile company.

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Speaker 1: I want focus here. Instead, what do I get? I

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Speaker 1: get them acquiring Solar City and telling me that they're

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Speaker 1: building battery panels and solar power. Well, you're gonna buy

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Speaker 1: a battery pack, put in your garage, put the solar

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Speaker 1: cells on your roof, and now we've made you completely

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Speaker 1: independent off the grid. Look what we've done for you

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Speaker 1: as an automobile purchaser. And in a sense, I think,

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Speaker 1: once you've established yourself as an automobile company, maybe you

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Speaker 1: want to do this, But already you have enough on

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Speaker 1: your plate. Why would you want to load up more

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Speaker 1: stuff on your plate? Especially because I think Solar City

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Speaker 1: is a commodity in a commodity business with a huge

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Speaker 1: amount of dead So it's almost like you're adding three

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Speaker 1: distractions to a company that already has so much to

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Speaker 1: focus on. If I were Testla, I'd looking at getting

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Speaker 1: the Tesla three delivered on time, because that's going to

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Speaker 1: be disastrous. If you've got four hundred thousand people expecting

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Speaker 1: the car to be delivered in two thousand and eighteen,

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Speaker 1: and that doesn't get and this is this is the

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Speaker 1: low cost under forty dollars exactly every person all electric automobile,

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Speaker 1: which ironically General Motors um turns out to be ahead

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Speaker 1: of Tesla with Chevy with their their first they had

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Speaker 1: the vaults and now they have the vaults, They're actually

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Speaker 1: gonna beat Tesla the market. Yeah, but I think Tesla

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Speaker 1: will win that game simply because General Motors wants to

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Speaker 1: go after the fifteen maybe the lowest. I mean a

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Speaker 1: Tesla will never be a fully mass market automobile company

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Speaker 1: because I think in a sense they will always trade

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Speaker 1: at a premium over a GM car. So I think

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Speaker 1: they will have that price premium and a profit margin.

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Speaker 1: But all of that presupposes that they make the trains

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Speaker 1: run on time. I could talk about Tesla all day.

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Speaker 1: Will come back to that. Let's talk about Amazon. I

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Speaker 1: mentioned there was a uh euron Musk premium. Is there

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Speaker 1: a Jeff Bezos premium at Amazon? And I think it's deserved.

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Speaker 1: I mean, Amazon has been one of my pet obsessions

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Speaker 1: for twenty years. It's a company that I valued first

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Speaker 1: in seven and I valued pretty much every year since.

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Speaker 1: I tell people I bought Amazon four times and I've

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Speaker 1: sold Amazon four times, which kind of tells you where

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Speaker 1: I am with Amazon, Right. Have you regretted all of

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Speaker 1: those cells? No, because I think in a sense, I

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Speaker 1: I have to stay true to my faith, which is

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Speaker 1: I buy companies because I think, like an investor, the

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Speaker 1: price has to be less than the value. Amazon is

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Speaker 1: a company where the price can take off and go

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Speaker 1: well ahead of value for extended stretches for years. And

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Speaker 1: I think part of the reason I think people are

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Speaker 1: willing to pay a premium is I've never seen a

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Speaker 1: company where a CEO has been more consistent about his narrative.

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Speaker 1: In fact, he's the the anti Elon Musk. Right. If

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Speaker 1: you go back to and then you read the letter

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Speaker 1: that Amazon said to I don't know whether you've ever

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Speaker 1: read that Jeff Bezos. There's a great letter that's online

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Speaker 1: on what he told people he would do as a company,

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Speaker 1: and he's done it, and he's stayed exactly true to

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Speaker 1: the script. There have been a handful of pivots, but

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Speaker 1: he's pretty much been fairly consist Their minor shifts, of course,

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Speaker 1: course changes, not wholesale reversals in any ways. In the fact,

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Speaker 1: I describe Amazon as a field of dreams company, right,

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Speaker 1: which is if we is what the story he told is,

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Speaker 1: if we build it, they will come. Basically said, we're

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Speaker 1: gonna go after revenues first, We're not going to worry

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Speaker 1: about profits, and once we build the revenues, the profits

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Speaker 1: will come. And he stayed true not just to that

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Speaker 1: story in terms of what he said, but how he acts.

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Speaker 1: You look at the Kindle, you look at it basically

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Speaker 1: go product by product. So yeah, let's go through. You

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Speaker 1: have the Kindle, you have the Amazon Fires, Amazon Tia,

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Speaker 1: Amazon Prime. Right. Basically, Amazon Prime is I started Amazon

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Speaker 1: Prime a few years years ago. I was late to

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Speaker 1: the party, and it's just become indispensable. Amazon Music, Amazon Video,

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Speaker 1: and what they share in common is Amazon sells every

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Speaker 1: one of those products at cost or below. They're open

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Speaker 1: about it. Amazon Prime they charge you go to the

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Speaker 1: ten K, they actually tell you how much it was.

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Speaker 1: They give you the cost or how much it costs

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Speaker 1: them to service. It's about three. Basically they're saying, look,

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Speaker 1: why's subsidizing it, But they say it's okay because if

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Speaker 1: we build it, they'll come. Your colleague, an n y

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Speaker 1: U professor Galloway, has been suggesting Amazon should go out

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Speaker 1: and buy the US Postal Service. Of course, they're shipping

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Speaker 1: costs are now in the billions, soon to be tens

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Speaker 1: of billions of dollars, and you know whatever our FedEx

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Speaker 1: and UPS. I'd be terrified where where Amazon is going next? Right,

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Speaker 1: because Amazon has this insane capacity to destroy almost every

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Speaker 1: business for everybody else in the business. Now it's done

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Speaker 1: in the retail business already. The reason I will I

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Speaker 1: will be hesitant to buy Netflix X is because Amazon

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Speaker 1: is entering big time into that business. Ups and FedEx.

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Speaker 1: You look at Amazon entering the business because here's what

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Speaker 1: Amazon does with every business they go into. They sell

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Speaker 1: below cost, They challenge you to match up with them,

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Speaker 1: and they can keep going long after you dropped out

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Speaker 1: of the game. So I think that Scot's right. I mean,

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Speaker 1: I think that there is change coming to this business.

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Speaker 1: I'm Barry Ridhults. You're listening to Masters in Business on

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Speaker 1: Bloomberg Radio. My guest today is Aswath Damadarn. He is

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Speaker 1: a professor of finance at n y u's Stern School

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Speaker 1: of Business. He is also an author of numerous books,

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Speaker 1: including the Little Book Evaluation and Investment Philosophies, Successful Strategies

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Speaker 1: and the investors who made them work well. We were

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Speaker 1: previously talking about Tesla and Amazon, and but these are

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Speaker 1: public companies and the market gives us an approximate valuation

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Speaker 1: every day. Let's talk a little bit about private companies

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Speaker 1: where there isn't that public valuation. How do you go

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Speaker 1: about valuing a non traded private company. I think knowing

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Speaker 1: the prices a crutch, right, I mean, in a sense

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Speaker 1: you value should not be a function of knowing the price.

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Speaker 1: We use it in public companies often as a feedback

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Speaker 1: loop to make sure we aren't screwing up too badly.

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Speaker 1: So it's true when you value a private business you

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Speaker 1: look for that crutch. It's not dead, though it depends

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Speaker 1: on the private business. I valued over multiple times on

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Speaker 1: my blog. And there there is a pricing. It's a

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Speaker 1: VC pricing or a Saudi Sovereign Fund pricing of the company.

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Speaker 1: But is that a true pricing. I've I've heard people

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Speaker 1: make that argument. But what you're essentially doing is instead

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Speaker 1: of taking the collective wisdom of millions of investors. However, misguarded.

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Speaker 1: They may be at times you're lying on one person

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Speaker 1: rinning a check and that valuation. In fact, this is statistical.

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Speaker 1: It's a statistical problem. In the regular market, you have

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Speaker 1: hundreds and hundreds of people trying to assess the price

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Speaker 1: your one person. So all you need is one crazy person.

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Speaker 1: In fact, I wrote an article on VC pricing just

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Speaker 1: a couple of weeks ago on how VC pricing can

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Speaker 1: very quickly get out of control because all you need

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Speaker 1: is one crazy person to drive the pricing process out

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Speaker 1: of control. It's a feedback feedback, it's a feedback loops.

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Speaker 1: So but it's so that's that's part of the problem.

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Speaker 1: The other part is when a VC says I've got

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Speaker 1: five percent of a company and I paid fifty million,

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Speaker 1: you can't extrapolate that simply because vcs, when they invest

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Speaker 1: in the equity and a company often get these options

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Speaker 1: on the side right roundside protect so it's much more

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Speaker 1: so it's a much sweeter deal than exactly. So it's

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Speaker 1: a much more difficult place to get a price, but

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Speaker 1: at least you have a price. I think it gets

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Speaker 1: really tough if you ask me to value the hot

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Speaker 1: dog stand outside right, because I can value it. There

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Speaker 1: is no price I can compare it to, and it

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Speaker 1: makes me uncomfortable. It makes everybody uncomfortable not to have

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Speaker 1: something check, and that I think is a psychological problem

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Speaker 1: with valuing private businesses. You value the business, say, how

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Speaker 1: do I know I'm even within the ballpark? Right? You're stuck,

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Speaker 1: stuck relying on the metrics that exactly that you use

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Speaker 1: um And to make sure people understand, when you talk

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Speaker 1: about options, vcs very often have the right to reinvest

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Speaker 1: in a subsequent round at a same or discounted price

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Speaker 1: to whatever that next valuation is. When you say options,

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Speaker 1: it's not actually options, it's it's so they're getting this

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Speaker 1: upside advantage. You also get a downside protection sometimes, which

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Speaker 1: is the value decreases. They are allowed ways of escaping

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Speaker 1: from that. So vcs are pretty careful about putting those

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Speaker 1: options in, and founders go along because it allows them

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Speaker 1: them to inflate that because I'm sure because when the

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Speaker 1: journalist says, you know, so such in the Terranniss valued

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Speaker 1: at nine billion, they're often extrapolating from a VC investment.

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Speaker 1: So it serves the founder and the vcs to at

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Speaker 1: least put out the surface number that everybody is buying into.

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Speaker 1: So you mentioned Taranis, which was widely known as a

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Speaker 1: unicorn before the nar would have turned out to be

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Speaker 1: pretty much nonsense. And now the whole thing is sort

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Speaker 1: of mid collapse. We're watching it doing enron Um not

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Speaker 1: quite the same underlying issues as Enron had. But when

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Speaker 1: when the business school case is written on that, it's

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Speaker 1: going to be really interesting to see how people missed

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Speaker 1: all the warning signs. What about some of the other

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Speaker 1: Silicon Valley unicorns, the billion dollar private companies. You mentioned Uber?

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Speaker 1: What is uberworth? Well, according to the pricing, it's worth

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Speaker 1: over sixty billion. What do you think it's worth. I

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Speaker 1: think it's worth a number, and well it's my estimate

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Speaker 1: of the value. I'm not sure what the legit number

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Speaker 1: is because Uber is very I think the part of

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Speaker 1: the problem with Uber is we know what it can do.

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Speaker 1: It can grow revenues like crazy. It's in kind. I

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Speaker 1: go to cities where you would not think Uber would

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Speaker 1: be there, but it's there, such as give me a few.

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Speaker 1: I was in Moscow's Apolo, Jakarta and Delhi last summer.

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Speaker 1: Every single city, I asked the audience, how many of

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Speaker 1: you got your Uber and in every single city. To

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Speaker 1: the people in the in the session said they came

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Speaker 1: in by Uber. That's astonishing. They're that widespread. That's the

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Speaker 1: good news that they know how to grow because they

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Speaker 1: have a very low capital intensity model. To grow in

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Speaker 1: the city, all they need to do is hire a guy,

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Speaker 1: put them in a motel room, ask them to get

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Speaker 1: some drivers together. Because they don't own the cars, they

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Speaker 1: don't have the driver, they don't the drivers don't work

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Speaker 1: for them. The problem for Ruber is they have a

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Speaker 1: business model which is indefensible. What I mean by that

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Speaker 1: is they can grow revenues, but they can't make money

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Speaker 1: right because entry is easy. So that what was happening.

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Speaker 1: What's happening in the U S and it's concerning every

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Speaker 1: right sharing company is Lift offers Uber drivers two thousand

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Speaker 1: dollars to switch to lift. Uber offers Lift and to

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Speaker 1: make them exclusive, so they pay them money. So basically

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Speaker 1: what's happening is the expenses are way out of control.

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Speaker 1: So well, revenues are doubling, costs are tripling. So the

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Speaker 1: way I described it, you've got to get an exit

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Speaker 1: from this, but you've got to come up with the

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Speaker 1: defensible business. You've got increased stickiness, and much of what

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Speaker 1: you're seeing in the right sharing space in the last

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Speaker 1: year is how to increase stickiness. Staking is in the

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Speaker 1: sense of keeping drivers attached to you, keeping customers with you.

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Speaker 1: I'm very results you're listening to Masters in Business on

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Speaker 1: Bloomberg Radio. My guest today is Professor Aswat Damadarn. He

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Speaker 1: is of n y U Stern School of Business and

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Speaker 1: he is an expert in valuing companies, both private and public.

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Speaker 1: Let's let's talk a little bit about valuing stocks and

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Speaker 1: the market. Generally. We've if you've been an investor for

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Speaker 1: any length of time, you may have noticed that valuations

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Speaker 1: in general have been creeping up over the years. Not

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Speaker 1: just things are a little pricey today, but generally things

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Speaker 1: have gone up in price in the stock market. And

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Speaker 1: there are a number of different theories as to why. First,

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Speaker 1: is that a fair valuation. Two stocks generally look pricier

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Speaker 1: this decade, in last decade than five, six, seven decades.

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Speaker 1: It's not debatable, right, the p ratios are up, capes

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Speaker 1: are up, price to book ray shows up. You take

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Speaker 1: any multiple there at high but hears the way to

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Speaker 1: think about it investing is not an absolute game. You

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Speaker 1: say I invest or not. It's a relative game. So

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Speaker 1: if you're not going to invest in stocks, the question

429
00:23:22,520 --> 00:23:24,840
Speaker 1: is ware you can invest instead, And what do you

430
00:23:24,880 --> 00:23:27,800
Speaker 1: have is a game that has fundamentally changed on that dimension.

431
00:23:28,119 --> 00:23:30,280
Speaker 1: In two thousand and seven, if you didn't invest in stocks,

432
00:23:30,280 --> 00:23:31,760
Speaker 1: you might have put your money in T bonds and

433
00:23:31,800 --> 00:23:34,639
Speaker 1: made four percent in corporate bonds and made five and

434
00:23:34,640 --> 00:23:37,480
Speaker 1: a half to six percent. But today you don't invest

435
00:23:37,520 --> 00:23:39,399
Speaker 1: in stocks. You invest in T bonds you make one

436
00:23:39,440 --> 00:23:42,080
Speaker 1: point five percent, or corporate bonds and make two and

437
00:23:42,080 --> 00:23:46,240
Speaker 1: a half percent. Stocks look expensive relative to their own industry,

438
00:23:46,280 --> 00:23:48,640
Speaker 1: but they don't look expensive relative to what my other

439
00:23:48,760 --> 00:23:50,960
Speaker 1: choices are. So in other words, you can't just look

440
00:23:51,000 --> 00:23:55,720
Speaker 1: at a single metric in isolation like priceto earnings ratio

441
00:23:55,840 --> 00:23:58,560
Speaker 1: and say, hey stocks or cheaper stocks. If it was

442
00:23:58,600 --> 00:24:00,560
Speaker 1: that simple, you buy it when the pe was low,

443
00:24:00,640 --> 00:24:02,919
Speaker 1: you sell when the pus high, and then you get

444
00:24:03,280 --> 00:24:07,399
Speaker 1: that's not That's been my pet peeve with especially chilla,

445
00:24:07,560 --> 00:24:10,280
Speaker 1: the chilla cape right, because it's become this single metric,

446
00:24:10,320 --> 00:24:13,000
Speaker 1: it's a nobel price when they're pushing it. Therefore, it

447
00:24:13,160 --> 00:24:16,159
Speaker 1: might so, first if you think about the cape, and

448
00:24:16,200 --> 00:24:18,600
Speaker 1: you look at the history. It goes back to seventy one,

449
00:24:18,640 --> 00:24:21,600
Speaker 1: and you can show all kinds of relationships in cape

450
00:24:21,640 --> 00:24:25,360
Speaker 1: now and returns in the future, So it's and there

451
00:24:25,359 --> 00:24:29,119
Speaker 1: again the statistics are incontestable, which is if you have

452
00:24:29,480 --> 00:24:32,359
Speaker 1: if the CAPE is high now, returns in the future

453
00:24:32,359 --> 00:24:35,520
Speaker 1: I have tended to be lower. So people then say, okay,

454
00:24:35,560 --> 00:24:37,280
Speaker 1: that means you should be out of stocks now because

455
00:24:37,320 --> 00:24:39,280
Speaker 1: the CAPE is at twenty six or twenty seven or

456
00:24:39,280 --> 00:24:42,560
Speaker 1: twenty depending on how you compute it. But that assumes

457
00:24:42,640 --> 00:24:45,640
Speaker 1: that the underlying alternative, which is what you can make

458
00:24:45,680 --> 00:24:49,160
Speaker 1: if you don't invest in stocks, hasn't changed, and it has.

459
00:24:49,920 --> 00:24:53,640
Speaker 1: Someone used the number not too long ago that if

460
00:24:53,680 --> 00:24:57,760
Speaker 1: you were out of stocks when the cape was um,

461
00:24:57,800 --> 00:25:00,639
Speaker 1: either someone elevated or very very of it over the

462
00:25:00,640 --> 00:25:05,399
Speaker 1: past thirty years, you would have missed of the stock rally.

463
00:25:05,480 --> 00:25:08,320
Speaker 1: So you can't look at it in isolation. In fact,

464
00:25:08,320 --> 00:25:10,520
Speaker 1: what I did was I took Schiller's own numbers. Let's

465
00:25:10,560 --> 00:25:13,240
Speaker 1: to be accused of using some variant. I took Schiller's

466
00:25:13,280 --> 00:25:16,399
Speaker 1: own numbers that go back to eighteen seventy one, and

467
00:25:16,400 --> 00:25:18,280
Speaker 1: I put in an Excel spreadsheet which is on my

468
00:25:18,320 --> 00:25:24,840
Speaker 1: blog where I try different market timing strategies based on Cape. Like,

469
00:25:24,960 --> 00:25:27,520
Speaker 1: for instance, I said, what would happen if every time

470
00:25:27,560 --> 00:25:31,640
Speaker 1: the CAPE was twenty above the median from the previous

471
00:25:31,680 --> 00:25:35,240
Speaker 1: fifty years, I sold stocks and the CAPE was twenty five?

472
00:25:35,240 --> 00:25:37,240
Speaker 1: Because I mean, ultimately, if you want me to time

473
00:25:37,240 --> 00:25:40,240
Speaker 1: markets based on strips, and then I tried. I could

474
00:25:40,240 --> 00:25:44,119
Speaker 1: not find a single timing strategy over the last fifty

475
00:25:44,200 --> 00:25:48,200
Speaker 1: years where I could use Cape to make money. I

476
00:25:48,280 --> 00:25:51,240
Speaker 1: tell people correlations are not cash because all too often

477
00:25:51,240 --> 00:25:53,040
Speaker 1: when people tell them to send me the stuff on Cape,

478
00:25:53,040 --> 00:25:55,199
Speaker 1: it's look at the correlation in CAPE and future returns.

479
00:25:55,200 --> 00:25:56,920
Speaker 1: And I said, I've never been able to make money

480
00:25:56,920 --> 00:26:00,000
Speaker 1: in correlations. Tell me how I used this to make money,

481
00:26:00,080 --> 00:26:03,199
Speaker 1: And at least based on the testing I did, I

482
00:26:03,200 --> 00:26:06,199
Speaker 1: couldn't find a single way you could use Cape to

483
00:26:06,359 --> 00:26:08,919
Speaker 1: make money from timing. Monk, we just just look at

484
00:26:08,960 --> 00:26:10,840
Speaker 1: the past decade. If you would have used Cape, you

485
00:26:10,880 --> 00:26:13,840
Speaker 1: would have sat in in a pretty nice bond rally,

486
00:26:13,880 --> 00:26:16,960
Speaker 1: but you would have missed a two plus percent equity rally.

487
00:26:16,960 --> 00:26:20,480
Speaker 1: It would have been would have been pretty pretty off. Um. So,

488
00:26:20,480 --> 00:26:23,040
Speaker 1: so let's talk about a couple of people have some

489
00:26:23,080 --> 00:26:26,320
Speaker 1: theories I think are interesting. Cliff Fastness of a qu

490
00:26:26,520 --> 00:26:30,160
Speaker 1: R said, it used to be very expensive to buy stocks.

491
00:26:30,640 --> 00:26:32,760
Speaker 1: You didn't have indexes, you didn't have a lot of

492
00:26:32,800 --> 00:26:37,840
Speaker 1: things that made it friction lists and and efficient. It

493
00:26:38,000 --> 00:26:40,760
Speaker 1: was time consuming, expensive, and you had to be paid.

494
00:26:41,280 --> 00:26:43,800
Speaker 1: Investors had to be paid or reasonable ry to return

495
00:26:44,240 --> 00:26:48,600
Speaker 1: to compensate for that initial friction. And most of those frictions,

496
00:26:48,600 --> 00:26:51,480
Speaker 1: according to Cliff, have gone away. Is that a fair

497
00:26:51,880 --> 00:26:53,639
Speaker 1: way to look at it? I don't buy it. I mean,

498
00:26:53,800 --> 00:26:56,080
Speaker 1: I don't think there are enough, there were ever enough

499
00:26:56,080 --> 00:26:59,000
Speaker 1: frictions in this game to explain anything but a very

500
00:26:59,000 --> 00:27:01,920
Speaker 1: small prime because as transactions costs, argument you can always

501
00:27:01,920 --> 00:27:05,040
Speaker 1: make about value that what you pay today is a

502
00:27:05,080 --> 00:27:09,240
Speaker 1: function of expected future transactions. Where I'm using transactions costs broadly,

503
00:27:09,840 --> 00:27:11,399
Speaker 1: and it's always been the case that have been in

504
00:27:11,440 --> 00:27:14,160
Speaker 1: a less liquid market, you're willing to pay less upfront

505
00:27:14,280 --> 00:27:17,520
Speaker 1: simply because you worry about in the US market, And

506
00:27:17,720 --> 00:27:20,160
Speaker 1: I mean, unless you're going back to nineteen twenty five

507
00:27:20,240 --> 00:27:23,680
Speaker 1: or five, for the last fifty years, I've pretty much

508
00:27:23,760 --> 00:27:27,159
Speaker 1: had this option of investing in index one's. So the

509
00:27:27,200 --> 00:27:29,159
Speaker 1: Van Guard five index one goes back to what the

510
00:27:29,240 --> 00:27:32,920
Speaker 1: nineteen seventy seventy. So I've always had that option. I've

511
00:27:33,000 --> 00:27:35,879
Speaker 1: chosen not to use the option. And transactions cost starting

512
00:27:35,880 --> 00:27:38,920
Speaker 1: in the eighties started sliding very very quick, so it's

513
00:27:38,920 --> 00:27:41,680
Speaker 1: almost it's not like we've gone from seven percent bid

514
00:27:41,680 --> 00:27:45,359
Speaker 1: ask spreads to half a percent. So if in fact,

515
00:27:45,359 --> 00:27:48,240
Speaker 1: there's a very simple, testable way you could test a

516
00:27:48,240 --> 00:27:51,679
Speaker 1: cliff strategy, which is to break companies down into large

517
00:27:51,720 --> 00:27:54,680
Speaker 1: market gap all the way down to the least traded stocks.

518
00:27:54,920 --> 00:27:57,560
Speaker 1: And if he's right, the least traded stocks shouldn't mean

519
00:27:57,600 --> 00:28:00,480
Speaker 1: the ones that went up the most. The that's where

520
00:28:00,480 --> 00:28:03,040
Speaker 1: they so the most liquid stocks you should actually see

521
00:28:03,200 --> 00:28:07,200
Speaker 1: underperforming the least liquid stocks. If the argument for prices

522
00:28:07,240 --> 00:28:09,800
Speaker 1: going up was this, hey, transactions costs have gone down.

523
00:28:09,960 --> 00:28:13,080
Speaker 1: What about the concept that in the old days to

524
00:28:13,119 --> 00:28:15,919
Speaker 1: build a company you needed a lot of capital, a

525
00:28:15,920 --> 00:28:20,400
Speaker 1: lot of steel, building, staffing. When you look at Facebook's

526
00:28:20,520 --> 00:28:25,439
Speaker 1: purchase of WhatsApp, of WhatsApp, or or go through the

527
00:28:25,440 --> 00:28:29,280
Speaker 1: whole list Instagram, go through, they're they're making these purchases

528
00:28:29,800 --> 00:28:33,359
Speaker 1: small little companies run by four or five people. You

529
00:28:33,359 --> 00:28:37,560
Speaker 1: don't even need the servers and the know how you

530
00:28:37,640 --> 00:28:40,760
Speaker 1: used to ten twenty years ago. It's all in the cloud,

531
00:28:40,800 --> 00:28:43,600
Speaker 1: it's all modular. Those sort of things are readily available.

532
00:28:44,280 --> 00:28:47,840
Speaker 1: So companies today are so much require so much less capital,

533
00:28:47,880 --> 00:28:51,800
Speaker 1: so much less staffing. They deserve a higher multiple because

534
00:28:51,960 --> 00:28:55,160
Speaker 1: the cost structure is so cheap. It's a mixed blessing. Right.

535
00:28:55,160 --> 00:28:57,920
Speaker 1: Because you can grow really fast, you can also shrink

536
00:28:57,960 --> 00:29:00,040
Speaker 1: really fast. Right, Because I have this stare of a

537
00:29:00,200 --> 00:29:03,920
Speaker 1: life cycles infrastructure companies, the old time infrastructure companies. It

538
00:29:04,000 --> 00:29:06,840
Speaker 1: did take you decades to build up the company. But

539
00:29:06,960 --> 00:29:09,280
Speaker 1: once you've built up the company, you could live off

540
00:29:09,320 --> 00:29:13,120
Speaker 1: the fat for a really long time exactly, and you

541
00:29:13,160 --> 00:29:15,920
Speaker 1: could make money for thirty forty fifty years before the

542
00:29:15,960 --> 00:29:19,280
Speaker 1: decline set in. And the decline was slow because the

543
00:29:19,280 --> 00:29:22,280
Speaker 1: same forces that took you a long time to grow

544
00:29:22,320 --> 00:29:25,880
Speaker 1: are now working in your favor as you decline. In contrast,

545
00:29:26,000 --> 00:29:29,120
Speaker 1: you take a tech company. I described tech companies as

546
00:29:29,160 --> 00:29:32,200
Speaker 1: aging in dog years like a tenure Tech companies like

547
00:29:32,240 --> 00:29:34,800
Speaker 1: a seventy year infrastructure company. You can grow really fast

548
00:29:34,840 --> 00:29:36,880
Speaker 1: because as you pointed out, it doesn't require the kind

549
00:29:36,880 --> 00:29:39,800
Speaker 1: of capital investment you did as as an old time

550
00:29:39,840 --> 00:29:42,400
Speaker 1: infrastructure company. But it also means you don't get to

551
00:29:42,680 --> 00:29:44,640
Speaker 1: enjoy the fat for very long. I mean, I think

552
00:29:44,680 --> 00:29:47,480
Speaker 1: of BlackBerry in two thousand and six, it was a star.

553
00:29:48,600 --> 00:29:51,960
Speaker 1: By two thousand and nine it was dead man walking. Basically,

554
00:29:51,960 --> 00:29:54,120
Speaker 1: it's a company that went to where. And that's I

555
00:29:54,200 --> 00:29:56,760
Speaker 1: think when you think about valuation, is valuing across a

556
00:29:56,800 --> 00:29:59,400
Speaker 1: life cycle. I'm not sure that the value of a

557
00:29:59,440 --> 00:30:02,440
Speaker 1: twenty year old life cycle tech company is going to

558
00:30:02,480 --> 00:30:05,400
Speaker 1: be higher than the value of a hundred year infrastructure company,

559
00:30:05,480 --> 00:30:08,600
Speaker 1: because I've got two valued across the cycle, and the

560
00:30:08,600 --> 00:30:12,719
Speaker 1: tech companies are probably more susceptible exact to rapid change

561
00:30:12,840 --> 00:30:17,280
Speaker 1: than someone who's making dishwashing soap or something exactly. That's

562
00:30:17,320 --> 00:30:19,360
Speaker 1: that's a very different So let's let's talk about some

563
00:30:19,440 --> 00:30:22,840
Speaker 1: of your favorite valuation techniques. What do you think is

564
00:30:22,880 --> 00:30:28,280
Speaker 1: the most informative measure of a company's valuation. I ultimately

565
00:30:28,320 --> 00:30:31,080
Speaker 1: am a believer that companies should be price based on

566
00:30:31,160 --> 00:30:33,440
Speaker 1: three things. One is their cash flows. The second is

567
00:30:33,480 --> 00:30:35,480
Speaker 1: the value of their growth, not growth per se, but

568
00:30:35,520 --> 00:30:37,479
Speaker 1: how much it's costing them to get the growth and

569
00:30:38,280 --> 00:30:41,760
Speaker 1: the expensive growth, not just the growth percentage. So I

570
00:30:41,800 --> 00:30:43,560
Speaker 1: bring in both sides. Growth has a good side. It

571
00:30:43,600 --> 00:30:45,840
Speaker 1: makes my revenues grow faster. Growth has a bad side.

572
00:30:45,880 --> 00:30:47,880
Speaker 1: I've got to set aside earnings to cover the growth.

573
00:30:47,880 --> 00:30:50,320
Speaker 1: The net effect is what drives the value of growth.

574
00:30:51,760 --> 00:30:56,120
Speaker 1: Companies globally destroy value as they grow, destroy destroy value

575
00:30:56,120 --> 00:30:58,760
Speaker 1: as it grow. It's one of the scariest statistics on growth.

576
00:30:59,080 --> 00:31:00,880
Speaker 1: So always a mixed fee things when the company says

577
00:31:00,920 --> 00:31:04,719
Speaker 1: we're going to grow, because my first reaction is exactly

578
00:31:05,160 --> 00:31:06,480
Speaker 1: so that I look at the value growth and I

579
00:31:06,520 --> 00:31:09,960
Speaker 1: look at risk. So when I do a discounted Casulow valuation,

580
00:31:10,000 --> 00:31:11,760
Speaker 1: people think of it as some kind of new model.

581
00:31:11,880 --> 00:31:14,920
Speaker 1: People have always valued businesses based on cash flow growth

582
00:31:14,960 --> 00:31:17,240
Speaker 1: at risk. I'm sure there's a Venetian glassmaker in the

583
00:31:17,280 --> 00:31:19,760
Speaker 1: four hundreds, and he might not have gone through a

584
00:31:19,840 --> 00:31:22,240
Speaker 1: d c of model, but I'm sure he asked questions about, hey,

585
00:31:22,240 --> 00:31:24,160
Speaker 1: what are your cash loss He knew better than to

586
00:31:24,240 --> 00:31:28,240
Speaker 1: trust your accounting numbers. Even then, he talked about growth

587
00:31:28,240 --> 00:31:30,560
Speaker 1: in the cost of growth, and he talked about risks.

588
00:31:30,640 --> 00:31:33,640
Speaker 1: So to me, when I value a company, I've got

589
00:31:33,680 --> 00:31:36,440
Speaker 1: to bring those into what I end up with as

590
00:31:36,480 --> 00:31:38,640
Speaker 1: a as a number for the company. What's what's the

591
00:31:38,680 --> 00:31:41,920
Speaker 1: third factor? It's risks, So it's cash flows, growth, the value, growth,

592
00:31:41,920 --> 00:31:44,800
Speaker 1: and risk. So those three factors play out no matter

593
00:31:44,880 --> 00:31:48,240
Speaker 1: how complicated my discounted. That's the end game that I'm

594
00:31:48,280 --> 00:31:50,320
Speaker 1: looking at. So what do you think are the most

595
00:31:50,440 --> 00:31:57,520
Speaker 1: overrated valuation metrics? I think accounting valuation to me is pointless, right,

596
00:31:57,560 --> 00:32:00,920
Speaker 1: So I mean book value or i've I see price,

597
00:32:01,000 --> 00:32:03,760
Speaker 1: the book gets tossed out. All that's exactly now. And

598
00:32:03,800 --> 00:32:05,800
Speaker 1: to me, there's that two things on book value. The

599
00:32:05,840 --> 00:32:08,240
Speaker 1: reason people get so caught up with book values first

600
00:32:08,280 --> 00:32:10,920
Speaker 1: they think that it actually is a measure of what

601
00:32:10,960 --> 00:32:13,280
Speaker 1: you would get if you liquidate the company today, which

602
00:32:13,320 --> 00:32:17,000
Speaker 1: is absolutely absurd. So what about tobin Que ratio? And

603
00:32:17,080 --> 00:32:19,640
Speaker 1: I think it's the same lazy approach, right. The tobanqueue

604
00:32:19,680 --> 00:32:23,080
Speaker 1: approach was developed for a different era with a lot

605
00:32:23,120 --> 00:32:27,800
Speaker 1: of manufacturing company, heavy heavy heavy manufacturing companies where perhaps

606
00:32:27,840 --> 00:32:30,080
Speaker 1: they had factories, they had machinery, They are things that

607
00:32:30,080 --> 00:32:33,760
Speaker 1: could be liquidated in bankrupts exactly. So again there's there's

608
00:32:33,800 --> 00:32:36,600
Speaker 1: a attachment. We have two techniques that we're developed for

609
00:32:36,640 --> 00:32:40,560
Speaker 1: a different age, and we keep trying to apply those

610
00:32:40,560 --> 00:32:43,560
Speaker 1: techniques to the new age companies, which are eighty percent

611
00:32:43,640 --> 00:32:48,680
Speaker 1: the market now is that of by market cap or

612
00:32:48,680 --> 00:32:52,400
Speaker 1: by number of market gaps. So basically are are at

613
00:32:52,480 --> 00:32:55,200
Speaker 1: least some aspect of them. But as a new age

614
00:32:55,240 --> 00:32:59,440
Speaker 1: it's their value comes not from physical assets. So I

615
00:32:59,440 --> 00:33:02,400
Speaker 1: think of Co Cola as a new age company in

616
00:33:02,400 --> 00:33:05,720
Speaker 1: the sense of the physical investments at Coca Cola makes

617
00:33:05,760 --> 00:33:08,320
Speaker 1: a completely useless in my determining what the value of

618
00:33:08,320 --> 00:33:11,440
Speaker 1: Coca Cola really So it's what is it? Brand recognition,

619
00:33:11,560 --> 00:33:15,240
Speaker 1: intellectual property exactly right. So it's a capacity to charge

620
00:33:15,320 --> 00:33:17,880
Speaker 1: a dollar for a can of water with crap and

621
00:33:17,920 --> 00:33:21,720
Speaker 1: sugar and syrup thrown into it. It costs the three

622
00:33:21,720 --> 00:33:25,600
Speaker 1: cents to make. It's it's pure pricing power. That pricing

623
00:33:25,600 --> 00:33:27,320
Speaker 1: power is not going to be reflected if I look

624
00:33:27,360 --> 00:33:29,160
Speaker 1: at the plant and equipment. In fact, when they're sold

625
00:33:29,200 --> 00:33:32,040
Speaker 1: off their bottling, they were telling us a look, you know,

626
00:33:32,240 --> 00:33:36,479
Speaker 1: physical assets don't matter. It's all about brand name, right.

627
00:33:36,520 --> 00:33:39,040
Speaker 1: It's the purest brand name play you can make because

628
00:33:39,040 --> 00:33:42,640
Speaker 1: the bottling is separated from from the rest of the company.

629
00:33:42,680 --> 00:33:45,360
Speaker 1: So what about a company like Walt Disney where you

630
00:33:45,400 --> 00:33:48,600
Speaker 1: have a mix of assets. You have you have the

631
00:33:48,600 --> 00:33:51,840
Speaker 1: theme parks, you have the television studios, you have there's

632
00:33:51,880 --> 00:33:54,560
Speaker 1: so many different moving parts. How do you put a valuation.

633
00:33:54,600 --> 00:33:57,160
Speaker 1: I'm a Disney investor. You know what scares me the most?

634
00:33:57,160 --> 00:34:02,400
Speaker 1: At forty percent of Disney's value comes from ESPN. That's

635
00:34:02,400 --> 00:34:05,680
Speaker 1: the facts. You have a lot of competition for ESPN.

636
00:34:05,960 --> 00:34:09,000
Speaker 1: And here's why ESPN has been the greatest cash cow

637
00:34:09,200 --> 00:34:13,520
Speaker 1: in broadcasting history. Every person who's listening to the show

638
00:34:13,600 --> 00:34:19,040
Speaker 1: who has cable, well, it's not that they're paying seven

639
00:34:19,120 --> 00:34:22,880
Speaker 1: dollars each month of their cable bill is going to ESPN.

640
00:34:23,120 --> 00:34:26,120
Speaker 1: Now what happens when we unbundled in people's That's what

641
00:34:26,280 --> 00:34:30,760
Speaker 1: scares me, right because for two decades it was cable

642
00:34:30,840 --> 00:34:34,040
Speaker 1: or nothing. And now my son was who's who's not

643
00:34:34,200 --> 00:34:37,719
Speaker 1: quite thirty, has got the cable because you know, and

644
00:34:37,760 --> 00:34:39,920
Speaker 1: the reason he's able to use it doesn't watch live sports.

645
00:34:39,960 --> 00:34:44,200
Speaker 1: So to him watching Hulu, Netflix, the way I describe ESPN,

646
00:34:44,200 --> 00:34:47,120
Speaker 1: it's the only thing left between cable and the abyss.

647
00:34:47,680 --> 00:34:50,640
Speaker 1: Because you take life sports out of cable, you've taken

648
00:34:50,640 --> 00:34:53,200
Speaker 1: the lifeblood out of cable. So if people want to

649
00:34:53,239 --> 00:34:56,040
Speaker 1: find more of your research and writing other than the

650
00:34:56,080 --> 00:35:01,160
Speaker 1: books on Amazon, where can they find your your valuation

651
00:35:01,800 --> 00:35:04,960
Speaker 1: discussions and where is your blog? I describe myself as

652
00:35:04,960 --> 00:35:10,560
Speaker 1: a Kim Kardashian evaluation, I am. I'm never shy about

653
00:35:10,640 --> 00:35:14,720
Speaker 1: exposing the material that I have. So I write a blog,

654
00:35:14,760 --> 00:35:17,200
Speaker 1: whichever I do write once a week, and where is that?

655
00:35:17,520 --> 00:35:19,920
Speaker 1: And that's it's a Google blog. So it's actually it's

656
00:35:19,920 --> 00:35:22,600
Speaker 1: called music on Musings on Markets. It's a Google blog.

657
00:35:22,680 --> 00:35:24,759
Speaker 1: So if you type in musings on Markets and my name,

658
00:35:24,800 --> 00:35:28,120
Speaker 1: it should pop up. And I don't put right every day,

659
00:35:28,120 --> 00:35:31,200
Speaker 1: but I write whenever I find something interesting right about.

660
00:35:31,239 --> 00:35:34,719
Speaker 1: So this week I wrote about Deutschebek Why because I

661
00:35:34,760 --> 00:35:38,920
Speaker 1: was just interested in the necessarily exactly the mess that's

662
00:35:38,960 --> 00:35:42,680
Speaker 1: been created. Wells Fargo should be next to New Cross.

663
00:35:42,680 --> 00:35:45,719
Speaker 1: But I drom I value the San Diego Clippers when

664
00:35:45,920 --> 00:35:48,680
Speaker 1: Steve Barmber bought it for two billion, because I was curious,

665
00:35:48,680 --> 00:35:50,800
Speaker 1: it's really worth two billion? I mean, I'm sort of

666
00:35:50,800 --> 00:35:53,160
Speaker 1: the Los Angeles Clippers because I lived in Los Angeles.

667
00:35:53,239 --> 00:35:56,360
Speaker 1: I know how much of a poor cousin the Clippers

668
00:35:56,360 --> 00:35:57,879
Speaker 1: out of the Lakers in l A. And I said,

669
00:35:57,920 --> 00:35:59,799
Speaker 1: you're playing two billion for the Clippers. If that's true,

670
00:35:59,840 --> 00:36:03,720
Speaker 1: how should I pay for the Lakers? I valued Aryan Foster,

671
00:36:04,320 --> 00:36:09,319
Speaker 1: the running back for the Houston the NFL team at

672
00:36:09,360 --> 00:36:13,319
Speaker 1: one point issue tracking stock on his earning st right.

673
00:36:13,719 --> 00:36:16,520
Speaker 1: So I valued a running back in the NFL. And

674
00:36:16,640 --> 00:36:19,080
Speaker 1: this summer I was actually I was watching my kids

675
00:36:19,080 --> 00:36:21,120
Speaker 1: and these are grown up kids with a Pokemon Go

676
00:36:21,239 --> 00:36:23,080
Speaker 1: all over, and I said, when it was a Nintendo

677
00:36:23,200 --> 00:36:26,000
Speaker 1: really doubled in value just because everybody's using Pokemon Go.

678
00:36:26,680 --> 00:36:29,480
Speaker 1: So when I think about value, I just think about

679
00:36:29,520 --> 00:36:31,799
Speaker 1: value across the board. I'm I would know what the

680
00:36:31,880 --> 00:36:35,480
Speaker 1: pricing of bitcoin is. Why is bitcoin press? So I'm

681
00:36:35,520 --> 00:36:37,680
Speaker 1: I describe myself not as an expert in valuation, but

682
00:36:37,719 --> 00:36:41,520
Speaker 1: as a dabbler invaluation. I'm just fascinated by how people

683
00:36:41,560 --> 00:36:44,480
Speaker 1: attach numbers to things, and I look at them in

684
00:36:44,560 --> 00:36:48,239
Speaker 1: my blog. So musing on musing zen market, people are

685
00:36:48,239 --> 00:36:51,759
Speaker 1: gonna just look for Aswadamadaran and using zo market. There's

686
00:36:51,760 --> 00:36:54,040
Speaker 1: a website I have called the Modern Online. So if

687
00:36:54,040 --> 00:36:56,400
Speaker 1: you'd open The Modern Online, which is where you actually

688
00:36:56,440 --> 00:37:00,479
Speaker 1: apologize for it not being pretty, because I'm the reason

689
00:37:00,600 --> 00:37:04,560
Speaker 1: is I get offers from people who are web designers

690
00:37:04,640 --> 00:37:06,879
Speaker 1: and they keep saying, I can make your website look

691
00:37:06,920 --> 00:37:10,960
Speaker 1: really much more attractive, and you can because I need

692
00:37:11,040 --> 00:37:13,400
Speaker 1: to be because I visit my website at least once

693
00:37:13,440 --> 00:37:16,239
Speaker 1: every day or two to add stuff. It's a constant

694
00:37:16,560 --> 00:37:18,319
Speaker 1: because you look at the amount of stuff and on

695
00:37:18,360 --> 00:37:20,719
Speaker 1: the website, it's got there incrementally. So the way it

696
00:37:20,880 --> 00:37:23,360
Speaker 1: works is Wednesday, I taught Monday, I taught a class.

697
00:37:23,719 --> 00:37:26,280
Speaker 1: When I teach the class, it's actually recorded. A YouTube

698
00:37:26,360 --> 00:37:28,799
Speaker 1: video is created out of the class, and I go

699
00:37:28,920 --> 00:37:30,920
Speaker 1: to my website and I put the YouTube video, and

700
00:37:30,960 --> 00:37:32,680
Speaker 1: I put the link to the class, and I put

701
00:37:32,719 --> 00:37:35,000
Speaker 1: the lecture notes I put. I put pretty much my

702
00:37:35,160 --> 00:37:37,440
Speaker 1: entire life is on the website, so you can get

703
00:37:37,480 --> 00:37:42,000
Speaker 1: my classes, my writing, my essentially any aspect of what

704
00:37:42,080 --> 00:37:45,120
Speaker 1: I do in business, I put on that website. So

705
00:37:45,200 --> 00:37:47,840
Speaker 1: I do that. I give a YouTube channel. I have

706
00:37:47,840 --> 00:37:50,320
Speaker 1: a YouTube channel on which I put playlists. The players

707
00:37:50,360 --> 00:37:53,080
Speaker 1: some of the playlists are of my classes. Every time

708
00:37:53,080 --> 00:37:55,640
Speaker 1: I do a blog post, I've learned that I can

709
00:37:55,760 --> 00:37:58,120
Speaker 1: double the number of people who are exposed to that

710
00:37:58,160 --> 00:38:00,800
Speaker 1: blog post by putting a YouTube video on the blog post.

711
00:38:00,920 --> 00:38:04,759
Speaker 1: And what do you discuss the post? I've discovered people

712
00:38:04,800 --> 00:38:07,120
Speaker 1: have stopped reading. A lot of people will never have

713
00:38:07,160 --> 00:38:10,239
Speaker 1: done the other direction. To me, I could read so

714
00:38:10,360 --> 00:38:13,960
Speaker 1: much more than watching a video or listening to it,

715
00:38:14,160 --> 00:38:16,920
Speaker 1: and people have told me they'll listen to this podcast.

716
00:38:17,239 --> 00:38:18,839
Speaker 1: There are a couple of apps that will let you

717
00:38:18,920 --> 00:38:22,560
Speaker 1: listen to it at I think one point five times

718
00:38:22,600 --> 00:38:25,880
Speaker 1: the speed. There's an optimal number. But I've tried to

719
00:38:25,920 --> 00:38:29,000
Speaker 1: do that and I found that that's really especially a

720
00:38:29,040 --> 00:38:32,600
Speaker 1: conversation that might be a little more complex or nuanced.

721
00:38:33,280 --> 00:38:35,239
Speaker 1: I find it's a challenge. So what I do? Then?

722
00:38:35,320 --> 00:38:37,000
Speaker 1: I do the blog post. It's actually I sit in

723
00:38:37,080 --> 00:38:39,840
Speaker 1: front of my computer with slides, so teacher treated like

724
00:38:39,880 --> 00:38:41,840
Speaker 1: a class. I said, if I had twelve minutes to

725
00:38:41,880 --> 00:38:44,560
Speaker 1: take this blog post and teach a class around it,

726
00:38:45,000 --> 00:38:47,560
Speaker 1: because that's to me, I mean, I love teaching, and

727
00:38:47,600 --> 00:38:50,600
Speaker 1: to me, a blog post is just another opportunity to

728
00:38:50,640 --> 00:38:54,560
Speaker 1: talk about a company, so and generalize the discussion because

729
00:38:54,600 --> 00:38:56,960
Speaker 1: I don't want a blog post to ever be about

730
00:38:57,080 --> 00:38:59,520
Speaker 1: just Deutsche Bank. So when I value Deutsche Bank, I

731
00:38:59,560 --> 00:39:01,360
Speaker 1: want would be able to take what I do in

732
00:39:01,400 --> 00:39:03,680
Speaker 1: dotche Bank and value wells fargoing. You're not You're not

733
00:39:03,719 --> 00:39:06,279
Speaker 1: giving them a fish, you're teaching them to fish. Here's

734
00:39:06,280 --> 00:39:09,239
Speaker 1: an example. Now apply the selfwhere in fact, where I

735
00:39:09,320 --> 00:39:13,120
Speaker 1: describe it as would rather be transparently wrong than opaquely right.

736
00:39:13,640 --> 00:39:15,640
Speaker 1: And what I mean by that is, this is a

737
00:39:15,640 --> 00:39:17,799
Speaker 1: business where people want to be opaquely right. You listen

738
00:39:17,840 --> 00:39:20,719
Speaker 1: to c NBC. You see people coming on, and they

739
00:39:20,760 --> 00:39:23,000
Speaker 1: talked for about six minutes. In the end of six minutes,

740
00:39:23,040 --> 00:39:26,360
Speaker 1: you say, what exactly did they say? They've been so opaque,

741
00:39:26,960 --> 00:39:29,040
Speaker 1: that they're so careful because they don't want to be wrong,

742
00:39:29,080 --> 00:39:32,200
Speaker 1: that they've kind of covered every base and they put

743
00:39:32,239 --> 00:39:35,760
Speaker 1: smoke and mirrors in there. I'd rather be open about

744
00:39:35,800 --> 00:39:37,680
Speaker 1: what I think. So I'm gonna say, dotes what twenty

745
00:39:37,680 --> 00:39:40,120
Speaker 1: one and a half and I'm buying it at thirteen

746
00:39:40,200 --> 00:39:43,719
Speaker 1: point two. Here's why I think it's what twenty one

747
00:39:43,920 --> 00:39:46,719
Speaker 1: And I could be absolutely wrong about every single one,

748
00:39:46,760 --> 00:39:49,120
Speaker 1: but at least if I'm wrong, you can tell me

749
00:39:49,239 --> 00:39:52,800
Speaker 1: what part of my valuation I screwed up on, you know? Um.

750
00:39:52,960 --> 00:39:57,560
Speaker 1: Professor tetlock Of of Wharton UH describes what you just

751
00:39:58,120 --> 00:40:02,359
Speaker 1: discussed as weasel words that when you whenever you see

752
00:40:02,400 --> 00:40:06,760
Speaker 1: someone discussing something and they're hoping to be opaquely wrong,

753
00:40:07,360 --> 00:40:11,440
Speaker 1: you'll hear them say there's a possibility, there's a better

754
00:40:11,520 --> 00:40:14,600
Speaker 1: than even chance. And when you step back and look

755
00:40:14,600 --> 00:40:18,480
Speaker 1: at all these statements. In reality, they have no meaning

756
00:40:18,600 --> 00:40:22,319
Speaker 1: because no matter what the outcome is, they get to say, well,

757
00:40:22,360 --> 00:40:25,200
Speaker 1: I told you that this was a possibility, it's happened,

758
00:40:25,640 --> 00:40:27,680
Speaker 1: and when it doesn't happen, it's like, well, I said

759
00:40:27,680 --> 00:40:30,200
Speaker 1: it was only a possibility. I could tell the Giants

760
00:40:30,239 --> 00:40:33,960
Speaker 1: may win the Super Bowl this year. They may, I

761
00:40:34,040 --> 00:40:37,040
Speaker 1: think they And if they don't, well I told you

762
00:40:37,280 --> 00:40:39,799
Speaker 1: they only all right. So if people want to find

763
00:40:39,800 --> 00:40:43,799
Speaker 1: your work, they could go to Musing on Markets by

764
00:40:43,960 --> 00:40:49,880
Speaker 1: Professor Aswadamadarin. Your YouTube channel, which is gonna be just

765
00:40:50,040 --> 00:40:53,000
Speaker 1: search for your name on YouTube should find it. For

766
00:40:53,040 --> 00:40:55,480
Speaker 1: those of you who enjoy this conversation, be sure and

767
00:40:55,560 --> 00:40:58,000
Speaker 1: check out our podcast extras. Will we keep the tape

768
00:40:58,080 --> 00:41:03,080
Speaker 1: rolling and continue chatting ab out all things valuation. We

769
00:41:03,200 --> 00:41:05,879
Speaker 1: love your comments and feedback. Be Shoran right to us

770
00:41:06,000 --> 00:41:10,439
Speaker 1: at m IB podcast at Bloomberg dot net. Check out

771
00:41:10,480 --> 00:41:13,720
Speaker 1: my daily column on Bloomberg View dot com, or follow

772
00:41:13,760 --> 00:41:18,000
Speaker 1: me on Twitter at Rid Halts. I'm Barry Ridlts. You've

773
00:41:18,040 --> 00:41:22,640
Speaker 1: been listening to Masters in Business on Bloomberg Radio, brought

774
00:41:22,680 --> 00:41:25,600
Speaker 1: to you by Bank of America. Merrill Lynch committed to

775
00:41:25,640 --> 00:41:29,080
Speaker 1: bringing higher finance to lower carbon named the most innovative

776
00:41:29,080 --> 00:41:32,520
Speaker 1: investment bank for climate change and sustainability by the banker.

777
00:41:32,880 --> 00:41:36,200
Speaker 1: That's the power of Global Connections. Bank of America North

778
00:41:36,239 --> 00:41:40,839
Speaker 1: America member f D I C. Welcome to the podcast. UM,

779
00:41:40,880 --> 00:41:42,160
Speaker 1: I don't know why I do this. I do this

780
00:41:42,200 --> 00:41:45,880
Speaker 1: every week. Thank you, professor. This is really so interesting.

781
00:41:46,280 --> 00:41:49,160
Speaker 1: I mentioned to you I want to mention on the air. Um.

782
00:41:49,200 --> 00:41:51,080
Speaker 1: There are a few cf as in my office. They

783
00:41:51,080 --> 00:41:55,359
Speaker 1: were all excited when they heard you were, UM coming

784
00:41:55,400 --> 00:41:57,600
Speaker 1: on the show. Oh, he's the man, he's the man

785
00:41:57,680 --> 00:42:00,640
Speaker 1: on valuation. So a lot of these quite sans came

786
00:42:00,719 --> 00:42:03,480
Speaker 1: from them. They make me look much smarter than I

787
00:42:03,520 --> 00:42:06,879
Speaker 1: actually am. Um, let's talk about some before I get

788
00:42:06,880 --> 00:42:09,120
Speaker 1: into my favorite questions. And I know only have you

789
00:42:09,920 --> 00:42:11,960
Speaker 1: for a finite amount of time. Let's let's see if

790
00:42:12,000 --> 00:42:15,520
Speaker 1: I could find some of the questions that we skipped

791
00:42:15,560 --> 00:42:20,480
Speaker 1: through before that I thought were, um, really interesting. So

792
00:42:21,960 --> 00:42:24,520
Speaker 1: one of the questions that came from one of our

793
00:42:24,560 --> 00:42:27,319
Speaker 1: c f A s Hey, there are certain companies that

794
00:42:27,360 --> 00:42:31,640
Speaker 1: are cheap, aren't They cheap for a reason? And most

795
00:42:31,640 --> 00:42:33,719
Speaker 1: companies that are cheap are cheaper a reason. The whole

796
00:42:33,760 --> 00:42:36,880
Speaker 1: idea in investing is to separate the five percent of

797
00:42:36,920 --> 00:42:39,560
Speaker 1: companies that are cheap that shouldn't be cheap from the

798
00:42:39,640 --> 00:42:41,880
Speaker 1: ninety percent that are cheap for a good reason. So

799
00:42:42,000 --> 00:42:45,160
Speaker 1: you think the best approach for someone who's an expert

800
00:42:45,160 --> 00:42:50,160
Speaker 1: in valuation is to take a value oriented investment tact

801
00:42:50,239 --> 00:42:52,719
Speaker 1: that that's the best strategy. But if you're not going

802
00:42:52,760 --> 00:42:55,240
Speaker 1: to be even one of those indexers. But I'm saying,

803
00:42:55,360 --> 00:42:58,520
Speaker 1: value oriented to me, is a much broader concept than

804
00:42:58,800 --> 00:43:01,840
Speaker 1: value oriented to know, old time value investor. So who

805
00:43:02,200 --> 00:43:06,120
Speaker 1: Warren Buffett do you think that's a or Benjamin Graham

806
00:43:06,440 --> 00:43:09,560
Speaker 1: old time? You're looking at it slightly differently, And here's

807
00:43:09,560 --> 00:43:12,680
Speaker 1: the difference. They actually want to buy companies where the

808
00:43:12,719 --> 00:43:15,279
Speaker 1: price is less than the value. What's what's already on

809
00:43:15,320 --> 00:43:17,280
Speaker 1: the ground. I mean, if you think about the classic

810
00:43:17,360 --> 00:43:21,440
Speaker 1: net net strategy, the ten screens, what Ben Graham wanted

811
00:43:21,520 --> 00:43:24,000
Speaker 1: was a company which had a hundred dollars in cash,

812
00:43:24,400 --> 00:43:27,000
Speaker 1: no doubt, andrust trading at fifty dollars. How many of

813
00:43:27,040 --> 00:43:30,560
Speaker 1: those are that exactly right? So, in a sense, the

814
00:43:30,560 --> 00:43:33,640
Speaker 1: the sphere of old time value investing each year gets

815
00:43:33,960 --> 00:43:36,319
Speaker 1: smaller and smaller. So when I was in Omaha last

816
00:43:36,400 --> 00:43:39,040
Speaker 1: year and I was talking to the portfolio mans, because

817
00:43:39,239 --> 00:43:41,520
Speaker 1: for some reason, when they were pulled, they picked me

818
00:43:41,560 --> 00:43:43,520
Speaker 1: as one of the people they wanted to listen to.

819
00:43:43,600 --> 00:43:46,640
Speaker 1: They probably would never invite me back again. And I said,

820
00:43:46,719 --> 00:43:50,400
Speaker 1: old time value investing suffers from three proms. One is

821
00:43:50,440 --> 00:43:54,279
Speaker 1: it's extraordinarily rigid. Right, if all these rules right, it's

822
00:43:54,400 --> 00:43:58,200
Speaker 1: very ritualistic. You've got to go through all the rituals

823
00:43:58,200 --> 00:44:01,480
Speaker 1: of being a value investor. And it's very righteous. They

824
00:44:01,520 --> 00:44:04,200
Speaker 1: believe they're the chosen ones, right, they've done the right thing,

825
00:44:04,600 --> 00:44:07,160
Speaker 1: and they think the rest of the world deserves They're

826
00:44:07,239 --> 00:44:09,480
Speaker 1: very puritanical. Right, They're going to get it, and they

827
00:44:09,560 --> 00:44:12,800
Speaker 1: deserve and they want the text. So they're actually waiting

828
00:44:12,840 --> 00:44:15,319
Speaker 1: for the tech stalk collapse because they can wag their

829
00:44:15,320 --> 00:44:18,520
Speaker 1: fingers and say I told you so, So you told

830
00:44:18,560 --> 00:44:20,600
Speaker 1: that to them, and and that's why you think you're

831
00:44:20,600 --> 00:44:24,680
Speaker 1: not getting invited back. They did they chuckle or well.

832
00:44:24,719 --> 00:44:28,400
Speaker 1: I think that they they know in their heart of

833
00:44:28,480 --> 00:44:32,560
Speaker 1: hearts that this is I mean, it's it's I think

834
00:44:32,560 --> 00:44:34,880
Speaker 1: there are enough people in there who got past the

835
00:44:34,920 --> 00:44:38,160
Speaker 1: self delusion, who know, in a moment of honesty, that

836
00:44:38,320 --> 00:44:41,640
Speaker 1: this is what's crippling old time value investing. Now, when

837
00:44:41,640 --> 00:44:44,920
Speaker 1: you say crippling, some of the old time value investors,

838
00:44:44,960 --> 00:44:47,040
Speaker 1: and I guess Warren Buffett has to be in that group,

839
00:44:47,400 --> 00:44:49,880
Speaker 1: have done fairly well for themselves. And in fact, the

840
00:44:49,920 --> 00:44:53,120
Speaker 1: fact that we keep going back to Warren Buffett as

841
00:44:53,160 --> 00:44:56,919
Speaker 1: the name more improving. He's more revealing than anything else.

842
00:44:56,960 --> 00:44:59,799
Speaker 1: And even Warrant's not been warrened for quite a while.

843
00:45:00,000 --> 00:45:02,040
Speaker 1: It's been for the last ten years. He's played a

844
00:45:02,120 --> 00:45:05,080
Speaker 1: very different game, and it's an insider game where he

845
00:45:05,120 --> 00:45:08,560
Speaker 1: gets special deals from companies, and he gets fantastic, fantastic

846
00:45:08,680 --> 00:45:12,400
Speaker 1: deals because he brings the warm Buffett good housekeeping seal

847
00:45:12,400 --> 00:45:16,239
Speaker 1: of approvingly, they're buying credibility. That's when I love the

848
00:45:16,280 --> 00:45:19,760
Speaker 1: fact that when you compare Lehman Brothers and Goldman Sachs,

849
00:45:20,440 --> 00:45:24,960
Speaker 1: Lehman Brothers turn Dick Fold rejected a warm buffet lifeline

850
00:45:25,400 --> 00:45:27,880
Speaker 1: and subsequently went out of business. In Goldman Sacks is

851
00:45:27,920 --> 00:45:30,800
Speaker 1: doing better than ever because they took the buffet lifeline.

852
00:45:30,880 --> 00:45:32,839
Speaker 1: In fact, I'll give you a statistic that I think

853
00:45:33,080 --> 00:45:37,920
Speaker 1: is very indicative of value there if you compare active

854
00:45:38,000 --> 00:45:41,120
Speaker 1: value investors, these old time value investors to a value

855
00:45:41,320 --> 00:45:44,719
Speaker 1: investing index fund. Because value investing index fund. Basically, what

856
00:45:44,760 --> 00:45:46,279
Speaker 1: you do is you just buy all the low price

857
00:45:46,320 --> 00:45:48,359
Speaker 1: to bookstocks and these talks. You put them in an

858
00:45:48,360 --> 00:45:54,320
Speaker 1: index fund. The average active value investor underperforms the value

859
00:45:54,320 --> 00:45:58,839
Speaker 1: index fund by about one and a half percent base.

860
00:45:59,320 --> 00:46:06,839
Speaker 1: The average active growth investment investor underperforms the average growth

861
00:46:06,880 --> 00:46:10,160
Speaker 1: fund by only fifty basis points. So they both underperformed.

862
00:46:10,280 --> 00:46:13,960
Speaker 1: But the average value investor actually and wonder why that is?

863
00:46:14,000 --> 00:46:15,640
Speaker 1: And I think the reason is very simple. What does

864
00:46:15,640 --> 00:46:18,360
Speaker 1: an old time value investor bring to the table that's unique.

865
00:46:18,560 --> 00:46:21,239
Speaker 1: Anybody can compute low p right or low price to

866
00:46:21,280 --> 00:46:25,360
Speaker 1: book or go the financial ratios. So at least a

867
00:46:25,400 --> 00:46:28,000
Speaker 1: growth investor might have a chance because you've got to

868
00:46:28,040 --> 00:46:31,360
Speaker 1: think about or they could they could have some competitive

869
00:46:31,360 --> 00:46:34,200
Speaker 1: advantage and assessing a market and making judgments and whether

870
00:46:34,200 --> 00:46:38,160
Speaker 1: wober will succeed or not. So the message I left

871
00:46:38,160 --> 00:46:40,120
Speaker 1: them is, don't be so right, is because the average

872
00:46:40,160 --> 00:46:43,440
Speaker 1: growth investor actually does much better. You know, they both underperforming,

873
00:46:43,719 --> 00:46:47,480
Speaker 1: both underperform. At least they're less underperforming than you are.

874
00:46:47,640 --> 00:46:52,520
Speaker 1: So so let's talk about those um dimensions. Let's talk

875
00:46:52,560 --> 00:46:57,799
Speaker 1: about the Farmer French factor model. You talked about a

876
00:46:57,840 --> 00:47:01,759
Speaker 1: liquid small cap stuffs. We know small cap is a

877
00:47:01,800 --> 00:47:06,760
Speaker 1: factor that tends to outperform. We also know valuation tends

878
00:47:06,800 --> 00:47:10,040
Speaker 1: to outprice price book right, and then the same thing

879
00:47:10,080 --> 00:47:14,520
Speaker 1: with quality if you eliminate the high debt laden companies.

880
00:47:14,920 --> 00:47:20,040
Speaker 1: So so, given what we know about those those factors, um,

881
00:47:20,080 --> 00:47:23,480
Speaker 1: what is that telling us about just straight up valuation

882
00:47:24,040 --> 00:47:26,200
Speaker 1: based investment? I think two things. One is we have

883
00:47:26,239 --> 00:47:28,239
Speaker 1: too much data now and part of the problems you

884
00:47:28,280 --> 00:47:30,879
Speaker 1: have so much data and everybody's looking. Every year there's

885
00:47:30,880 --> 00:47:33,480
Speaker 1: a new factor that people see up to six smart

886
00:47:33,560 --> 00:47:37,120
Speaker 1: datas and you know, so now you they've added momentum,

887
00:47:37,160 --> 00:47:40,880
Speaker 1: and they've added there's just six is three factor model,

888
00:47:41,000 --> 00:47:43,719
Speaker 1: and so you could create any And that's why when

889
00:47:43,760 --> 00:47:48,799
Speaker 1: people talk about these these index funds, which are they're

890
00:47:48,800 --> 00:47:50,879
Speaker 1: early quasi index funds where you do a little bit

891
00:47:50,920 --> 00:47:54,160
Speaker 1: of active stuff on the side. You're just not buying

892
00:47:54,160 --> 00:47:58,040
Speaker 1: it based on market cap. You basically using different basically

893
00:47:58,160 --> 00:48:02,120
Speaker 1: tilted You're they're using the proxy data from the past.

894
00:48:02,160 --> 00:48:05,400
Speaker 1: And here's my cautionary note. Any time use any of

895
00:48:05,440 --> 00:48:09,040
Speaker 1: these things, you're implicitly assuming mean reversion, right. I mean, ever,

896
00:48:09,239 --> 00:48:11,399
Speaker 1: if you look at ninety percent of investing it's based

897
00:48:11,400 --> 00:48:15,760
Speaker 1: on and mean reversion worked incredibly well in the US

898
00:48:15,840 --> 00:48:21,000
Speaker 1: market in the twentieth century, except for steam companies and levels.

899
00:48:21,000 --> 00:48:22,920
Speaker 1: And the reason it did was if you look at

900
00:48:22,960 --> 00:48:26,360
Speaker 1: the history of economic growth over the centuries and you

901
00:48:26,440 --> 00:48:30,280
Speaker 1: plot the US between and two thousand and that graph.

902
00:48:30,560 --> 00:48:33,680
Speaker 1: On that graph, it's like watching a patient going to

903
00:48:33,680 --> 00:48:36,680
Speaker 1: a coma because if you look at it typical, you know,

904
00:48:36,719 --> 00:48:38,600
Speaker 1: when you go to a hospital, they say that it

905
00:48:38,880 --> 00:48:42,880
Speaker 1: is an unusual period of history, an incredibly stable period,

906
00:48:43,520 --> 00:48:46,759
Speaker 1: which made me reverse the whole post war exactly from

907
00:48:46,800 --> 00:48:49,360
Speaker 1: not because you were the global economy, you could do it.

908
00:48:49,560 --> 00:48:53,560
Speaker 1: And I remember recessions used to be so predictable. You

909
00:48:53,600 --> 00:48:55,800
Speaker 1: have a recession and six months in this would happen,

910
00:48:55,800 --> 00:48:58,440
Speaker 1: and three months later and it was it was almost

911
00:48:58,560 --> 00:49:03,880
Speaker 1: like you were on on a regular So many of

912
00:49:03,920 --> 00:49:06,800
Speaker 1: these things that we know work from the farmer friends

913
00:49:06,880 --> 00:49:11,399
Speaker 1: are based on looking at US data from through two

914
00:49:11,400 --> 00:49:13,760
Speaker 1: thousand two, so you have to look at out sample data,

915
00:49:13,760 --> 00:49:17,160
Speaker 1: and not even with the out sample data, I think

916
00:49:17,200 --> 00:49:20,879
Speaker 1: there's been a structural shift in the world. The US

917
00:49:20,960 --> 00:49:22,759
Speaker 1: is no longer the center of the universe. To me,

918
00:49:22,840 --> 00:49:25,840
Speaker 1: the wake up moment is two thousand and eight. I

919
00:49:26,000 --> 00:49:29,040
Speaker 1: describe September twelve to two tho eight, that's a Friday

920
00:49:29,040 --> 00:49:31,880
Speaker 1: before the Lehman collapse, as the last day of innocence

921
00:49:31,960 --> 00:49:34,400
Speaker 1: for me. That's interesting because I used to describe the

922
00:49:34,440 --> 00:49:37,400
Speaker 1: world as developer markets versus emerging markets. I used to

923
00:49:37,480 --> 00:49:40,400
Speaker 1: draw on meter version, And the lesson I got from

924
00:49:40,440 --> 00:49:43,160
Speaker 1: two thousand and eight was and maybe there's been a

925
00:49:43,200 --> 00:49:46,279
Speaker 1: structural shift, and all these things that look like they

926
00:49:46,360 --> 00:49:48,640
Speaker 1: work will continue to look like they work, but if

927
00:49:48,680 --> 00:49:52,719
Speaker 1: you put your money behind them, they might not work

928
00:49:52,760 --> 00:49:56,680
Speaker 1: anymore because you're looking at a very different economic model.

929
00:49:57,200 --> 00:50:01,160
Speaker 1: So I'm wary of all these because it's so easy

930
00:50:01,200 --> 00:50:04,120
Speaker 1: to make money on paper. So so how come more

931
00:50:04,160 --> 00:50:06,560
Speaker 1: people are not beating the market. If if all these

932
00:50:06,600 --> 00:50:10,719
Speaker 1: things beat the market, why is the average active investors

933
00:50:10,760 --> 00:50:13,879
Speaker 1: still under performing the market by one to I mean,

934
00:50:14,360 --> 00:50:17,360
Speaker 1: I've never seen active investing in the state of angst

935
00:50:17,680 --> 00:50:21,640
Speaker 1: that it's in right now. I mean, I know active investors,

936
00:50:21,640 --> 00:50:23,799
Speaker 1: and these are really smart people. Who mean I mean,

937
00:50:24,120 --> 00:50:26,359
Speaker 1: I know I've known them for thirty years and they

938
00:50:26,400 --> 00:50:29,480
Speaker 1: were They've always been extraordinary confident people, at least in

939
00:50:29,520 --> 00:50:32,920
Speaker 1: their own abilities. They might say collectively, they all lose,

940
00:50:33,000 --> 00:50:37,280
Speaker 1: but we win because those people are having second thoughts

941
00:50:37,280 --> 00:50:40,000
Speaker 1: about what they they They might never mentioned with their clients.

942
00:50:40,000 --> 00:50:44,560
Speaker 1: Of course they couldn't do it. But when I add

943
00:50:44,640 --> 00:50:46,279
Speaker 1: them in an honest moment, they said, I don't know

944
00:50:46,320 --> 00:50:48,759
Speaker 1: what I'm doing in this listeness anything. Charlie Ellis has

945
00:50:48,800 --> 00:50:55,040
Speaker 1: been talking about the paradox of skill since the nineties seventies,

946
00:50:55,080 --> 00:50:59,600
Speaker 1: the right, the whole idea that it's not that these

947
00:50:59,600 --> 00:51:02,640
Speaker 1: people are bad or dumb, it's that there are so

948
00:51:02,680 --> 00:51:06,279
Speaker 1: many of them and they're smart and good. That advantage

949
00:51:06,280 --> 00:51:10,839
Speaker 1: goes away. If everybody is really well schooled and well

950
00:51:10,960 --> 00:51:15,280
Speaker 1: educated and intelligent and and really skillful, how can that

951
00:51:15,719 --> 00:51:18,319
Speaker 1: group essentially you beat anyone else. And he has a

952
00:51:18,400 --> 00:51:20,960
Speaker 1: very simple way to think about, you know, Tom Friedman

953
00:51:21,000 --> 00:51:23,680
Speaker 1: at this world becoming the investment world has become a

954
00:51:23,680 --> 00:51:26,719
Speaker 1: flatter place. I tell people in six if you're an

955
00:51:26,760 --> 00:51:29,080
Speaker 1: investor in New York, you started off already with a

956
00:51:29,120 --> 00:51:32,279
Speaker 1: competitive advantage over a guy in Des Moines, Iowa. Because

957
00:51:32,320 --> 00:51:35,440
Speaker 1: the sec offices you could get to them, right, you

958
00:51:35,440 --> 00:51:37,600
Speaker 1: can go to get to get to a ten K

959
00:51:37,760 --> 00:51:40,040
Speaker 1: or a ten Q. You actually had to show up

960
00:51:40,040 --> 00:51:42,359
Speaker 1: at the offices and get the physical copy. There were

961
00:51:42,360 --> 00:51:48,640
Speaker 1: no online PDF versions, so you had competitive advantages coming

962
00:51:48,680 --> 00:51:52,760
Speaker 1: from location, from where you work from. As the data

963
00:51:52,760 --> 00:51:56,320
Speaker 1: has become more accessible and everybody has, you know, computers

964
00:51:56,360 --> 00:51:58,160
Speaker 1: that they can use, and you can't claim I'm the

965
00:51:58,200 --> 00:52:00,600
Speaker 1: only main frame computer in the world now have it?

966
00:52:01,200 --> 00:52:03,920
Speaker 1: The words become a flatter place. So it doesn't surprise

967
00:52:04,000 --> 00:52:08,160
Speaker 1: me that the payoff to active investing has dropped. And

968
00:52:08,160 --> 00:52:10,560
Speaker 1: the more people tried, the worst they're going to do

969
00:52:10,640 --> 00:52:13,600
Speaker 1: because as they tried, they're expanding more resources to kind

970
00:52:13,600 --> 00:52:17,000
Speaker 1: of run in place. So I think Charlie was prescient

971
00:52:17,080 --> 00:52:19,000
Speaker 1: when he wrote that. Not that was way back, but

972
00:52:19,080 --> 00:52:20,959
Speaker 1: I think it's kind of caught up with the word now.

973
00:52:22,000 --> 00:52:24,680
Speaker 1: So um god, there's so many other questions I want

974
00:52:24,680 --> 00:52:27,480
Speaker 1: to plow through before I get to my my run

975
00:52:27,520 --> 00:52:33,239
Speaker 1: of favorite questions, my standard questions. UM, let me ask

976
00:52:33,280 --> 00:52:37,160
Speaker 1: you one or two other questions about we talked about Tesla,

977
00:52:37,239 --> 00:52:44,520
Speaker 1: We talked about um Amazon in in the original forecasts

978
00:52:44,760 --> 00:52:48,719
Speaker 1: or or or strategic plan that Besos made. I don't

979
00:52:48,760 --> 00:52:52,799
Speaker 1: recall ever reading anything about the cloud based computing and

980
00:52:52,840 --> 00:52:56,680
Speaker 1: how they had built such a substantial infrastructure on their own.

981
00:52:57,719 --> 00:53:00,520
Speaker 1: How did the idea of of hey, let's sell this

982
00:53:00,640 --> 00:53:03,759
Speaker 1: to people so we can monetize something that we have

983
00:53:03,840 --> 00:53:06,520
Speaker 1: to do ourselves. When did that come along? And that's

984
00:53:06,640 --> 00:53:09,239
Speaker 1: a five or ten billion dollar business, isn't it? And

985
00:53:09,280 --> 00:53:11,719
Speaker 1: I think it's in that sense of Amazon in the

986
00:53:11,800 --> 00:53:14,840
Speaker 1: nineties never talked about because it was a book retailer.

987
00:53:15,160 --> 00:53:19,680
Speaker 1: Seven really in the letter. In the letter you never

988
00:53:19,800 --> 00:53:23,080
Speaker 1: mentioned that there were a book He said, whatever business

989
00:53:23,160 --> 00:53:27,120
Speaker 1: we're in, we're going to go after businesses that we

990
00:53:27,200 --> 00:53:32,680
Speaker 1: think our big businesses where the existing players are not

991
00:53:33,600 --> 00:53:36,040
Speaker 1: playing the game we think, and we're going to sell

992
00:53:36,080 --> 00:53:38,520
Speaker 1: stuff at costa below in that business. So in a

993
00:53:38,560 --> 00:53:40,360
Speaker 1: sense he was laying the framework for what was the

994
00:53:40,400 --> 00:53:44,600
Speaker 1: first retail business, then the entertainment business, now the cloud

995
00:53:44,600 --> 00:53:47,880
Speaker 1: computing business. Right it's it's it's the basic business models.

996
00:53:47,880 --> 00:53:50,880
Speaker 1: We're going to go after big businesses. If I were

997
00:53:50,920 --> 00:53:54,400
Speaker 1: a bank, my worst case scenarios that some of Amazon

998
00:53:54,480 --> 00:53:56,080
Speaker 1: decides that the next thing they want to do is

999
00:53:56,120 --> 00:53:59,640
Speaker 1: big time fintech. Let's face it, there's no business where

1000
00:53:59,680 --> 00:54:02,719
Speaker 1: there's s value added by the existing players in the game,

1001
00:54:02,920 --> 00:54:07,480
Speaker 1: for sure. And you think about Amazon has everybody's credit

1002
00:54:07,480 --> 00:54:10,080
Speaker 1: card on file, what do they have half a billion quireants,

1003
00:54:10,120 --> 00:54:11,880
Speaker 1: and they know a lot more about me than I

1004
00:54:11,920 --> 00:54:13,719
Speaker 1: ever want them to know. Right you open up an

1005
00:54:13,719 --> 00:54:16,400
Speaker 1: Amazon page, it's kind of creepy because they said, oh,

1006
00:54:16,480 --> 00:54:18,759
Speaker 1: you might want to look at these five items, just

1007
00:54:18,840 --> 00:54:21,839
Speaker 1: like Netflix. Right see. I find it less creepy from

1008
00:54:21,880 --> 00:54:24,880
Speaker 1: Amazon than I do from Facebook. So when I go

1009
00:54:24,960 --> 00:54:28,560
Speaker 1: to Amazon and um, Norah Jones has a new CD

1010
00:54:28,680 --> 00:54:31,960
Speaker 1: coming out, Amazon knows I bought a Norah Jones c

1011
00:54:32,120 --> 00:54:35,480
Speaker 1: D six years ago and it shows up in you

1012
00:54:35,520 --> 00:54:38,640
Speaker 1: may want to see these That's different than going to

1013
00:54:38,719 --> 00:54:41,880
Speaker 1: a hall. So I could kind of Hey, if I

1014
00:54:41,960 --> 00:54:43,680
Speaker 1: for kids who are listening, there used to be a

1015
00:54:43,680 --> 00:54:46,440
Speaker 1: store called Tower Records, and right by n y U

1016
00:54:46,640 --> 00:54:49,640
Speaker 1: on on Fourth Street and Broadway. If I walked into

1017
00:54:49,640 --> 00:54:52,120
Speaker 1: Tower Records and they said, hey, I know you. You

1018
00:54:52,160 --> 00:54:55,279
Speaker 1: were here for the Peter Gabriel album a few years ago.

1019
00:54:55,640 --> 00:54:59,240
Speaker 1: Here's a new one, that wouldn't necessarily be all that creepy.

1020
00:54:59,280 --> 00:55:02,600
Speaker 1: But when I so, We're redoing a room in the

1021
00:55:02,640 --> 00:55:07,360
Speaker 1: house and I'm searching for polls like nibs for a

1022
00:55:07,400 --> 00:55:10,920
Speaker 1: for a cabinet, and I'm not on Facebook. I'm in

1023
00:55:10,960 --> 00:55:15,279
Speaker 1: a wholly different part of the Internet. And then I

1024
00:55:15,360 --> 00:55:19,640
Speaker 1: show up on Facebook and here adds for that's really creepy.

1025
00:55:19,719 --> 00:55:22,600
Speaker 1: And that's why when I leave Facebook, I log out

1026
00:55:23,080 --> 00:55:25,839
Speaker 1: so they can't track me because I find that and

1027
00:55:25,880 --> 00:55:27,600
Speaker 1: I don't know if that's still true that used to

1028
00:55:27,600 --> 00:55:30,960
Speaker 1: be true. I find that really creepy, really obnoxious, and

1029
00:55:30,960 --> 00:55:32,880
Speaker 1: that's why I'm not an act. And you're right, Amazon,

1030
00:55:32,920 --> 00:55:36,080
Speaker 1: at least it's the commercial relationship, the stuff. You understand

1031
00:55:36,080 --> 00:55:38,520
Speaker 1: why they do and I've bought that stuff. That's the

1032
00:55:38,520 --> 00:55:40,879
Speaker 1: other thing is if I've bought books or CDs from

1033
00:55:40,880 --> 00:55:45,120
Speaker 1: Amazon and they say our recommendation engine said you bought this,

1034
00:55:45,160 --> 00:55:47,440
Speaker 1: and this and this, you might be interested in this.

1035
00:55:49,800 --> 00:55:53,359
Speaker 1: That's not a big intrusive leap as opposed to, oh,

1036
00:55:53,440 --> 00:55:56,719
Speaker 1: let's see how Susie's baby is doing. No, I don't

1037
00:55:56,960 --> 00:55:59,600
Speaker 1: need you to sell me this. You know, chrome and

1038
00:55:59,719 --> 00:56:02,720
Speaker 1: leather a pole. I decided not to get it, stopped

1039
00:56:02,719 --> 00:56:06,239
Speaker 1: following me around the internet. And I think that this

1040
00:56:06,600 --> 00:56:09,000
Speaker 1: goes to the heart of big data, right, because again,

1041
00:56:09,000 --> 00:56:11,719
Speaker 1: big data has become this buzzword that everybody throws out.

1042
00:56:12,280 --> 00:56:14,960
Speaker 1: Nine percent companies that claim to use big data have

1043
00:56:14,960 --> 00:56:17,719
Speaker 1: no idea what to do with it. I saw, you know,

1044
00:56:18,520 --> 00:56:22,440
Speaker 1: job job listing, for I think it was I don't

1045
00:56:22,480 --> 00:56:25,080
Speaker 1: know toys r US saying well, we want a big

1046
00:56:25,200 --> 00:56:27,600
Speaker 1: data specialist and saying, what the heck are you guys

1047
00:56:27,600 --> 00:56:30,160
Speaker 1: going to do with big data? The companies that are

1048
00:56:30,160 --> 00:56:34,279
Speaker 1: the biggest users of big data are Google, Facebook, Nextlix,

1049
00:56:34,360 --> 00:56:38,960
Speaker 1: and Amazon because to them, big data actually is collecting information.

1050
00:56:39,000 --> 00:56:42,680
Speaker 1: And Netflix actually is even bigger into big data because

1051
00:56:42,680 --> 00:56:47,200
Speaker 1: they're actually making shows based on because they remember, not recommending,

1052
00:56:47,239 --> 00:56:51,399
Speaker 1: they're actually going out building products because they can. They're

1053
00:56:51,400 --> 00:56:53,520
Speaker 1: not only did they know what you watch, they also

1054
00:56:53,600 --> 00:56:56,560
Speaker 1: know when you stopped watching it's it's again kind of creepy,

1055
00:56:56,560 --> 00:56:58,840
Speaker 1: you're going to show? They remember twenty seven minutes you

1056
00:56:58,840 --> 00:57:02,120
Speaker 1: gave up on the show. To actually track what shows

1057
00:57:02,160 --> 00:57:06,040
Speaker 1: you're watching, what shows you stopping watching, when you're stopping watching,

1058
00:57:06,160 --> 00:57:09,400
Speaker 1: what might have caused you to stop watching, and in

1059
00:57:09,400 --> 00:57:12,719
Speaker 1: a sense that incorporating all of that into trying to

1060
00:57:12,880 --> 00:57:15,600
Speaker 1: make shows that are just for you, right, which is

1061
00:57:15,640 --> 00:57:19,600
Speaker 1: so bizarre because if you read William Goldman in Confessions

1062
00:57:19,640 --> 00:57:24,760
Speaker 1: of a of a Screenwriter, he describes the quote I

1063
00:57:24,800 --> 00:57:28,080
Speaker 1: love and applies to investing. As much as Hollywood, nobody

1064
00:57:28,120 --> 00:57:31,760
Speaker 1: knows anything they passed on Star wars. No one wants

1065
00:57:31,840 --> 00:57:36,200
Speaker 1: to make it people. I think Indiana Jones was twelve.

1066
00:57:36,280 --> 00:57:38,920
Speaker 1: Studio said no to it, and he has a hundred

1067
00:57:38,920 --> 00:57:43,520
Speaker 1: examples of all these blockbuster films that left to the

1068
00:57:43,560 --> 00:57:46,240
Speaker 1: studio test and left to the big data people. Right,

1069
00:57:46,560 --> 00:57:49,920
Speaker 1: So it makes me nervous that the quality of as

1070
00:57:49,920 --> 00:57:52,600
Speaker 1: long as there are independents who can do that. On

1071
00:57:52,640 --> 00:57:57,000
Speaker 1: the other hand, I gotta say, in general, the Amazon

1072
00:57:57,080 --> 00:58:02,880
Speaker 1: recommendation engine pretty good. The net Flix recommendation engine really

1073
00:58:02,960 --> 00:58:08,080
Speaker 1: not bad. Um. And I find myself sometimes logging out

1074
00:58:08,120 --> 00:58:12,000
Speaker 1: of Google Search because I want to see the objective.

1075
00:58:12,360 --> 00:58:15,840
Speaker 1: So when you search for something that modifies the Google

1076
00:58:15,880 --> 00:58:19,479
Speaker 1: Search out algorithm, anything I've clicked on is actually if

1077
00:58:19,680 --> 00:58:23,200
Speaker 1: So I have a Firefox and a Chrome open up

1078
00:58:23,200 --> 00:58:25,640
Speaker 1: at once. I'm not logged into Firefox, but i am

1079
00:58:25,680 --> 00:58:28,200
Speaker 1: logged into Chrome. Because every now and then I'll search

1080
00:58:28,240 --> 00:58:31,600
Speaker 1: for something and I'll see the same results that I've

1081
00:58:31,640 --> 00:58:34,440
Speaker 1: seen previously come up. It's like, no, no, I'm looking

1082
00:58:34,440 --> 00:58:36,800
Speaker 1: for something new, not trying to find something I looked

1083
00:58:36,840 --> 00:58:39,800
Speaker 1: for before. I hop over to Firefox, and now it's

1084
00:58:39,800 --> 00:58:42,440
Speaker 1: a whole not logged into Google, it's a whole different

1085
00:58:42,480 --> 00:58:45,520
Speaker 1: run of results. So that raises a whole another question

1086
00:58:45,560 --> 00:58:48,920
Speaker 1: about how effective big data is and about the lines

1087
00:58:48,960 --> 00:58:52,240
Speaker 1: that are being crossed along the way, which is privacy.

1088
00:58:52,320 --> 00:58:55,040
Speaker 1: So when I hear people complain about privacy, I tell them, look,

1089
00:58:55,080 --> 00:58:58,320
Speaker 1: your privacy has gone already. It's you and I are

1090
00:58:58,320 --> 00:59:02,800
Speaker 1: both over fifty. We care about privacy. Kids happily open

1091
00:59:02,840 --> 00:59:06,040
Speaker 1: the kimono for wait, I get a free coupon. Here,

1092
00:59:06,200 --> 00:59:09,160
Speaker 1: you have access to every They don't think in terms

1093
00:59:09,200 --> 00:59:12,720
Speaker 1: of privacy, which may they may end up regretting that.

1094
00:59:13,120 --> 00:59:16,520
Speaker 1: They will, they will absolutely regret it because I think

1095
00:59:16,520 --> 00:59:18,920
Speaker 1: two things are happening. One is our visions are being

1096
00:59:19,000 --> 00:59:21,640
Speaker 1: narrowed because, as you said, the one of the advantages

1097
00:59:21,760 --> 00:59:27,520
Speaker 1: of not being directed to the things you like specifically

1098
00:59:27,960 --> 00:59:31,240
Speaker 1: look at you. So Pandora has a recommendation engine, which

1099
00:59:31,280 --> 00:59:35,000
Speaker 1: I think is great. But Spotify just started this thing

1100
00:59:35,120 --> 00:59:40,000
Speaker 1: where it's their new weekly playlist. Is hey, these are

1101
00:59:40,320 --> 00:59:43,920
Speaker 1: Obviously if you listen to all death metal, country music,

1102
00:59:43,960 --> 00:59:47,000
Speaker 1: probably isn't gonna work. But if you want to expand

1103
00:59:47,080 --> 00:59:51,040
Speaker 1: your horizons just a little bit, and that excitement of

1104
00:59:51,080 --> 00:59:54,880
Speaker 1: discovery gets lost if you're only fed the same stuff.

1105
00:59:55,080 --> 00:59:57,120
Speaker 1: All right, I could talk about this stuff forever. But

1106
00:59:57,200 --> 00:59:59,840
Speaker 1: I have questions I have to get to with you.

1107
01:00:00,360 --> 01:00:02,680
Speaker 1: Of course, I think people want to hear less of

1108
01:00:02,720 --> 01:00:05,800
Speaker 1: me and more of you, so you know, let me

1109
01:00:05,840 --> 01:00:09,640
Speaker 1: find my favorite questions. Um, we got through a lot

1110
01:00:09,640 --> 01:00:13,800
Speaker 1: of stuff, even if we didn't get through everything. By

1111
01:00:13,800 --> 01:00:16,160
Speaker 1: the way you people are hearing this now, you should

1112
01:00:16,200 --> 01:00:20,560
Speaker 1: realize there's like eight pages of questions in eighteen point fonts.

1113
01:00:20,640 --> 01:00:22,960
Speaker 1: So I don't have to swint when I when I

1114
01:00:23,000 --> 01:00:25,200
Speaker 1: see it. So you and I talked a little bit

1115
01:00:25,200 --> 01:00:28,280
Speaker 1: about this. You come from the southern part of India,

1116
01:00:28,320 --> 01:00:32,960
Speaker 1: you move to l a four um grad school, you

1117
01:00:33,040 --> 01:00:35,440
Speaker 1: teach there, you go get your n b A and

1118
01:00:35,720 --> 01:00:40,040
Speaker 1: your PhD. How do you give up the southern California

1119
01:00:40,160 --> 01:00:44,600
Speaker 1: climate and come to New York with our miserable winters? How? How?

1120
01:00:44,640 --> 01:00:47,360
Speaker 1: What are very reluctantly? I love New York as a city.

1121
01:00:47,400 --> 01:00:49,560
Speaker 1: I love the fact that it's never boring, that things

1122
01:00:49,600 --> 01:00:53,000
Speaker 1: are always happening. But I like leaving the city sometimes.

1123
01:00:53,240 --> 01:00:57,400
Speaker 1: I know the feeling, and I do miss southern California weather,

1124
01:00:57,520 --> 01:00:59,800
Speaker 1: especially on those days where I have to go out

1125
01:01:00,120 --> 01:01:04,280
Speaker 1: shovel snow. So it's up and I it's it's been

1126
01:01:04,320 --> 01:01:07,240
Speaker 1: thirty one years in New York, and I think it's time.

1127
01:01:07,560 --> 01:01:09,880
Speaker 1: I'm at a stage in my life where I'm I

1128
01:01:10,320 --> 01:01:13,040
Speaker 1: don't have to be in New York all year. So

1129
01:01:13,720 --> 01:01:15,920
Speaker 1: at some point in time, more sooner rather than later,

1130
01:01:15,960 --> 01:01:19,160
Speaker 1: I'll be back in California and enjoying the weather exactly.

1131
01:01:19,280 --> 01:01:25,360
Speaker 1: So let's talk about I mentioned UM Berkeley where you

1132
01:01:25,360 --> 01:01:26,960
Speaker 1: were a lecturer, U c l A, where you got

1133
01:01:26,960 --> 01:01:29,360
Speaker 1: your m b A and PhD. Who were some of

1134
01:01:29,440 --> 01:01:32,800
Speaker 1: your early mentors who guided your career in law. It's

1135
01:01:32,840 --> 01:01:35,600
Speaker 1: interesting when I was a PhD student at U c

1136
01:01:35,760 --> 01:01:37,600
Speaker 1: l A. We had a professor of the University of

1137
01:01:37,720 --> 01:01:41,360
Speaker 1: Chicago visit every summer because every winter because he didn't

1138
01:01:41,400 --> 01:01:45,800
Speaker 1: want the guy called Jeane Farmer. And Jeane used to

1139
01:01:45,920 --> 01:01:49,760
Speaker 1: love to play tennis, and I was I used to

1140
01:01:49,800 --> 01:01:53,120
Speaker 1: teach tennis for a long time. I was given I

1141
01:01:53,160 --> 01:01:55,960
Speaker 1: was assigned as his r A simply because I could

1142
01:01:56,000 --> 01:01:59,960
Speaker 1: play tennis with him, and so it was I was exposed.

1143
01:02:00,600 --> 01:02:04,080
Speaker 1: So Jean was a very big influence on me. Um

1144
01:02:04,320 --> 01:02:06,320
Speaker 1: Dick Roll, who was at U c l A then,

1145
01:02:06,440 --> 01:02:09,040
Speaker 1: was a big researcher, and you know how difficult it

1146
01:02:09,120 --> 01:02:11,040
Speaker 1: was to test the Kapam and other models was a

1147
01:02:11,040 --> 01:02:14,120
Speaker 1: big influence Tom Copeland, who taught me corporate finance and

1148
01:02:14,200 --> 01:02:16,440
Speaker 1: then went on to McKinsey to write the first valuation

1149
01:02:16,520 --> 01:02:20,280
Speaker 1: but kind of embedded the liking for valuation and corporate finance.

1150
01:02:20,360 --> 01:02:23,000
Speaker 1: So I was lucky. It was an interesting and exciting

1151
01:02:23,080 --> 01:02:24,800
Speaker 1: time to be in finance. And I think in a

1152
01:02:24,880 --> 01:02:28,200
Speaker 1: sense it's um when I look at how finance has

1153
01:02:28,280 --> 01:02:31,080
Speaker 1: kind of changed over time. It's like any other discipline.

1154
01:02:31,120 --> 01:02:33,800
Speaker 1: When you start the discipline, you ask really big questions,

1155
01:02:33,880 --> 01:02:36,440
Speaker 1: like in physics a hundred years ago, your Einstein and

1156
01:02:36,480 --> 01:02:39,280
Speaker 1: Bore asking questions about the meaning of the universe. You

1157
01:02:39,320 --> 01:02:42,160
Speaker 1: look at a physics journal, now you've got seven people

1158
01:02:42,520 --> 01:02:46,440
Speaker 1: co writing a paper on a title that nobody outside

1159
01:02:46,440 --> 01:02:49,840
Speaker 1: physics can even understand what they're writing about. As disciplines age,

1160
01:02:50,200 --> 01:02:55,560
Speaker 1: they narrow and the same things happens. You get specialists,

1161
01:02:55,600 --> 01:02:57,840
Speaker 1: right So, and that's what's happened in finances. We have

1162
01:02:57,960 --> 01:03:01,000
Speaker 1: lots of specialists. And that's not through just in academia.

1163
01:03:01,000 --> 01:03:03,560
Speaker 1: It's happened in practice as well. The industry has matured,

1164
01:03:03,680 --> 01:03:06,680
Speaker 1: it's matured and it's created specialists, and we've lost something

1165
01:03:06,680 --> 01:03:09,400
Speaker 1: in the process. We've lost those generalists because when I

1166
01:03:09,440 --> 01:03:12,480
Speaker 1: first started working with investment banks in the nineteen eighties,

1167
01:03:12,520 --> 01:03:14,520
Speaker 1: I'd go into an investment bank and you'd have somebody

1168
01:03:14,520 --> 01:03:17,240
Speaker 1: who has a bankerbody could talk about the theater. You

1169
01:03:17,280 --> 01:03:20,080
Speaker 1: could talk about stock markets, you could talk about born markets,

1170
01:03:20,120 --> 01:03:23,520
Speaker 1: you could talk about history. And I mean the old

1171
01:03:23,560 --> 01:03:25,920
Speaker 1: notion of a renaissance man. I mean there were a

1172
01:03:25,920 --> 01:03:30,200
Speaker 1: few of them around, and they brought perspective to discussions

1173
01:03:30,280 --> 01:03:32,560
Speaker 1: that you don't have anymore. I've said in on banking

1174
01:03:32,600 --> 01:03:35,640
Speaker 1: discussions we have it doesn't really smart people around the table,

1175
01:03:36,000 --> 01:03:37,840
Speaker 1: but they're in an echo chamber where they talk to

1176
01:03:37,840 --> 01:03:40,560
Speaker 1: each other in the little segment of the world, and

1177
01:03:40,600 --> 01:03:44,400
Speaker 1: they've completely lost perspective. This guy is a biotech specialist.

1178
01:03:44,480 --> 01:03:47,760
Speaker 1: That guy only the software. It's not like anybody who's

1179
01:03:47,800 --> 01:03:50,560
Speaker 1: broad anymore. So I think we need some some you know.

1180
01:03:50,920 --> 01:03:52,880
Speaker 1: My newest book is coming out in two months. It's

1181
01:03:52,880 --> 01:03:56,680
Speaker 1: called Narrative and Numbers. It's about storytelling and valuation, because

1182
01:03:56,720 --> 01:04:01,040
Speaker 1: to me, it's become all Excel, spread sheets and models

1183
01:04:01,320 --> 01:04:04,920
Speaker 1: all the time. People have no sense of commons that

1184
01:04:04,960 --> 01:04:07,160
Speaker 1: they're not bringing common sense and they're not bringing in

1185
01:04:07,640 --> 01:04:10,080
Speaker 1: So the book I wrote was about how when I

1186
01:04:10,120 --> 01:04:12,320
Speaker 1: do valuation. I start with a story for the company

1187
01:04:12,320 --> 01:04:15,240
Speaker 1: with it's Tesla or Amazon, our doutsche bank, and the

1188
01:04:15,360 --> 01:04:19,040
Speaker 1: valuation comes out of this story, rather than me sitting

1189
01:04:19,040 --> 01:04:20,880
Speaker 1: in front of a spreadsheet and just making up numbers

1190
01:04:20,880 --> 01:04:24,520
Speaker 1: as I go along. That's really that's really interesting. Let's

1191
01:04:24,600 --> 01:04:28,800
Speaker 1: let's talk about the investors who influenced you. I referenced

1192
01:04:29,000 --> 01:04:32,640
Speaker 1: some value investors who you said, Hey, that's old school,

1193
01:04:32,680 --> 01:04:34,720
Speaker 1: and we don't know how well that works anymore. Who

1194
01:04:34,760 --> 01:04:36,919
Speaker 1: are the investors who have influenced I learned a little

1195
01:04:36,960 --> 01:04:40,439
Speaker 1: bit from everybody. I learned from Warren Buffett. I've learned

1196
01:04:40,480 --> 01:04:43,360
Speaker 1: the importance of having a core philosophy that you go

1197
01:04:43,440 --> 01:04:48,800
Speaker 1: back to when in doubt. The active money managers are

1198
01:04:48,840 --> 01:04:51,680
Speaker 1: running to have no core philosophy, have investment strategies. You

1199
01:04:51,720 --> 01:04:53,840
Speaker 1: ask them, what's your philosophy? They side by low ps

1200
01:04:53,840 --> 01:04:57,280
Speaker 1: talks and I said, that's not a philosophy, that's a strategy.

1201
01:04:57,520 --> 01:04:59,280
Speaker 1: And I think if you have a philosophy, it's a

1202
01:04:59,320 --> 01:05:01,840
Speaker 1: way of thinking of about markets, thinking about how markets

1203
01:05:01,880 --> 01:05:04,200
Speaker 1: make mistakes and why you should be able to take

1204
01:05:04,240 --> 01:05:08,320
Speaker 1: advantage of mistakes. So I've learned from watching people like

1205
01:05:08,360 --> 01:05:11,040
Speaker 1: Warren Buffett about how important it is to have a

1206
01:05:11,080 --> 01:05:14,200
Speaker 1: core philosophy that is yours, that's not somebody else's, that

1207
01:05:14,240 --> 01:05:17,840
Speaker 1: you can go back to. I've learned from Mike Mobuson

1208
01:05:17,960 --> 01:05:21,160
Speaker 1: was one of my favorite people to talk because when

1209
01:05:21,160 --> 01:05:24,200
Speaker 1: you talk about renaissance man, he's he's one of those, right.

1210
01:05:24,200 --> 01:05:26,880
Speaker 1: He can talk about philosophy, can talk about psychology, can

1211
01:05:26,880 --> 01:05:29,840
Speaker 1: talk about finance, you can talk. And I love talking

1212
01:05:30,040 --> 01:05:33,680
Speaker 1: to him simply because he tells me things that I said.

1213
01:05:33,720 --> 01:05:35,560
Speaker 1: You know what, I never thought about that it came

1214
01:05:35,600 --> 01:05:38,800
Speaker 1: from a different discipline, but everything is kind of related.

1215
01:05:39,600 --> 01:05:42,600
Speaker 1: I'll give an example. This summer, I was in Florence

1216
01:05:42,680 --> 01:05:46,440
Speaker 1: and I was not that you've got the Bruno Leski

1217
01:05:46,640 --> 01:05:50,280
Speaker 1: Dome in Florence is amazing dome. And I was looking

1218
01:05:50,280 --> 01:05:52,200
Speaker 1: at the dome in my As I was looking at

1219
01:05:52,200 --> 01:05:54,000
Speaker 1: the dome, I was thinking about the fact that Bruno

1220
01:05:54,040 --> 01:05:56,440
Speaker 1: Leski was He was an artist he loved and this

1221
01:05:56,640 --> 01:06:00,680
Speaker 1: dome required huge amounts of science and architecture, and he

1222
01:06:00,800 --> 01:06:03,000
Speaker 1: taught himself enough that he was able to build the dome.

1223
01:06:04,000 --> 01:06:06,520
Speaker 1: And I thought about how he was willing to leave

1224
01:06:06,760 --> 01:06:11,280
Speaker 1: his preferred habitat and go into these areas because he

1225
01:06:11,400 --> 01:06:15,479
Speaker 1: had to be multi skilled, and how much we need

1226
01:06:15,680 --> 01:06:19,200
Speaker 1: more people like that who leave their preferred habitat and

1227
01:06:19,280 --> 01:06:22,400
Speaker 1: move into areas that don't come easily to them and

1228
01:06:22,440 --> 01:06:25,200
Speaker 1: try at least to get conversant with those areas out

1229
01:06:25,200 --> 01:06:28,400
Speaker 1: of their comfort zone, learn a new new discipline, and

1230
01:06:28,400 --> 01:06:32,520
Speaker 1: then bring it back to where you start. Um. Everybody

1231
01:06:33,040 --> 01:06:36,040
Speaker 1: loves this question. What are some of your favorite books?

1232
01:06:36,600 --> 01:06:39,800
Speaker 1: Be they abound, investing, fiction, non fiction, it doesn't matter.

1233
01:06:40,440 --> 01:06:43,360
Speaker 1: I do. I mean I read. I've read Ben Graham,

1234
01:06:43,760 --> 01:06:47,760
Speaker 1: and I read it with a very different lens than

1235
01:06:47,920 --> 01:06:50,600
Speaker 1: most people. I don't read it for the techniques. Let's face,

1236
01:06:50,720 --> 01:06:54,280
Speaker 1: Ben Graham valued stocks as of their were bonds too.

1237
01:06:54,480 --> 01:06:57,680
Speaker 1: He was a natural fixed income guy who thought about

1238
01:06:57,680 --> 01:06:59,360
Speaker 1: how do I make a stock look like a bond?

1239
01:06:59,680 --> 01:07:02,760
Speaker 1: So of dividends with the equivalent of coupons. But what

1240
01:07:02,840 --> 01:07:05,640
Speaker 1: I get out of is a philosophy about investing, which

1241
01:07:05,720 --> 01:07:08,200
Speaker 1: is you've got to have the essence of values, faith

1242
01:07:08,240 --> 01:07:10,640
Speaker 1: that you if you believe that something is worth something,

1243
01:07:10,760 --> 01:07:13,920
Speaker 1: you shouldn't be letting the price system provide your feedback

1244
01:07:13,960 --> 01:07:18,480
Speaker 1: and change your So I like I like security analysis.

1245
01:07:18,680 --> 01:07:22,560
Speaker 1: Evaluation is a faith based exercisectly it's a faith based

1246
01:07:22,640 --> 01:07:25,120
Speaker 1: exercise because you have to faith in your value and

1247
01:07:25,240 --> 01:07:27,680
Speaker 1: faith that the market will correct its mistakes. And it's

1248
01:07:27,720 --> 01:07:30,440
Speaker 1: faith because you ask me for proof or either I

1249
01:07:30,480 --> 01:07:32,920
Speaker 1: can offer you nothing. Well, isn't that where mean reversion

1250
01:07:32,960 --> 01:07:37,760
Speaker 1: comes in? Hey, it's always eventually return to it always

1251
01:07:37,840 --> 01:07:41,400
Speaker 1: is what I don't historically. And see that's the problem

1252
01:07:41,440 --> 01:07:43,680
Speaker 1: is you ask me for guarantees. I can offer you data.

1253
01:07:43,720 --> 01:07:45,720
Speaker 1: I can offer you the past. But if you say

1254
01:07:45,880 --> 01:07:48,000
Speaker 1: is that going to happen for sure? And say I don't.

1255
01:07:48,120 --> 01:07:53,640
Speaker 1: So it's history plus faith equals future performance. That was saying, So,

1256
01:07:53,640 --> 01:07:56,760
Speaker 1: so what other books do you like? Um? I like?

1257
01:07:57,880 --> 01:08:00,360
Speaker 1: And this is going to be off kilter. I called

1258
01:08:00,440 --> 01:08:04,960
Speaker 1: David Liss, who writes books, their novels L I S

1259
01:08:04,960 --> 01:08:09,400
Speaker 1: T l I S S. And one of his books

1260
01:08:09,400 --> 01:08:11,840
Speaker 1: was A Conspiracy of Paper. It's a it's a book

1261
01:08:11,880 --> 01:08:16,160
Speaker 1: set in the days after the south Sea Bubble in London.

1262
01:08:16,200 --> 01:08:18,840
Speaker 1: It's a novel around that and it talks about how

1263
01:08:18,840 --> 01:08:21,719
Speaker 1: the bubble was created. Like people, so what would happen

1264
01:08:21,840 --> 01:08:24,360
Speaker 1: is south Sea Bubble is of course this this company

1265
01:08:24,400 --> 01:08:27,200
Speaker 1: that was created which really not for a purpose to

1266
01:08:27,280 --> 01:08:31,160
Speaker 1: be determined, for purpose to be determined, and it was there.

1267
01:08:31,280 --> 01:08:33,280
Speaker 1: It was a bubble in the sense people bought into

1268
01:08:33,280 --> 01:08:35,760
Speaker 1: the story and the price went up and collapsed. But

1269
01:08:35,840 --> 01:08:38,920
Speaker 1: he talked about how the people running the company would

1270
01:08:38,920 --> 01:08:43,000
Speaker 1: send people out to to two bars or you know,

1271
01:08:43,640 --> 01:08:48,439
Speaker 1: and two taverns and whispered to other people about what

1272
01:08:48,840 --> 01:08:51,800
Speaker 1: was private information, but whisper loud enough that other people

1273
01:08:51,840 --> 01:08:54,479
Speaker 1: could hear them. It was your CNBC of its death.

1274
01:08:54,560 --> 01:08:56,920
Speaker 1: It was I was gonna say, the first investor relationships

1275
01:08:57,080 --> 01:09:00,479
Speaker 1: exactly right. So it's so basically as I was reading it,

1276
01:09:00,520 --> 01:09:02,479
Speaker 1: I was talking about I was thinking about how little.

1277
01:09:02,640 --> 01:09:07,080
Speaker 1: So it's incredibly descriptive about how markets work then and

1278
01:09:07,240 --> 01:09:11,120
Speaker 1: how rumors got spread and how bubbles God created. And

1279
01:09:11,880 --> 01:09:15,200
Speaker 1: to me, when I look at how about, not much

1280
01:09:15,240 --> 01:09:18,639
Speaker 1: has changed, right, So, David Less conspiracy of paper, David

1281
01:09:18,720 --> 01:09:21,320
Speaker 1: List conspiracy. You know a lot of the classic books

1282
01:09:21,520 --> 01:09:26,960
Speaker 1: fifty two hundred years old. They Richard Wykoff had a book,

1283
01:09:27,000 --> 01:09:30,760
Speaker 1: I think, How I Trade Stocks and Bonds, And if

1284
01:09:30,760 --> 01:09:33,679
Speaker 1: you opened it up today and substitute of the word

1285
01:09:33,680 --> 01:09:38,559
Speaker 1: internet for telegram and and the word railroad for shipping,

1286
01:09:39,720 --> 01:09:43,040
Speaker 1: it would be no different than the lettle More books, right,

1287
01:09:43,080 --> 01:09:45,960
Speaker 1: I mean the madness of crowds. I mean there's I

1288
01:09:46,400 --> 01:09:50,759
Speaker 1: actually don't read much current stuff. I like Michael Lewis

1289
01:09:51,040 --> 01:09:53,720
Speaker 1: why because he can spend a great story everything. I

1290
01:09:53,760 --> 01:09:56,640
Speaker 1: don't think quite by it, because you can tell that

1291
01:09:56,920 --> 01:09:59,800
Speaker 1: he's such a good story writer that he has an agenda.

1292
01:10:00,040 --> 01:10:02,360
Speaker 1: He wants to go along with that agenda. So I

1293
01:10:02,400 --> 01:10:05,439
Speaker 1: try to fight it, whether it's Moneyball or flash Moneyball

1294
01:10:05,520 --> 01:10:07,360
Speaker 1: is hard to fight. It's it's hard to fight because

1295
01:10:07,400 --> 01:10:08,760
Speaker 1: the end of it is saying we should all be

1296
01:10:08,800 --> 01:10:11,720
Speaker 1: doing it, but then we've discovered the downside. Well, once

1297
01:10:11,760 --> 01:10:14,519
Speaker 1: every that goes back to Charlie Ellis, once we're all

1298
01:10:14,520 --> 01:10:18,360
Speaker 1: doing it, the competitive advantage goes goes away, right, So

1299
01:10:18,920 --> 01:10:21,320
Speaker 1: so I love Michael Lewis for that reason. I like

1300
01:10:21,479 --> 01:10:24,920
Speaker 1: James Stewart for all of the Barbarians, Barbarians of the

1301
01:10:24,960 --> 01:10:28,559
Speaker 1: Gate and the book, and so I like reading not

1302
01:10:28,840 --> 01:10:33,719
Speaker 1: so dry. I read very few books which are about

1303
01:10:33,720 --> 01:10:35,720
Speaker 1: how to get rich quickly. I don't read any of

1304
01:10:35,720 --> 01:10:39,600
Speaker 1: the Warren Buffett. There's always more lessons in the failures.

1305
01:10:39,960 --> 01:10:43,880
Speaker 1: When genius failed. That's that's one of the books I

1306
01:10:43,880 --> 01:10:47,800
Speaker 1: should have mass and then Bethany McLean's Smartest Guys in

1307
01:10:47,840 --> 01:10:50,960
Speaker 1: the room about Enron. The lessons are in the failures,

1308
01:10:51,000 --> 01:10:54,200
Speaker 1: not the lesson I'm looking for. To me, the big

1309
01:10:54,640 --> 01:10:56,839
Speaker 1: there are two things that get me into trouble investing.

1310
01:10:56,840 --> 01:10:59,640
Speaker 1: One is hubris, the second is over confidence. The two

1311
01:10:59,680 --> 01:11:03,120
Speaker 1: are right. Any book that reminds me that hubris and

1312
01:11:03,160 --> 01:11:07,280
Speaker 1: over confidence historically have been set ups for disaster is

1313
01:11:07,320 --> 01:11:10,960
Speaker 1: a good reminder for me, right, because it's so easy

1314
01:11:11,000 --> 01:11:13,719
Speaker 1: to get caught up in I know more than everybody else.

1315
01:11:13,760 --> 01:11:17,439
Speaker 1: I can put stuff into martws and not recognize how

1316
01:11:17,520 --> 01:11:22,840
Speaker 1: much of this stuff you don't control. No doubt about that. Um. So,

1317
01:11:23,040 --> 01:11:25,360
Speaker 1: this is a question that comes from a reader. You

1318
01:11:25,400 --> 01:11:28,800
Speaker 1: mentioned tennis. What do you do to keep mentally and

1319
01:11:29,160 --> 01:11:32,000
Speaker 1: or physically fit? What do you do to relax outside

1320
01:11:32,000 --> 01:11:35,559
Speaker 1: of the office? You know what? As I walk around

1321
01:11:35,560 --> 01:11:41,000
Speaker 1: New York, I get valuation cues almost everywhere, which is,

1322
01:11:41,040 --> 01:11:42,680
Speaker 1: you know, I'm looking at things and I'm trying to

1323
01:11:42,760 --> 01:11:45,919
Speaker 1: generalize from it. So to me, everything has some lessons

1324
01:11:46,000 --> 01:11:48,639
Speaker 1: for me in investing. Whether I'm at a ball game

1325
01:11:48,720 --> 01:11:50,840
Speaker 1: and I remember that we're going to a Yankee game

1326
01:11:50,920 --> 01:11:52,880
Speaker 1: and watching the This was in two thousand nine, with

1327
01:11:53,000 --> 01:11:55,799
Speaker 1: the New Yankee Citium. I took my kids and watching

1328
01:11:55,920 --> 01:11:58,639
Speaker 1: the Yankees run onto the field, and I'm thinking about

1329
01:11:58,720 --> 01:12:01,840
Speaker 1: least commitments and debt because what I'm thinking what I'm seeing,

1330
01:12:01,880 --> 01:12:04,240
Speaker 1: you know, Mark to Shara run out of first based

1331
01:12:04,240 --> 01:12:07,439
Speaker 1: seven years at twenty three million. Actually, I took the

1332
01:12:07,479 --> 01:12:09,479
Speaker 1: present value of commitments and in the infield and it

1333
01:12:09,479 --> 01:12:11,880
Speaker 1: came up with seven hundred million dollars of commitments and said,

1334
01:12:11,880 --> 01:12:13,720
Speaker 1: if I were buying the Yankees, this is a dead

1335
01:12:13,760 --> 01:12:16,080
Speaker 1: issue that I should be worrying about it. So to me,

1336
01:12:16,240 --> 01:12:18,759
Speaker 1: life is full of lessons. That's why the brunal Lesky

1337
01:12:18,840 --> 01:12:21,280
Speaker 1: Dome was a typical example. I'm looking at things and

1338
01:12:21,280 --> 01:12:23,680
Speaker 1: I'm saying, what can I learn about my Because to

1339
01:12:23,720 --> 01:12:26,320
Speaker 1: be a successful investor, I don't need to understand how

1340
01:12:26,320 --> 01:12:29,000
Speaker 1: Warren Buffett invest and how I need to understand what

1341
01:12:29,160 --> 01:12:32,640
Speaker 1: makes me tech, what makes me comfortable, what makes me uncomfortable.

1342
01:12:33,040 --> 01:12:35,640
Speaker 1: So I'm constantly looking for feedback from the world that

1343
01:12:35,680 --> 01:12:38,880
Speaker 1: I can use to kind of look inside myself, because

1344
01:12:38,880 --> 01:12:41,240
Speaker 1: self delusion is so easy when you're an investor. So

1345
01:12:41,280 --> 01:12:43,760
Speaker 1: that's that's a little bit of Peter Lynch. You know,

1346
01:12:43,880 --> 01:12:47,920
Speaker 1: invest what you know. Although the self delusion issue I'm

1347
01:12:47,960 --> 01:12:52,519
Speaker 1: always wrestling with I remember the middle of the financial crisis.

1348
01:12:52,600 --> 01:12:56,040
Speaker 1: You can walk into any Manhattan right restaurant, no reservation,

1349
01:12:57,000 --> 01:12:59,760
Speaker 1: best seat in the house, it didn't matter. It was empty.

1350
01:13:00,120 --> 01:13:02,960
Speaker 1: But then when things started to take pick up again,

1351
01:13:03,000 --> 01:13:06,719
Speaker 1: and if you mid by middle nine, everybody weren't back

1352
01:13:06,760 --> 01:13:10,040
Speaker 1: to normal, but things were normalizing. But that's in Manhattan

1353
01:13:10,080 --> 01:13:13,280
Speaker 1: where there was a trillion dollar bail out. A lot

1354
01:13:13,280 --> 01:13:16,559
Speaker 1: of it passed through this part of the country. There's

1355
01:13:16,600 --> 01:13:20,439
Speaker 1: a risk that our perspective in this little island off

1356
01:13:20,479 --> 01:13:22,840
Speaker 1: the east coast of America is huge. And I think

1357
01:13:22,880 --> 01:13:25,360
Speaker 1: when you look at things like Brexit, it should be

1358
01:13:25,400 --> 01:13:28,880
Speaker 1: a warning sign that we've lost perspective. Right we are,

1359
01:13:28,920 --> 01:13:30,960
Speaker 1: So it's easy in New York to say, well, this

1360
01:13:31,040 --> 01:13:33,519
Speaker 1: company should do this, We should be doing this on

1361
01:13:34,400 --> 01:13:38,960
Speaker 1: India aggregate incrementally, this is good for the country. What

1362
01:13:38,960 --> 01:13:42,559
Speaker 1: we're forgetting that's the average exactly. And we're talking about

1363
01:13:42,600 --> 01:13:47,559
Speaker 1: a fifty five year old steelworker in Pennsylvania. He gets

1364
01:13:47,600 --> 01:13:50,360
Speaker 1: little consolation out of the fact that free trade is

1365
01:13:50,400 --> 01:13:53,160
Speaker 1: going to create more world world when he says, where

1366
01:13:53,160 --> 01:13:55,559
Speaker 1: the heck am I going to get my paycheck next month?

1367
01:13:55,960 --> 01:13:58,200
Speaker 1: And I think politically, when you look at what's happening

1368
01:13:58,200 --> 01:14:02,720
Speaker 1: around the world. We're paying a price for almost deliberate

1369
01:14:02,920 --> 01:14:05,920
Speaker 1: blindness in the financial capitals of the world, the kind

1370
01:14:05,920 --> 01:14:08,600
Speaker 1: of cost that are being created in the rest of

1371
01:14:08,640 --> 01:14:12,599
Speaker 1: the country sometimes And and to me, that was in Brexit,

1372
01:14:12,640 --> 01:14:15,200
Speaker 1: when you saw the divide between London and the rest

1373
01:14:15,439 --> 01:14:19,719
Speaker 1: of the UK. In London, what was a sent voting

1374
01:14:19,760 --> 01:14:23,120
Speaker 1: against Brexit? It was a signal of how big the

1375
01:14:23,200 --> 01:14:27,439
Speaker 1: divide is between how the financial sector sometimes looks at

1376
01:14:27,479 --> 01:14:30,160
Speaker 1: things and how the rest of the market is looking

1377
01:14:30,200 --> 01:14:33,320
Speaker 1: at those same things. And I also think that folks

1378
01:14:33,400 --> 01:14:36,360
Speaker 1: like you and I, who tend to be rational and

1379
01:14:36,400 --> 01:14:40,080
Speaker 1: tend to be data driven, assume everybody else in the

1380
01:14:40,080 --> 01:14:43,080
Speaker 1: world is rational. And sometimes people you know, you talk

1381
01:14:43,160 --> 01:14:46,000
Speaker 1: to people who voted against Brexit, and you could demonstrate

1382
01:14:46,040 --> 01:14:49,160
Speaker 1: for them this is gonna hurt not just London, not

1383
01:14:49,280 --> 01:14:52,320
Speaker 1: just England. This will ultimately hurt you. You will have

1384
01:14:52,400 --> 01:14:55,080
Speaker 1: less wealth because of it. And the answers I don't care.

1385
01:14:55,120 --> 01:14:58,920
Speaker 1: I have to what the current situation is unacceptable. I

1386
01:14:58,960 --> 01:15:01,960
Speaker 1: had to vote my protests And in fact that's why

1387
01:15:01,960 --> 01:15:04,200
Speaker 1: I wrote the book on Storytelling a number crunching. I said,

1388
01:15:04,200 --> 01:15:06,439
Speaker 1: if you have a valuation, all you present his numbers.

1389
01:15:06,479 --> 01:15:09,040
Speaker 1: People will forget fifteen minutes after you leave the room.

1390
01:15:09,240 --> 01:15:12,160
Speaker 1: But the stories, the story stays, The story stays. And

1391
01:15:12,240 --> 01:15:15,040
Speaker 1: that's why I've had to incorporate my teaching, because when

1392
01:15:15,040 --> 01:15:17,240
Speaker 1: I first started teaching, I taught like a number cruncher,

1393
01:15:17,600 --> 01:15:20,080
Speaker 1: all numbers, all the time. And I learned that if

1394
01:15:20,080 --> 01:15:22,880
Speaker 1: people want the things that people I have students have

1395
01:15:22,960 --> 01:15:25,160
Speaker 1: been at that thirty one years and the things that

1396
01:15:25,160 --> 01:15:27,400
Speaker 1: I remember from my class, and not the numbers that

1397
01:15:27,439 --> 01:15:30,000
Speaker 1: they talked about, but a story I might have toldn anecdote.

1398
01:15:30,600 --> 01:15:35,000
Speaker 1: So the power of emotions is huge in this process.

1399
01:15:35,040 --> 01:15:39,280
Speaker 1: We've evolved to tell stories long before there was written language.

1400
01:15:39,680 --> 01:15:43,880
Speaker 1: Stories are memorable, and that's not an accident, it's it's

1401
01:15:43,880 --> 01:15:47,160
Speaker 1: actually built in um. So what do you do to

1402
01:15:47,160 --> 01:15:50,360
Speaker 1: to to stay physically and mentally fit? What do you

1403
01:15:50,400 --> 01:15:54,519
Speaker 1: do outside of work? I run, I watched a movie.

1404
01:15:54,600 --> 01:15:57,080
Speaker 1: I still play tennis. I still play tennis, and not

1405
01:15:57,240 --> 01:15:58,920
Speaker 1: as much as i'd like to because I don't like

1406
01:15:59,200 --> 01:16:01,559
Speaker 1: to play indoors, so that means I'm restricted to bang.

1407
01:16:01,600 --> 01:16:04,400
Speaker 1: And one reason I will probably moved to California sooner

1408
01:16:04,479 --> 01:16:07,080
Speaker 1: or later is because I can play tennis all the time.

1409
01:16:07,479 --> 01:16:11,040
Speaker 1: Down the street and outside. Um, I gotta work on

1410
01:16:11,080 --> 01:16:14,679
Speaker 1: my forehand. My backhand is good. My forehand is actually

1411
01:16:14,720 --> 01:16:17,240
Speaker 1: for me to reverse back hands. You can groove in

1412
01:16:17,360 --> 01:16:19,280
Speaker 1: much better. And once you get into the back end, right,

1413
01:16:19,800 --> 01:16:21,920
Speaker 1: That's what I'm saying. My back hand is fine. I

1414
01:16:21,960 --> 01:16:25,559
Speaker 1: got I'm also a lefty. There's actually an investment lesson

1415
01:16:25,640 --> 01:16:28,120
Speaker 1: in that too. The reason your forehand gives you more

1416
01:16:28,120 --> 01:16:31,320
Speaker 1: trouble is you try more stuff. There's more you try

1417
01:16:31,520 --> 01:16:34,879
Speaker 1: big stuff, there's more stuff that you're supposed to just stroking.

1418
01:16:35,760 --> 01:16:39,080
Speaker 1: And the same thing in investing. More activity often hurts

1419
01:16:39,280 --> 01:16:41,679
Speaker 1: you in investing when you try to do too much,

1420
01:16:42,160 --> 01:16:46,080
Speaker 1: and sometimes doing the more natural thing and not forcing

1421
01:16:46,160 --> 01:16:48,920
Speaker 1: more activity is the best thing you can do. And tennis,

1422
01:16:48,960 --> 01:16:51,200
Speaker 1: that shows up as your forehand versus your back hand. Right,

1423
01:16:51,240 --> 01:16:54,479
Speaker 1: I like you, I'm backwards. My forehand is better. The

1424
01:16:54,600 --> 01:16:58,800
Speaker 1: last two questions, UM, you work with a lot of

1425
01:16:58,840 --> 01:17:02,439
Speaker 1: millennials and college students. Someone comes to you and says, hey,

1426
01:17:02,479 --> 01:17:04,960
Speaker 1: I'm thinking about going into finance as a career. What

1427
01:17:05,080 --> 01:17:07,000
Speaker 1: sort of advice do you give them? I asked them

1428
01:17:07,000 --> 01:17:11,040
Speaker 1: why why? Because why do you want to go on

1429
01:17:11,120 --> 01:17:12,800
Speaker 1: to find it? And if their answer is because I

1430
01:17:12,840 --> 01:17:14,760
Speaker 1: think I can make more money. I said, come back

1431
01:17:14,800 --> 01:17:16,760
Speaker 1: with a better reason and then we'll talk about this,

1432
01:17:16,880 --> 01:17:20,799
Speaker 1: because I think especially the subset of finance, it's investment, banking,

1433
01:17:20,840 --> 01:17:23,320
Speaker 1: and consulting. The reality is, and I see this. My

1434
01:17:23,400 --> 01:17:26,920
Speaker 1: forecasts are all as I said, no grown up, but

1435
01:17:27,240 --> 01:17:29,880
Speaker 1: my seventeen year old is thinking about going to college.

1436
01:17:29,880 --> 01:17:32,800
Speaker 1: He's an amazing writer. He writes poetry that doesn't drive

1437
01:17:32,920 --> 01:17:35,720
Speaker 1: something that's beyond my comprehend because to me, poetry means

1438
01:17:35,720 --> 01:17:37,960
Speaker 1: the last word should always rhyme. He's a poet. He's

1439
01:17:38,000 --> 01:17:41,720
Speaker 1: a great writer. And he came to me and said, Dad,

1440
01:17:41,760 --> 01:17:44,920
Speaker 1: you know, I really love writing, but I think I

1441
01:17:44,920 --> 01:17:47,400
Speaker 1: should be going to a business school. And I said

1442
01:17:47,439 --> 01:17:50,519
Speaker 1: why and he said, well, that's how you can make money.

1443
01:17:51,120 --> 01:17:53,120
Speaker 1: I want. I want to end up in banking and

1444
01:17:53,120 --> 01:17:55,920
Speaker 1: make money like everybody else. And I said, you know, so,

1445
01:17:57,720 --> 01:18:00,120
Speaker 1: it's a terrible way to structure your life. I mean,

1446
01:18:00,120 --> 01:18:02,200
Speaker 1: if you're going to go into finance, do it because

1447
01:18:02,479 --> 01:18:06,760
Speaker 1: you like being in finance. You like doing valuation in them.

1448
01:18:06,800 --> 01:18:09,640
Speaker 1: And I think that that's I think the key to

1449
01:18:09,720 --> 01:18:11,360
Speaker 1: me is I think a lot of people are in

1450
01:18:11,439 --> 01:18:14,639
Speaker 1: finance and finance for exactly the wrong reasons and finance

1451
01:18:14,680 --> 01:18:16,840
Speaker 1: because this is what they don't like what they're doing.

1452
01:18:17,960 --> 01:18:20,280
Speaker 1: They don't enjoy it. It's not part of their solf.

1453
01:18:20,320 --> 01:18:22,920
Speaker 1: I mean, it's so to me. If they say that

1454
01:18:22,960 --> 01:18:26,479
Speaker 1: it's beyond money, then ask them what do you enjoy doing?

1455
01:18:26,520 --> 01:18:29,080
Speaker 1: Do you enjoy working on short term projects where you

1456
01:18:29,120 --> 01:18:31,000
Speaker 1: get a quick payoff or do you like working on

1457
01:18:31,040 --> 01:18:33,360
Speaker 1: something long term? Because that tells me whether they're better

1458
01:18:33,400 --> 01:18:36,760
Speaker 1: suited for a dealmaking job like M and A, or

1459
01:18:36,760 --> 01:18:39,080
Speaker 1: whether they're more suited to a consulting job you work

1460
01:18:39,080 --> 01:18:41,960
Speaker 1: with a client for multiple years. Because finance is such

1461
01:18:42,000 --> 01:18:44,000
Speaker 1: a big area that I can find a place for

1462
01:18:44,040 --> 01:18:46,040
Speaker 1: you depending on what your skill set is, what you

1463
01:18:46,120 --> 01:18:49,840
Speaker 1: enjoy doing. And our final question, what is it that

1464
01:18:49,880 --> 01:18:54,160
Speaker 1: you know about valuation and investing today that you wish

1465
01:18:54,160 --> 01:18:57,679
Speaker 1: you knew thirty years ago when you started teaching That

1466
01:18:59,760 --> 01:19:03,240
Speaker 1: I can be horribly wrong and be okay with it.

1467
01:19:03,320 --> 01:19:05,400
Speaker 1: I used to be afraid to be wrong, so I

1468
01:19:05,400 --> 01:19:08,280
Speaker 1: would pick companies where you're less likely to be wrong.

1469
01:19:09,000 --> 01:19:11,040
Speaker 1: I'm okay now with being wrong, and I know it's

1470
01:19:11,040 --> 01:19:14,599
Speaker 1: not necessarily my fault. It's the there's so many things

1471
01:19:14,640 --> 01:19:17,160
Speaker 1: out of my control, and I think I've understood how

1472
01:19:17,240 --> 01:19:20,320
Speaker 1: much luck is the dominant paradigm in this business, much

1473
01:19:20,320 --> 01:19:22,599
Speaker 1: as we'd like to tell the world that it's skillful

1474
01:19:22,640 --> 01:19:25,360
Speaker 1: people to make money and this is a game. Or

1475
01:19:25,400 --> 01:19:28,000
Speaker 1: if you're lucky, you can do some really crappy stuff

1476
01:19:28,040 --> 01:19:29,840
Speaker 1: and get away with it, and if you're not, you

1477
01:19:29,840 --> 01:19:33,400
Speaker 1: can do everything right and still and still end up

1478
01:19:33,400 --> 01:19:37,439
Speaker 1: losing money. So I've learned to be okay with people

1479
01:19:37,720 --> 01:19:40,120
Speaker 1: who invest off the seat of their pants, invest based

1480
01:19:40,160 --> 01:19:43,200
Speaker 1: on astrological science. If it works for them, it works

1481
01:19:43,200 --> 01:19:46,000
Speaker 1: for them. If you look use charts, if you can

1482
01:19:46,040 --> 01:19:48,840
Speaker 1: make money doing whatever you're doing, Who am I to

1483
01:19:48,880 --> 01:19:51,080
Speaker 1: come in and say that's a bad way of making money.

1484
01:19:51,080 --> 01:19:52,720
Speaker 1: Because you make a million dollars and I make a

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01:19:52,760 --> 01:19:55,720
Speaker 1: million dollars, we spend it the same way. So why

1486
01:19:55,760 --> 01:19:57,920
Speaker 1: does it matter that I make a million off long

1487
01:19:58,080 --> 01:20:00,840
Speaker 1: term investing based on value and you may chamillion based

1488
01:20:00,880 --> 01:20:04,880
Speaker 1: on supporting resistance lines. Well, the question is is it

1489
01:20:05,000 --> 01:20:09,080
Speaker 1: luck or repeatable? You know, the worst thing that happens

1490
01:20:09,120 --> 01:20:11,800
Speaker 1: to somebody is they walk into a casino, they win money,

1491
01:20:11,920 --> 01:20:14,840
Speaker 1: or the first goofy stocks they buy makes money, and

1492
01:20:14,840 --> 01:20:18,639
Speaker 1: they keep looking for that same sort of It's the process,

1493
01:20:18,720 --> 01:20:21,400
Speaker 1: not the outcome exactly, and you have to enjoy the process.

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01:20:21,400 --> 01:20:23,240
Speaker 1: And I tell people here's a very simple test and

1495
01:20:23,240 --> 01:20:25,320
Speaker 1: whether you should be an active investor. Let's say you

1496
01:20:25,360 --> 01:20:27,880
Speaker 1: get to be eighty five year in your deathbed, I

1497
01:20:27,920 --> 01:20:29,800
Speaker 1: come and ask you. I come and ask you about it.

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01:20:29,800 --> 01:20:31,920
Speaker 1: You've been an active investor for sixty years and you've

1499
01:20:31,920 --> 01:20:33,960
Speaker 1: actually made a nine percent return, and you could have

1500
01:20:33,960 --> 01:20:36,599
Speaker 1: made nine and a half percent investing in an index fund.

1501
01:20:37,880 --> 01:20:40,080
Speaker 1: So come and ask you whether you regret having spent

1502
01:20:40,120 --> 01:20:44,080
Speaker 1: a lifetime trying to pick stocks. If your answer is yes,

1503
01:20:44,760 --> 01:20:48,320
Speaker 1: my suggestion is you don't be an active investor because

1504
01:20:48,600 --> 01:20:51,719
Speaker 1: I invest actively, not because I hope to be rewarded.

1505
01:20:51,800 --> 01:20:54,400
Speaker 1: I invest actively because I truly enjoy this. It's a

1506
01:20:54,479 --> 01:20:58,519
Speaker 1: process that you like absolutely. Professor Domadaran, this has been

1507
01:20:58,760 --> 01:21:02,679
Speaker 1: absolutely fascinating. Thank you for being so generous with your time.

1508
01:21:03,240 --> 01:21:06,360
Speaker 1: We've been speaking with n y U Sterns, professor of

1509
01:21:06,920 --> 01:21:12,639
Speaker 1: finance who specializes in valuation. Professor Aswa Doma Dorn. If

1510
01:21:12,720 --> 01:21:15,400
Speaker 1: you enjoy this conversation, be sure and look up an

1511
01:21:15,400 --> 01:21:18,400
Speaker 1: inch or down an inch on iTunes and you'll see

1512
01:21:18,920 --> 01:21:22,479
Speaker 1: all of the other hundred and ten or so uh

1513
01:21:22,800 --> 01:21:25,880
Speaker 1: conversations we've had. I would be remiss if I did

1514
01:21:25,960 --> 01:21:30,160
Speaker 1: not forget. Thank my booker Taylor Riggs, my recording engineer,

1515
01:21:30,240 --> 01:21:34,559
Speaker 1: Charlie Vollmer, and my head of research Michael bat Nick.

1516
01:21:34,680 --> 01:21:36,840
Speaker 1: Be sure and send us an email and tell us

1517
01:21:37,120 --> 01:21:39,960
Speaker 1: what you would like to hear more of m IB

1518
01:21:40,160 --> 01:21:44,800
Speaker 1: podcast at Bloomberg dot net. I'm Barry rid Halts. You've

1519
01:21:44,800 --> 01:21:54,759
Speaker 1: been listening to Masters in Business on Bloomberg Radio, brought

1520
01:21:54,800 --> 01:21:57,599
Speaker 1: to you by Bank of America. Merrill Lynch. Seeing what

1521
01:21:57,720 --> 01:22:01,320
Speaker 1: others have seen, but uncovering what others may not. Global

1522
01:22:01,400 --> 01:22:05,040
Speaker 1: research that helps you harness disruption. Voted top global research

1523
01:22:05,080 --> 01:22:08,519
Speaker 1: firm five years running. Merrill Lynch, Pierce, Finner and Smith

1524
01:22:08,560 --> 01:22:09,280
Speaker 1: Incorporated