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

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

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Speaker 1: His name is Rob are Not and he is the

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Speaker 1: creator and chairman of Research Affiliates, a fundamental indexing firm

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Speaker 1: that has strategies that manage over two hundred billion dollars

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Speaker 1: in assets. Rob is perhaps best known for popularizing the

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Speaker 1: concept of fundamental indexing UH, often called smart data, although

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Speaker 1: he has uh takes issue with with that, um moniker.

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Speaker 1: He thinks it's just become a marketing term. Uh. This

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Speaker 1: is really a fascinating conversation. Every time I speak with Rob,

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Speaker 1: I learned something new. Uh. This time I discovered that

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Speaker 1: he has co authored a number of papers with the

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Speaker 1: great Peter Bernstein, which was something I I didn't previously know.

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Speaker 1: If you were at all interested in things like fundamental indexing,

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Speaker 1: what's wrong with market cap waiting? How large that sector

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Speaker 1: can grow? The advantages of factor based investing. And I

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Speaker 1: also learned that there are now over five hundreds such factors.

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Speaker 1: I had no idea. I knew it was hundreds, I

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Speaker 1: had no idea was that many. Uh. This is really

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Speaker 1: a masterclass in this space. So with no further ado.

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Speaker 1: Here is my conversation with Rob or Not. My extra

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Speaker 1: special guest this week is Rob are Not. He is

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Speaker 1: the founder and chairman of Research Affiliates, perhaps best known

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Speaker 1: for popularizing the concept of smart beta, better known or

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Speaker 1: better described as fundamental indexing uh rafie does not consider

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Speaker 1: market cap, focusing instead on four primary factors sales, profit, book, value,

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Speaker 1: and dividend are not formed the firm in O two

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Speaker 1: in Newport Beach, California. Investment strategies developed by Research Affiliates,

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Speaker 1: or Raphy as it's better known, manages over two hundred

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Speaker 1: billion dollars in assets. Rob are Not, Welcome back to Bloomberg.

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Speaker 1: Thank you. So I mentioned you started Research Affiliates and

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Speaker 1: I'm used to just calling it raffi is the nickname

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Speaker 1: everybody uses. Uh back in two thousand two. Could you

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Speaker 1: have imagined then that it's barely fifteen years later and

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Speaker 1: your strategies are now managing over two hundred billion dollars?

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Speaker 1: Did you have any expectation of how wildly successful smart

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Speaker 1: data would become? Those are two different questions. Did I

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Speaker 1: expect to be this successful? Yes, nobody starts a business

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Speaker 1: without a healthy dose of optimism. Okay, perhaps sometimes undeserved,

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Speaker 1: but but even still, that's a big chunk of change.

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Speaker 1: Did you expect to hit those sort of numbers this quickly? Um, Well,

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Speaker 1: I don't know how quick that is. Sixteen years is

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Speaker 1: a long time. But as for RAFI the fundamental index,

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Speaker 1: UH and it's growth. I couldn't have anticipated that because

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Speaker 1: we hadn't come up with the idea yet. When when

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Speaker 1: did that first roll out? We first thought about the

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Speaker 1: idea of indexing in non capitalization ways in two thousand three.

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Speaker 1: We formalized our research and UH completed development of the

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Speaker 1: idea by mid O four. We were live with assets

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Speaker 1: by the end of O four. First fund was launched

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Speaker 1: by Pimco and OH five, first published index was by

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Speaker 1: Footsie UM in November of oh five. So barely a

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Speaker 1: dozen years. That's even faster. Scale up to not a

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Speaker 1: lot of fun scale up to two and a dozen years. No,

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Speaker 1: that's true, that's true, but keep in mind it's not

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Speaker 1: a hundred billions of assets under management. We licensed the

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Speaker 1: idea three distribution partners, so we have the help of Pimco, Schwab, Investco, Nomura,

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Speaker 1: foot See and Russell UH and couldn't get any names

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Speaker 1: that anybody recognized. We keep looking, We keep hoping somebody

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Speaker 1: known actually signs onto the idea. So that let's let's

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Speaker 1: digress and talk about that a moment. That's a fascinating

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Speaker 1: model where now I know you guys also manage money directly,

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Speaker 1: not anymore, not anymore. Oh so that's that's a change

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Speaker 1: since the last time you were here. When did you

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Speaker 1: stop directly managing money? We decided in two thousand fourteen

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Speaker 1: that UM five of our assets under quote management close quote,

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Speaker 1: where money was money that we actually managed. Fift pcent

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Speaker 1: of our revenues came on that, and the tail was

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Speaker 1: wagging the dog. We were winding up with distribution partners,

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Speaker 1: fearful that we were going to compete directly with them.

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Speaker 1: That makes sense, and we decided we don't need these complications.

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Speaker 1: Let's stick to what we're best at. And so we

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Speaker 1: approached Pimco, our largest affiliate, and in effect said would

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Speaker 1: you like this eight billion dollar book of business UM

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Speaker 1: And they said, they said, at a price tag of zero,

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Speaker 1: that's not a bad deal. I think we'll go for you.

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Speaker 1: You know, if you would have come to me, I

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Speaker 1: would have I would have given you the same deal

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Speaker 1: they gave you. Oh my goodness, that didn't cross my mind.

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Speaker 1: I wish, I wish I had thought of that. So

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Speaker 1: so this is kind of fascinating. So what I find

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Speaker 1: so intriguing about your model in addition to the intellectual property,

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Speaker 1: but essentially, you're in the business of licensing these indices

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Speaker 1: to other people to do you know, the roll up

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Speaker 1: your sleeves and do the trading, do the clients everything

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Speaker 1: do which is complex and filled with risk and expensive.

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Speaker 1: So you really have the sweetest part of of the pie.

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Speaker 1: For us, it's the sweetest part because, uh, it's what

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Speaker 1: we love to do, and it's what you you, I

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Speaker 1: ostensibly would imagine you do best. Let the other people

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Speaker 1: who are experts in trading and what have you. Um

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Speaker 1: I think not being involved in trading back office call

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Speaker 1: desks is a wonderful thing. Um I think there are

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Speaker 1: people who are really good at that, and I want

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Speaker 1: them to do what they're really good at. In effect,

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Speaker 1: being in the business of product innovation and licensing, we

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Speaker 1: face an unusually high hurdle because our clients are distribution partners.

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Speaker 1: They don't need us, They have their own R and

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Speaker 1: D teams and so for our business to succeed, UM,

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Speaker 1: they have to see us as an extension of their

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Speaker 1: own R and D team, complimentary to what they do,

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Speaker 1: and UM, not all organizations can think that way. You

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Speaker 1: have to be willing to go against the not invented

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Speaker 1: here syndrome. So that sort of puts a little more

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Speaker 1: heft on the name of the firm research affiliates, because

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Speaker 1: essentially you provide intellectual um property and research strength to

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Speaker 1: the affiliates you work with. I never really thought of

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Speaker 1: it in quite those terms, but really that's what Raffie does.

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Speaker 1: That's exactly right. Was that intentional or did we you

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Speaker 1: just happen to stumble on that? Um, a little bit

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Speaker 1: of both. I was running first Quadrant at the time

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Speaker 1: and had decided if I'm ever going to start my

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Speaker 1: own business, I had to do it sooner rather than later,

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Speaker 1: and so I was running both in parallel for two years.

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Speaker 1: Running both in parallel for two years meant I had

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Speaker 1: to be studiously avoid conflicts of interest. So initially we

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Speaker 1: started out saying, let's subadvise, let's license our ideas, and

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Speaker 1: we found that that business model works, so we stuck

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Speaker 1: with it. Quite fascinating. Let's talk a little bit about

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Speaker 1: factors investing um Fama French famously identified first the three

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Speaker 1: factor model, then the five factor model, then the seven

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Speaker 1: factor model. Are there still factors out there that have

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Speaker 1: yet to be discovered? Two answers to that question. Firstly, yes,

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Speaker 1: there will be. They've already been five factors published five

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Speaker 1: and um there will be hundreds more. Now the more

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Speaker 1: pertinent question is how much of this is data mining

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Speaker 1: finding relationships that prevailed in the past that have no

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Speaker 1: reason to prevail in the future, And how much of

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Speaker 1: it is um truly factors that drive price that can

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Speaker 1: be truly a reliable source of access return, Meaning is

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Speaker 1: there enough outperformance here that can be captured by a portfolio? Exactly? So?

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Speaker 1: Of those five So if these were really good factors,

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Speaker 1: why wouldn't we create a if not a five hundred

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Speaker 1: factor portfolio. Hey, let's put these in order of the

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Speaker 1: strongest out performance generators, and here are the fifty or

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Speaker 1: five best factors. Why can't we do that? Well? I

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Speaker 1: wrote a paper a couple of years ago called how

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Speaker 1: can smart Bay to Go Horribly Wrong? And I remember

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Speaker 1: that it was massively controversial. I thought the controversy was

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Speaker 1: amusing because if I'd written a paper entitled how can

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Speaker 1: Stockpicking go Horribly Wrong? And I offered the the sage

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Speaker 1: inside that if a stock soars and its fundamentals haven't,

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Speaker 1: so it's valuation multiples have sword, it's past returns will

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Speaker 1: look artificially wonderful, and if there's any mean or version

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Speaker 1: on valuations, it's future performance will, to use a technical term,

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Speaker 1: suck so um. People would have read that paper and said,

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Speaker 1: what's this nitwit talking about? Everybody knows that by saying

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Speaker 1: exactly the same thing about factors and smart beta strategies,

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Speaker 1: I was pilloried for suggesting that the same thing could

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Speaker 1: apply for strategies. Now, if you go back historically, you

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Speaker 1: find that the alpha of many of the smart beta

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Speaker 1: factors have has not been tested in terms of how

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Speaker 1: much of that access return came from rising valuation multiples.

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Speaker 1: We might as well have a an Apple factor that

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Speaker 1: simply says Apple has outperformed magnificently. It is a powerful factor.

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Speaker 1: You have a long Apple, short everything else factor, and

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Speaker 1: because of the past performance, we know it's going to

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Speaker 1: continue to work. Let's digress a little bit and and

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Speaker 1: define factors for people who may not be familiar with

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Speaker 1: Gene Fama, who won the Noble Prize a few years

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Speaker 1: ago for his for a lot of his work. UM

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Speaker 1: the original and its Gene Farm in Ken French at

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Speaker 1: Dartmouth Farm is a Chicago. The original Fama French factor

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Speaker 1: paper was small capitalization value, and I'm trying to remember

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Speaker 1: was that momentum, quality, market market beta market. So that's

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Speaker 1: the three, okay, and then when we moved to five,

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Speaker 1: we added um uh, quality and momentum. That's the next lot,

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Speaker 1: and then seven. I don't even know what the next

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Speaker 1: two are. I'm not certain, but I think it's illiquidity

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Speaker 1: and investment. Right. So, so all of this comes back

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Speaker 1: to let's let's keep it simple. Low cost stocks over

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Speaker 1: long periods of time, low uh better value stocks, not

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Speaker 1: expensive stocks. I don't mean low price tend to outperform

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Speaker 1: expensive stocks over the long haul, exactly right now. Advocates

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Speaker 1: of efficient markets will say it's got to be because

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Speaker 1: of some kind of hidden risk, because you can't get

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Speaker 1: something for nothing. Um. I would push back against that

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Speaker 1: and say it's not risk with value. Might be risked

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Speaker 1: with small cap because there and then there's a liquidity issues,

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Speaker 1: but with value, it's the psychology it's the behavior of

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Speaker 1: who wants to buy this. Dominoes is one of my

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Speaker 1: favorite examples. Domino's had a big, a whole run of

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Speaker 1: issues late nineties early two thousands. The stock did poorly.

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Speaker 1: People were thinking, all right, well, that chain is pretty

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Speaker 1: much done. And Dominoes over the past I think it's

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Speaker 1: either fifteen or twenty years, has handily outperformed Apple. May

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Speaker 1: be getting the timeframe wrong. So and that's the psychology

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Speaker 1: of who wants to touch that? And if you look

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Speaker 1: from the trough in two thousand nine, Uh City ever

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Speaker 1: so briefly dipped below a buck, be of a dipped

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Speaker 1: to roughly two bucks, and since then those have handily

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Speaker 1: outperformed Apple. Um so, but they've outperformed it from a

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Speaker 1: starting point of being thought on death's door. So value,

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Speaker 1: it seems to me, does have a behavioral basis. Basically,

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Speaker 1: when you have a value orientation, you're buying what's unloved,

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Speaker 1: what people want to shun. That should be rewarded. Now

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Speaker 1: is it a hidden risk factor only psychologically? Well, let

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Speaker 1: me let me push back a little bit over there.

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Speaker 1: Two companies in financial crisis look terrible. One of them's

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Speaker 1: a I G. The other's Lehman brothers. You can buy

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Speaker 1: a I g rescued and and since pretty decent, not great,

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Speaker 1: but pretty decent returns off of the bottom and Lehman,

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Speaker 1: I guess we could call that a value trap. Although

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Speaker 1: there were elements of fraud and RepA one oh five,

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Speaker 1: there was a whole different set of problems with Lehman.

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Speaker 1: But the risk with value is am I buying something

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Speaker 1: that's going to be a zero? That's known as a

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Speaker 1: value trap. It's a stock that looks cheap on its

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Speaker 1: way to zero. Now it's hard to have a whole

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Speaker 1: sector that looks cheap on its way to zero. I

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Speaker 1: coined the term anti bubble in two thousand nine to

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Speaker 1: describe what I perceived as the inverse of a bubble,

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Speaker 1: where an entire sector of the economy is priced as

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Speaker 1: if they're all headed for oblivion, when in fact, every

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Speaker 1: failure clears the runway for the survivors to have higher profits,

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Speaker 1: higher margins, and greater success us ahead. So, unless you

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Speaker 1: wanted to um except the notion of armageddon the end

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Speaker 1: of the economy, uh you, it made sense to think

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Speaker 1: that collectively this sector was being dismissed when it shouldn't be.

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Speaker 1: There's a whole group of people that are the armageddon traders.

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Speaker 1: I think the Ft called them the plastic bears. They'll

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Speaker 1: scream arm again, but then they'll he here's what we

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Speaker 1: can sell you while we're waiting for armagedon um. The

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Speaker 1: other thing about anti bubble is so fascinating. You could

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Speaker 1: say there's an anti bubble in what was it? Home

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Speaker 1: builders and oh five and mortgage brokers and bankers and

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Speaker 1: oh six and pretty much anything else in finance and

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Speaker 1: O seven and each almost every there's almost always some

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Speaker 1: sort of bubble in the market somewhere and some sort

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Speaker 1: of anti bubble in the market somewhere. You go back

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Speaker 1: to early two thousand sixteen emerging mark, It's deep value

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Speaker 1: was priced at two to three times cash flow, and

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Speaker 1: it's not as if the emerging economies of the world

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Speaker 1: were collectively all going to go bust, so it represented

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Speaker 1: an extraordinary opportunity. Rafi, the fundamental index in emerging markets

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Speaker 1: was briefly trading for a Schiller Pe ratio below six. Wow,

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Speaker 1: that's amazing. Let's talk about a study you did way

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Speaker 1: back when looking at the SNP five index, which theoretically

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Speaker 1: is a passive, and we'll put a footnote on exactly

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Speaker 1: what cassive means. In this you found from to the

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Speaker 1: last year there was a full year of data available,

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Speaker 1: stocks added to the index underperformed those that were kicked

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Speaker 1: out by an average of twenty three percentage points over

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Speaker 1: the ensuing twelve months. That's not a bad gap in return.

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Speaker 1: Oh my goodness, that's amazing. So so what are the

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Speaker 1: occations of this and what does this mean about the

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Speaker 1: stock picking acumen of the editors on the committee of

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Speaker 1: the SMP Index who select the stocks that go into it.

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Speaker 1: I don't fault the index committee for the way they

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Speaker 1: make choices. They they are under. Their clients are the

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Speaker 1: people who license the index, and the people who license

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Speaker 1: the index want the index to include all the names

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Speaker 1: that are hot, beloved, and are embarrassing not to have

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Speaker 1: in the index. Just added was Twitter as of this

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Speaker 1: tape in okay Um. And if there's a stock that

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Speaker 1: has been brutalized, unloved, dirt cheap and nobody wants it,

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Speaker 1: it's embarrassing to have it in the index. So of

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Speaker 1: course they're going to take those out. Now, what happens

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Speaker 1: is two things. Firstly, they announce a change, and they

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Speaker 1: announced the date the change will take effect. That gives

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Speaker 1: the index funds a grace period in which to trade

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Speaker 1: where they're going to move those stock prices, and those

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Speaker 1: stocks will still be in the index, so it won't

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Speaker 1: create a performance drag. There. Everybody, please front run our trades. Well,

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Speaker 1: there's a hedge fund community that does exactly that. So

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Speaker 1: the trades are made, the positions built, and then given

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Speaker 1: over to the index funds on the effective date, preferably

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Speaker 1: at or near the close. The stocks added when you

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Speaker 1: compare them with the discretionary deletions deletions that aren't related

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Speaker 1: to corporate actions, um outperformed by nearly nine percent during

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Speaker 1: that grace period. So when index funds say we don't

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Speaker 1: move stock prices with our trading, that's that's rubbish. Um.

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Speaker 1: They let other people move it on their behalf, and

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Speaker 1: they could say, oh, look we haven't moved this. Well

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Speaker 1: there's that, and then there's also the simple fact that

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Speaker 1: the ads beat the discretionary deletions by nine during a

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Speaker 1: period of days. Well, that's a big move. So you

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Speaker 1: also have part of that return difference in the subsequent

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Speaker 1: year is simply mean reversion taking the price impact of

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Speaker 1: the index funds back out, and part of it is

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Speaker 1: quite simply. The stocks added are extravagantly expensive on average,

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Speaker 1: and the stocks dropped are usually a deep discounts um

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Speaker 1: over nine of the ads are companies trading at premium multiples.

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Speaker 1: Over the discretionary deletes are trading at a discount. And

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Speaker 1: the average gap between valuation multiples of the ads and

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Speaker 1: of the deletes is more than three to one. That's amazing.

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Speaker 1: So let me make the case for quote unquote passive indexing.

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Speaker 1: I think smart data. Let me caveat this. Smart data.

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Speaker 1: Is that the wrong phrase. It should be fundamental indexing.

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Speaker 1: Passive investing is a wrong phrase. It should be low

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Speaker 1: cost indexing. But here's the pro indexing argument, and I

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Speaker 1: want you to take this argument apart. This is the

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Speaker 1: most cost effective, least expensive way to get exposure to equities.

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Speaker 1: It has the lowest amount of turnover, the least amount

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Speaker 1: of tax consequences, and everybody seems to have the greatest

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Speaker 1: difficulty outperforming the SNP five hundred. So we're gonna keep

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Speaker 1: using that as a benchmark going forward. Discuss the Bill

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Speaker 1: Sharp wrote a piece in the nine nineties entitled the

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Speaker 1: arithmetic of active investing, and it's a very simple thesis.

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Speaker 1: The market is capitalization weighted. The index fund span almost

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Speaker 1: all of the market and is capitalization weighted. You take

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Speaker 1: that portfolio, all of the indexers out, and what's left

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Speaker 1: is what active managers collectively own. Well, it's the same portfolio,

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Speaker 1: giver take some wiggle room. So if it's the same portfolio,

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Speaker 1: active managers should have the same per formances index funds

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Speaker 1: minus costs, and the costs are higher for active managers.

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Speaker 1: That arithmetic is not just true, it's a truism that

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Speaker 1: means that if you're choosing active managers with absolutely no skill,

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Speaker 1: you should expect to earn index returns minus um when

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Speaker 1: you choose an active manager. This doesn't mean in choosing

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Speaker 1: active managers as a waste of time. What it does

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Speaker 1: mean is you'd better have a good answer to the

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Speaker 1: question if this active manager is a winner, so there's

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Speaker 1: a loser on the other side of their trades, who's

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Speaker 1: the loser and why are they are willing loser for

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Speaker 1: fundamental index The answer to that is really simple. This

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Speaker 1: is a strategy that contratrades against the market's biggest and

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Speaker 1: most extravagant bets, and so the loser on the other

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Speaker 1: side of the trade is the performance chasing lemmings who

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Speaker 1: are legion. Here's the Bill Miller pushed back. Most active

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Speaker 1: managers are as you've described, with a very low active

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Speaker 1: share and essentially their closet index ers. So why on

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Speaker 1: earth should anybody pay a high fee when you could

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Speaker 1: pay a low fee and get the same portfolio. Fair

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Speaker 1: fair criticism, It's a fair criticism. There's a lot of

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Speaker 1: active managers hiding the bushes near the benchmark. Most active

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Speaker 1: managers are constantly looking over their shoulder at the benchmark

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Speaker 1: and worrying about beating it. The beauty of fundamental index

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Speaker 1: and of smart beta as it was originally defined. Smart

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Speaker 1: beta originally meant strategies that break the link with price,

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Speaker 1: that don't pay any attention to market capitalization or a

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Speaker 1: price in setting the weight of a stock. This term

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Speaker 1: has been stretched to the point of meaninglessness. But um,

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Speaker 1: under that definition, you do have the advantage that you're

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Speaker 1: going to be contratrating against the market's biggest bats um,

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Speaker 1: whether you're equal weighting or fundamental index or minimum variants,

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Speaker 1: you're going to be having an anchor a target weight

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Speaker 1: that isn't related to price, so as the price sores

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Speaker 1: and tumbles, you're gonna be selling and buying. It's built

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Speaker 1: in structural cell high Bilow discipline. Fascinating. We were talking

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Speaker 1: earlier about traditional passive investing or or low cost indexing.

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Speaker 1: Let's let's do a little bit of a compare and

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Speaker 1: contrast with UM fundamental indexing. And we touched on this.

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Speaker 1: I want to give you a quote from Burton Malchiol.

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Speaker 1: Smart beta strategies are riskier than index funds and not

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Speaker 1: right for individual investors. What is professor Malkiel getting wrong there?

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Speaker 1: Malkiel comes from the efficient markets community. He believes he

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Speaker 1: wrote random Walk down Wall Street back in I think

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Speaker 1: the sixties. UM. It's interesting to note that if you

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Speaker 1: believe that UM the the share prices equal a fair

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Speaker 1: value that we cannot see plus or minus and error

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Speaker 1: adding up to the price, And if you believe the

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Speaker 1: market is constantly hunting for those errors and trying to

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Speaker 1: fix them so that the error is mean reverting, then

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Speaker 1: contratrating against big price moves has a structural alpha that

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Speaker 1: is in fact the key driver of the value effect.

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Speaker 1: If you look at value strategies, the alpha comes from

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Speaker 1: the rebalancing, not from the cheapness of the stocks. Well,

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Speaker 1: isn't that the same thing. When we say a stock

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Speaker 1: is cheap, we're essentially saying it's trading at a discount

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Speaker 1: to its fair value, and once that rebalance o cards

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Speaker 1: it should snap back to that and there are gains. Well,

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Speaker 1: just as an example, fundamental index overweight stocks that are

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Speaker 1: trading at discounts proportional to the magnitude of the discount,

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Speaker 1: underweight stocks trading at disc premiums proportional to the magnitude

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Speaker 1: of the premium. So your systematically downweighting the growth stocks

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Speaker 1: and upweighting the value stocks. You're saying, thanks for the

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Speaker 1: gains on the growth stocks, let me trim it back

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Speaker 1: to its economic footprint, thank you for the discounts on

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Speaker 1: the value stocks, reweighting it back up. So there's a

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Speaker 1: structural value tilt. Well, why then, does this strategy relentlessly

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Speaker 1: beat cap weighted value indexes Russell value EFA, Value IFA

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Speaker 1: Emerging Markets value UM winning against the value index is

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Speaker 1: seventy or eighty percent of the time, year by year. Uh.

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Speaker 1: It does so for the simple reason that value indexes

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Speaker 1: are themselves cap weighted so they're going to overweight the

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Speaker 1: overvalue and underweight the undervalued value stocks, even within the

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Speaker 1: universe of value correct um. Think of it this way.

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Speaker 1: If a stock outperforms in the future, then it must

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Speaker 1: have been under under price today. If it underperforms in

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Speaker 1: the future, it must have been overpriced today. And what

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Speaker 1: stocks get the most weight in a cap weighted portfolio

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Speaker 1: those that are overpriced. So let's let's address that. Casette

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Speaker 1: raises a really interesting question. When we look at cap

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Speaker 1: weighted indexes, we end up owning more of the names

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Speaker 1: that are outperforming have been out, I've been out before,

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Speaker 1: and since we know momentum as a factor, the assumption

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Speaker 1: is that those outperformers will, at least for some finite

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Speaker 1: period in the future, continue to outperform. So we end

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Speaker 1: up owning more of the winners that are keeping winning

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Speaker 1: and less of the losers that are keeping losing. Or

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Speaker 1: so goes the cap weighted indet argument. What's wrong with

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Speaker 1: that claim? Don't we want more exposure to stocks that

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Speaker 1: are winning. Here's what's fascinating. Yeah, momentum is a powerful

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Speaker 1: factor across multiple asset classes spanning decades, but within stocks,

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Speaker 1: momentum was first published by professors jagged Yaian Titman back

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Speaker 1: in nineteen two or three, and since nineteen momentum is

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Speaker 1: a strategy in the stock market hasn't worked well, hasn't worked,

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Speaker 1: it's underperformed some other factors. No high performing stocks have

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Speaker 1: underperformed low performing stocks on average since ninety nine. So

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Speaker 1: is that due to the dot com crash in the

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Speaker 1: Great Financial Crisis? How is it operated? And I know

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Speaker 1: you can't say I only want to invest when we're

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Speaker 1: not in a bubble or in a crisis, but how

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Speaker 1: significant were those events to the underperformance of outperformed tremendously significant.

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Speaker 1: And you put your finger on it that when you

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Speaker 1: have a strategy that outperforms for a while, then crashes,

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Speaker 1: outperforms for a while, then crashes net net, it can

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Speaker 1: look terrible if you can use it only when it's

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Speaker 1: gonna work. Great an index that does that exactly well.

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Speaker 1: In point of fact, when um, when momentum is telling

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Speaker 1: you the extravagantly priced growth stocks are the ones with momentum,

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Speaker 1: That's when it's more likely to have a crash than

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Speaker 1: at other times. When momentum is saying the value stocks

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Speaker 1: have turned and are showing positive momentum. That's when momentum

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Speaker 1: historically works best. So the combination of the two value

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Speaker 1: and momentum tends to be a really nice combination. But

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Speaker 1: be very careful about using momentum when it's telling you

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Speaker 1: to chase bubble stocks. If you can switch it off,

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Speaker 1: do so. Let's talk about the combination of value and momentum. Uh.

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Speaker 1: West Gray of Alpha architect wrote a piece Who's Who's um?

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Speaker 1: Title I love about combining value and momentum, which essentially

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Speaker 1: says even God would get fired as an active manager.

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Speaker 1: And he said he suggests that while we know the

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Speaker 1: combination of value and momentum outperforms just about any other

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Speaker 1: combination can come up with, there are these occasional draw

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Speaker 1: downs that can be brutal. Absolutely so, So how do

428
00:29:27,680 --> 00:29:33,480
Speaker 1: we explain why value and momentum outperforms just about any

429
00:29:33,480 --> 00:29:37,360
Speaker 1: other combination of factors if we still get these really

430
00:29:37,480 --> 00:29:41,000
Speaker 1: horrific periods of time where gee, this doesn't seem to

431
00:29:41,000 --> 00:29:44,160
Speaker 1: be working. Well, simple fact is, no strategy is going

432
00:29:44,200 --> 00:29:48,360
Speaker 1: to work all the time. UH. Contraining investing is arguably

433
00:29:48,440 --> 00:29:56,680
Speaker 1: the most powerful and reliable UH long term form of investing. Basically,

434
00:29:56,760 --> 00:30:01,840
Speaker 1: whatever is newly cheap is going to have performed terribly.

435
00:30:01,920 --> 00:30:05,440
Speaker 1: It will have inflicted pain and losses. People don't want

436
00:30:05,480 --> 00:30:08,719
Speaker 1: more of that. Imagine an investor saying, oh, I just

437
00:30:08,800 --> 00:30:10,400
Speaker 1: lost a ton of money on this, give me more

438
00:30:10,400 --> 00:30:14,520
Speaker 1: of that. Uh, that's what a contraining investor does. Um

439
00:30:14,560 --> 00:30:18,960
Speaker 1: a strategy that's provided great joy and profit, a stock

440
00:30:19,080 --> 00:30:22,840
Speaker 1: that's provided great joy and profit. How many people look

441
00:30:22,880 --> 00:30:24,760
Speaker 1: at that and say, oh, get me out of here.

442
00:30:24,960 --> 00:30:30,600
Speaker 1: That's what a contraining investor does. And so with contraining investing,

443
00:30:30,840 --> 00:30:36,640
Speaker 1: you're going against the crowd that's profoundly uncomfortable, and whenever

444
00:30:36,680 --> 00:30:41,200
Speaker 1: it fails, whenever it doesn't add value because the expensive

445
00:30:41,200 --> 00:30:44,960
Speaker 1: popular stocks are getting more expensive and the cheap garbage

446
00:30:44,960 --> 00:30:49,360
Speaker 1: stocks are getting more cheap. Anytime it does fail, people

447
00:30:49,600 --> 00:30:53,160
Speaker 1: start to think you're an absolute idiot. So let's talk

448
00:30:53,200 --> 00:30:56,880
Speaker 1: about that for a second. Because my office has a

449
00:30:57,000 --> 00:31:02,960
Speaker 1: value tilt, as does everything that the research affiliate or many.

450
00:31:03,000 --> 00:31:06,719
Speaker 1: I shouldn't say everything, but many, everything just just about.

451
00:31:07,120 --> 00:31:09,760
Speaker 1: And yet we're in a period where we have the

452
00:31:09,800 --> 00:31:13,120
Speaker 1: fang stocks, we have these tech companies doing great, and

453
00:31:13,440 --> 00:31:17,640
Speaker 1: value is going through one of its periodic stretches of

454
00:31:18,040 --> 00:31:22,719
Speaker 1: under performance where people stop and say, I understand the

455
00:31:22,760 --> 00:31:26,880
Speaker 1: concept behind value, but look at Netflix, why why do

456
00:31:26,960 --> 00:31:29,760
Speaker 1: we want to own these cheap stocks? The expensive stocks

457
00:31:29,760 --> 00:31:33,480
Speaker 1: are doing great. So two part question, A, why do

458
00:31:33,560 --> 00:31:38,000
Speaker 1: we go through these periodic spasms of of significant underperformance?

459
00:31:38,040 --> 00:31:41,760
Speaker 1: And B is that what enables value to over the

460
00:31:41,760 --> 00:31:46,520
Speaker 1: long haul outperform growth. Let me answer be first, Yes,

461
00:31:46,600 --> 00:31:49,600
Speaker 1: that's exactly why it outperforms in the long run, because

462
00:31:49,640 --> 00:31:54,640
Speaker 1: it's uncomfortable, but the periods, and because when it goes

463
00:31:54,640 --> 00:31:57,800
Speaker 1: through a period of disappointment, is very easy for people

464
00:31:57,840 --> 00:32:03,320
Speaker 1: to abandon a strategy that's on comfortable the making the

465
00:32:03,400 --> 00:32:07,560
Speaker 1: cheap stocks that have become cheaper even cheaper still correct.

466
00:32:08,520 --> 00:32:13,880
Speaker 1: So it is really important. When we wrote the paper

467
00:32:13,920 --> 00:32:17,640
Speaker 1: how can smart be to go horribly wrong? We showed

468
00:32:18,160 --> 00:32:25,000
Speaker 1: that when a strategy becomes more expensive, it creates a

469
00:32:25,000 --> 00:32:31,120
Speaker 1: a surge in relative performance, making the strategy look unusually powerful,

470
00:32:31,880 --> 00:32:36,760
Speaker 1: while so sewing seeds for future disappointment because the strategy

471
00:32:36,800 --> 00:32:40,760
Speaker 1: is newly expensive. The same thing applies to value. Value

472
00:32:40,800 --> 00:32:45,520
Speaker 1: in two thousand was profoundly cheap. The gap between growth

473
00:32:45,560 --> 00:32:49,080
Speaker 1: stocks and value stocks was eight to one in valuation

474
00:32:49,240 --> 00:32:53,280
Speaker 1: terms um By two thousand and seven it was two

475
00:32:53,320 --> 00:32:56,360
Speaker 1: and a half to one too, and after one sounds

476
00:32:56,400 --> 00:32:59,840
Speaker 1: like a big gap, it's not. It's considerably narrower than

477
00:32:59,880 --> 00:33:05,480
Speaker 1: you usual. And so in two thousand seven, objectively value

478
00:33:05,600 --> 00:33:09,320
Speaker 1: was priced high relative to its own history, and that's

479
00:33:09,360 --> 00:33:11,440
Speaker 1: what set the stage for the quant crash in August

480
00:33:11,520 --> 00:33:14,040
Speaker 1: of two thousand seven. That was a very crowded trade

481
00:33:14,080 --> 00:33:17,120
Speaker 1: a lot of people had. But that you know, you,

482
00:33:17,120 --> 00:33:20,320
Speaker 1: you you reference quants. That makes me think of a

483
00:33:20,440 --> 00:33:23,640
Speaker 1: quote of yours which I don't believe is still true.

484
00:33:24,240 --> 00:33:27,120
Speaker 1: Or maybe you could disabuse me of that notion. You

485
00:33:27,240 --> 00:33:33,120
Speaker 1: once wrote, our industry hates arithmetic. Now that was certainly

486
00:33:33,200 --> 00:33:37,280
Speaker 1: true decades ago, given the amount of things, amount of

487
00:33:37,320 --> 00:33:42,480
Speaker 1: assets managed by quantitative strategies, and just the flood of

488
00:33:42,720 --> 00:33:47,560
Speaker 1: smart quant analysts coming into the industry, does finance still

489
00:33:47,640 --> 00:33:51,760
Speaker 1: hate arithmetic? Oh my goodness, yes, it doesn't hate mathematics.

490
00:33:51,840 --> 00:33:57,800
Speaker 1: The notion of quantitative methods probably almost more popular than

491
00:33:57,840 --> 00:34:00,560
Speaker 1: ever before. Only two thousand seven might even come close.

492
00:34:01,440 --> 00:34:06,040
Speaker 1: But the notion of simple arithmetic, the arithmetic of returns

493
00:34:06,400 --> 00:34:10,520
Speaker 1: is you earn a yield, you have a growth in income,

494
00:34:11,040 --> 00:34:13,440
Speaker 1: and you have changes in valuation multiples. If you know

495
00:34:13,480 --> 00:34:16,880
Speaker 1: those three numbers, you know your rate of return. Disaggregating

496
00:34:16,920 --> 00:34:20,600
Speaker 1: historical returns into those three components gives you a very

497
00:34:20,680 --> 00:34:26,320
Speaker 1: clear picture of where returns came from, and looking ahead,

498
00:34:27,200 --> 00:34:32,040
Speaker 1: you know what the yield is. You know that historical

499
00:34:32,200 --> 00:34:35,600
Speaker 1: growth is probably not a bad predictor for future growth,

500
00:34:35,680 --> 00:34:40,560
Speaker 1: which leaves you valuation change. If valuations are high, you're

501
00:34:40,560 --> 00:34:43,640
Speaker 1: more likely to have mean reversion down. If valuations are low,

502
00:34:43,719 --> 00:34:46,600
Speaker 1: you're more likely to have mean reversion up. So when

503
00:34:46,640 --> 00:34:51,640
Speaker 1: markets are expensive, you have a lousy yield. Growth is

504
00:34:51,760 --> 00:34:55,440
Speaker 1: what it is, and valuations are more likely to come

505
00:34:55,480 --> 00:34:58,800
Speaker 1: down than rise, and when markets are cheap, the opposite

506
00:34:58,880 --> 00:35:03,799
Speaker 1: holds true. So that's the arithmetic people hate. When we're

507
00:35:03,800 --> 00:35:05,560
Speaker 1: in the tenth year of a bull market, tenth year

508
00:35:05,560 --> 00:35:10,920
Speaker 1: of an economic expansion, when times are good and people

509
00:35:10,960 --> 00:35:16,480
Speaker 1: have had wonderful success, their expectations for future returns go up,

510
00:35:16,960 --> 00:35:24,400
Speaker 1: not down. Now, that's interesting because when markets are rising

511
00:35:24,680 --> 00:35:28,280
Speaker 1: and yields are falling, your forward looking return is eroding.

512
00:35:28,640 --> 00:35:31,120
Speaker 1: You expect the return should be lower when things are

513
00:35:31,120 --> 00:35:34,479
Speaker 1: pricing and higher when they're cheating, right, and people think

514
00:35:34,520 --> 00:35:41,160
Speaker 1: the opposite way. Um, it's interesting. At the market lows

515
00:35:41,440 --> 00:35:46,800
Speaker 1: in eight seven in two thousand two, uh, and again

516
00:35:46,800 --> 00:35:50,520
Speaker 1: in two thousand nine. Each of those three episodes, I

517
00:35:50,600 --> 00:35:54,240
Speaker 1: found myself on a plane next to somebody who was saying,

518
00:35:54,800 --> 00:35:57,760
Speaker 1: I'm never gonna be investing in the stock market again.

519
00:35:58,560 --> 00:36:01,560
Speaker 1: Are all three time? So that's fascinating. Can you stick around?

520
00:36:01,600 --> 00:36:03,880
Speaker 1: I have a ton more questions for you. We have

521
00:36:04,000 --> 00:36:06,480
Speaker 1: been speaking with Rob are Not. He is the founder

522
00:36:06,520 --> 00:36:10,400
Speaker 1: and chairman of Research Affiliates. If you enjoy this conversation,

523
00:36:10,480 --> 00:36:13,120
Speaker 1: be sure and check out our podcast extras when we

524
00:36:13,239 --> 00:36:17,880
Speaker 1: keep the tape rolling and continue discussing all things smart Beta.

525
00:36:18,360 --> 00:36:22,640
Speaker 1: You can find that wherever final podcasts are sold. We

526
00:36:22,719 --> 00:36:26,800
Speaker 1: love your comments, feedback and suggestions right to us at

527
00:36:27,080 --> 00:36:30,520
Speaker 1: m IB podcast at Bloomberg dot net. Check out my

528
00:36:30,600 --> 00:36:33,800
Speaker 1: daily column on Bloomberg dot com. Follow me on Twitter

529
00:36:33,920 --> 00:36:37,680
Speaker 1: at Ridholts. I'm Barry Ridhults. You're listening to Masters in

530
00:36:37,719 --> 00:36:54,759
Speaker 1: Business on Bloomberg Radio. Welcome to the podcast, Rob, Thank

531
00:36:54,760 --> 00:36:57,600
Speaker 1: you so much for doing this. You we were discussing earlier.

532
00:36:58,000 --> 00:37:01,280
Speaker 1: You were one of the original old people. We kinda

533
00:37:01,920 --> 00:37:06,239
Speaker 1: first tested the water with the podcast with series back

534
00:37:06,239 --> 00:37:10,239
Speaker 1: in I want to say, right, that's that's when we

535
00:37:10,320 --> 00:37:14,160
Speaker 1: launched it, and you're you're one of the people where

536
00:37:15,280 --> 00:37:18,239
Speaker 1: a couple of years later, I kind of said I

537
00:37:18,280 --> 00:37:20,359
Speaker 1: wanted to ask him about this. I didn't even bring

538
00:37:20,440 --> 00:37:23,120
Speaker 1: up that I forgot this. I forgot that. I gotta

539
00:37:23,200 --> 00:37:25,320
Speaker 1: bring him back and go over some of these things.

540
00:37:25,320 --> 00:37:28,960
Speaker 1: So thank you so much for for returning. I'm fond

541
00:37:28,960 --> 00:37:30,960
Speaker 1: of saying this is the most fun I have all week.

542
00:37:31,040 --> 00:37:35,960
Speaker 1: So uh, this is uh yeah, right, I have to

543
00:37:35,960 --> 00:37:39,120
Speaker 1: sit down with people like you and Danny Kanaman and

544
00:37:39,239 --> 00:37:42,560
Speaker 1: Ray Dally and Howard Marks. That is my cross to

545
00:37:42,640 --> 00:37:47,279
Speaker 1: bit and hopefully yes, so it's an ugly dirty job,

546
00:37:47,280 --> 00:37:49,799
Speaker 1: but somebody's got to do it. Let's um, there's a

547
00:37:49,800 --> 00:37:53,040
Speaker 1: ton of stuff I didn't get to And since we

548
00:37:53,040 --> 00:37:57,279
Speaker 1: were talking about expensive stocks and tech stocks, let's talk

549
00:37:57,320 --> 00:38:00,920
Speaker 1: a little bit about fang Uh we see the fang

550
00:38:00,960 --> 00:38:04,040
Speaker 1: stocks that have you know, just taken off over the

551
00:38:04,040 --> 00:38:10,239
Speaker 1: past five or so years. But there's also let's do

552
00:38:10,280 --> 00:38:13,359
Speaker 1: a compare and contrast with the es. These are real

553
00:38:13,440 --> 00:38:22,000
Speaker 1: businesses with actual revenues some of them and profits well, Apple, Amazon, uh, Netflix, Google,

554
00:38:22,239 --> 00:38:26,960
Speaker 1: go down the top ten tech stocks. It's not like

555
00:38:27,080 --> 00:38:29,320
Speaker 1: the ephemeral or is it. Let me ask you the

556
00:38:29,400 --> 00:38:32,760
Speaker 1: question as opposed to answering what are the parallels between

557
00:38:32,840 --> 00:38:37,800
Speaker 1: today and the sort of mania we saw in the nines?

558
00:38:37,880 --> 00:38:41,960
Speaker 1: Are there parallels? There? Sure are? UM. I wrote a

559
00:38:42,000 --> 00:38:46,760
Speaker 1: paper recently entitled Yes, It's a bubble? So what? Okay,

560
00:38:46,880 --> 00:38:48,920
Speaker 1: that's one of my questions. Let's let's go over that

561
00:38:48,960 --> 00:38:51,920
Speaker 1: because I love the title. Yeah, the point of the

562
00:38:51,960 --> 00:38:58,319
Speaker 1: paper is twofold. Um. One point that we make is

563
00:38:58,440 --> 00:39:03,560
Speaker 1: that the term is bandied about without a definition, a bubble, bubble,

564
00:39:04,360 --> 00:39:09,680
Speaker 1: and so we make an effort to give a um

565
00:39:09,719 --> 00:39:13,560
Speaker 1: an objective definition that people can actually use as a

566
00:39:13,600 --> 00:39:16,840
Speaker 1: test for saying, is this a bubble? And that objective

567
00:39:16,880 --> 00:39:26,080
Speaker 1: definition is one where simple measures of fundamental business success

568
00:39:26,840 --> 00:39:33,520
Speaker 1: you have to make um exceptionally aggressive assumptions to justify

569
00:39:33,640 --> 00:39:38,040
Speaker 1: the price of a stock, or a sector or a country.

570
00:39:38,080 --> 00:39:40,400
Speaker 1: So you don't like the Potter Stewart definition, I know

571
00:39:40,480 --> 00:39:45,520
Speaker 1: it when I see it. It's a little vague, a

572
00:39:45,560 --> 00:39:49,319
Speaker 1: little vague. And the second part of the definition is

573
00:39:49,440 --> 00:39:54,840
Speaker 1: that the marginal buyer is somebody who is not buying

574
00:39:55,040 --> 00:40:00,400
Speaker 1: because of a careful analysis of fair value, but is

575
00:40:00,440 --> 00:40:03,000
Speaker 1: buying because they expect to be able to resell to

576
00:40:03,080 --> 00:40:06,680
Speaker 1: somebody else at a higher price. Greater fool theory correct.

577
00:40:07,280 --> 00:40:10,240
Speaker 1: So you look at the fangs today, the marginal buyer

578
00:40:10,280 --> 00:40:15,520
Speaker 1: in most cases is not somebody who's done careful analysis

579
00:40:15,600 --> 00:40:19,080
Speaker 1: of forward looking expected rates of return. The other parallel

580
00:40:19,120 --> 00:40:25,600
Speaker 1: with the tech bubble is that at the peak of

581
00:40:25,600 --> 00:40:28,919
Speaker 1: the tech bubble, five of the eight largest market cap

582
00:40:28,960 --> 00:40:34,120
Speaker 1: companies on the planet. We're tech companies today seven of

583
00:40:34,160 --> 00:40:39,319
Speaker 1: the eight largest our tech companies. The valuations aren't as

584
00:40:39,360 --> 00:40:42,800
Speaker 1: extravagant as they were in two thousand, but the concentration

585
00:40:44,080 --> 00:40:46,760
Speaker 1: at the top of the list in global market cap

586
00:40:47,080 --> 00:40:50,400
Speaker 1: is even more. So let me push back on that

587
00:40:50,440 --> 00:40:53,840
Speaker 1: because we've we've been debating this um with some folks

588
00:40:53,840 --> 00:41:00,839
Speaker 1: in the office. So technology today represents the elimination of

589
00:41:00,960 --> 00:41:07,319
Speaker 1: human ingenuity and effort. And why wouldn't technology companies that

590
00:41:07,480 --> 00:41:11,840
Speaker 1: don't require a giant infrastructure. Think back to the railroads.

591
00:41:11,880 --> 00:41:16,640
Speaker 1: It costs inflation adjusted billions to lay thousands of miles

592
00:41:16,640 --> 00:41:19,680
Speaker 1: of tracks and all the manpower you had to hire

593
00:41:19,719 --> 00:41:25,040
Speaker 1: in locomotives and rail cars and all that stuff. Right,

594
00:41:25,280 --> 00:41:31,160
Speaker 1: Completely tapping intensive, completely labor intensive. Now a startup is

595
00:41:31,200 --> 00:41:34,360
Speaker 1: a couple of guys on a laptop and an internet connection,

596
00:41:34,760 --> 00:41:37,520
Speaker 1: so you don't have the same capital requirements you don't

597
00:41:37,520 --> 00:41:41,720
Speaker 1: have the same labor intensity and the ability to scale

598
00:41:42,239 --> 00:41:46,319
Speaker 1: is immense. So shouldn't the A shouldn't these companies be

599
00:41:46,400 --> 00:41:50,680
Speaker 1: trading at a premium? And B shouldn't they be dominating

600
00:41:51,200 --> 00:41:54,279
Speaker 1: the market? Courts? Hey, this is the future and technology

601
00:41:54,360 --> 00:41:58,680
Speaker 1: is driving us all with robots and driverless cars and

602
00:41:58,880 --> 00:42:01,800
Speaker 1: automation and your name it. But you tell me which

603
00:42:01,840 --> 00:42:05,319
Speaker 1: companies are going to be the center of innovation ten

604
00:42:05,400 --> 00:42:07,960
Speaker 1: years from now, and I will happily acknowledge that those

605
00:42:08,000 --> 00:42:13,040
Speaker 1: companies deserve massive valuations today, huge multiples. The problem with

606
00:42:13,120 --> 00:42:17,480
Speaker 1: that thesis is really simple, and that's casting a BroadNet.

607
00:42:18,520 --> 00:42:21,239
Speaker 1: You're going to have a lot of companies with a

608
00:42:21,360 --> 00:42:28,120
Speaker 1: fantastic story. Back in two thousands, this story was these

609
00:42:28,200 --> 00:42:31,480
Speaker 1: companies are changing. It's a new paradigm. These companies are

610
00:42:31,560 --> 00:42:34,480
Speaker 1: changing the way we do business, the way we communicate.

611
00:42:35,280 --> 00:42:42,200
Speaker 1: They're radically reshaping the macro economy, and this industry is

612
00:42:42,280 --> 00:42:45,640
Speaker 1: going to be changing our world. So of course they

613
00:42:45,680 --> 00:42:52,200
Speaker 1: deserve massive premium multiples. What was overlooked was that these

614
00:42:52,239 --> 00:42:58,600
Speaker 1: companies were disruptors, and the disruptors themselves get disrupted, often

615
00:42:58,800 --> 00:43:03,879
Speaker 1: very fast. So how many search engines preceded Google how

616
00:43:03,920 --> 00:43:10,600
Speaker 1: many how many handheld devices preceded the iPhone, and we're

617
00:43:10,600 --> 00:43:16,160
Speaker 1: seen as utterly dominant. BlackBerry own that market for the palm.

618
00:43:16,320 --> 00:43:20,360
Speaker 1: Palm briefly was trading for market cap greater than General

619
00:43:20,400 --> 00:43:22,960
Speaker 1: Motors and and both of them eventually went to zero.

620
00:43:24,400 --> 00:43:29,239
Speaker 1: So you have an industry with massive change. Here's a

621
00:43:29,280 --> 00:43:35,720
Speaker 1: fascinating thing. The ten largest market cap companies in technology

622
00:43:35,760 --> 00:43:40,359
Speaker 1: in the US in the year two thousand, um how

623
00:43:40,400 --> 00:43:42,880
Speaker 1: many of those outperformed in the eighteen years before a

624
00:43:42,920 --> 00:43:44,640
Speaker 1: start of two thousand and the start of this year.

625
00:43:45,160 --> 00:43:48,920
Speaker 1: Zero a single one, not one beat the market. The

626
00:43:49,000 --> 00:43:52,400
Speaker 1: fascinating part about tell me the companies ten years hence,

627
00:43:53,719 --> 00:43:57,640
Speaker 1: look back ten years ago. There was no go down

628
00:43:57,680 --> 00:44:01,000
Speaker 1: the list. Facebook was in public, Twitter was in public.

629
00:44:01,080 --> 00:44:04,319
Speaker 1: Netflix wasn't public. Actually Netflix was public, but they were

630
00:44:04,360 --> 00:44:07,760
Speaker 1: sending DVDs through the MAIP. Go down the whole list

631
00:44:07,960 --> 00:44:12,280
Speaker 1: and go to ten years previous to that. Google wasn't public.

632
00:44:12,600 --> 00:44:16,000
Speaker 1: You could, you could. Apple was thought to be going bankrupt.

633
00:44:16,080 --> 00:44:19,080
Speaker 1: Amazon was a glimmer and Jeff Bezos is I So

634
00:44:19,239 --> 00:44:22,560
Speaker 1: it's ten years doesn't seem like a long time, but

635
00:44:22,760 --> 00:44:26,799
Speaker 1: making a forecast ten years, hence, it's all but impossible.

636
00:44:26,840 --> 00:44:30,320
Speaker 1: You could get lucky. But here's another fascinating thing about

637
00:44:30,680 --> 00:44:34,319
Speaker 1: um bubbles and about markets in general. If you take

638
00:44:34,360 --> 00:44:38,759
Speaker 1: the ten largest market cap companies on the planet, on average,

639
00:44:39,000 --> 00:44:40,960
Speaker 1: only two of them are still on that list ten

640
00:44:41,040 --> 00:44:45,920
Speaker 1: years later. That's amazing. It's eight or under performers. Now,

641
00:44:47,640 --> 00:44:51,759
Speaker 1: the eight that fall off the list are obviously and

642
00:44:51,840 --> 00:44:56,640
Speaker 1: assuredly performing worse than the eight that replaced them, and

643
00:44:56,680 --> 00:44:59,400
Speaker 1: the eight that are on definition by definition, and the

644
00:44:59,440 --> 00:45:02,360
Speaker 1: eight that are on the list today have a bigger

645
00:45:02,400 --> 00:45:05,400
Speaker 1: weight than the ones that replace them. So you have

646
00:45:05,520 --> 00:45:10,440
Speaker 1: this drag associated with capitalization waiting. So even the top

647
00:45:10,560 --> 00:45:14,960
Speaker 1: ten reinforces the notion of what's wrong with indexing. That

648
00:45:15,040 --> 00:45:17,920
Speaker 1: doesn't make it easier for active managers to add value

649
00:45:18,560 --> 00:45:22,000
Speaker 1: if they're looking over the shoulder and wondering, how am

650
00:45:22,000 --> 00:45:25,279
Speaker 1: I hurting myself by not owning Netflix? Or shouldn't I

651
00:45:25,320 --> 00:45:28,440
Speaker 1: really take advantage of the dip and buy more Tesla? Now,

652
00:45:28,480 --> 00:45:31,520
Speaker 1: I just we're recording this on a day when the

653
00:45:31,520 --> 00:45:34,880
Speaker 1: Wall Street Journal had a column out, I'm sorry it

654
00:45:34,960 --> 00:45:38,800
Speaker 1: was Bloomberg had a column out that said Apple a

655
00:45:38,920 --> 00:45:42,320
Speaker 1: stone's throw away from a trillion dollars and the biggest

656
00:45:42,360 --> 00:45:45,960
Speaker 1: market cap company is actually trading in a much lower

657
00:45:46,040 --> 00:45:50,319
Speaker 1: multiple than the previous times, when, at least on an

658
00:45:50,360 --> 00:45:54,920
Speaker 1: inflation adjusted basis, we had companies approaching the same level.

659
00:45:55,440 --> 00:45:59,319
Speaker 1: Apple at eighteen times is much cheaper than go down

660
00:45:59,400 --> 00:46:02,239
Speaker 1: the list when it was Qualcom, when it was Microsoft,

661
00:46:02,280 --> 00:46:07,720
Speaker 1: when it was whoever. Uh, so are the faning the problem?

662
00:46:07,719 --> 00:46:11,160
Speaker 1: I guess we run into Apple. A new favorite of

663
00:46:11,160 --> 00:46:16,200
Speaker 1: Warren Buffett's is cheap Amazon, now that they're starting to

664
00:46:16,200 --> 00:46:20,840
Speaker 1: show a profit by any rational measure other than pure

665
00:46:21,000 --> 00:46:26,719
Speaker 1: growth and market share, looks very expensive, so as does

666
00:46:26,800 --> 00:46:29,560
Speaker 1: we Work, and a bunch of others. Well, the private

667
00:46:29,560 --> 00:46:33,239
Speaker 1: company's uber we Works. Yeah, tharaos that one didn't work

668
00:46:33,280 --> 00:46:36,120
Speaker 1: out too well. But go down the list of the

669
00:46:36,200 --> 00:46:40,279
Speaker 1: so called unicorns, what does that tell us about how

670
00:46:40,360 --> 00:46:45,280
Speaker 1: much capital is chasing? Exactly right? And when you're talking

671
00:46:45,320 --> 00:46:48,239
Speaker 1: about the unicorns, the very simple fact is some of

672
00:46:48,280 --> 00:46:51,200
Speaker 1: them are worth every penny of what their valuation is today.

673
00:46:51,719 --> 00:46:55,160
Speaker 1: I just don't know which one. And uh, if you

674
00:46:55,200 --> 00:47:00,160
Speaker 1: can pick those um Google since it's i p OH

675
00:47:00,360 --> 00:47:06,440
Speaker 1: has been a persistent favorite for value managers to underweight

676
00:47:06,560 --> 00:47:10,840
Speaker 1: it and for value manager for deep value managers to

677
00:47:10,880 --> 00:47:14,799
Speaker 1: even short it. Okay, well that's not working out so well.

678
00:47:15,760 --> 00:47:22,160
Speaker 1: But for every Google, there's a palm Um, there's Uh,

679
00:47:22,200 --> 00:47:26,200
Speaker 1: there's an array of companies where you look back and say,

680
00:47:26,239 --> 00:47:28,799
Speaker 1: as you did with Saraos that didn't work out so well.

681
00:47:29,520 --> 00:47:34,160
Speaker 1: Thin Nos was hot, it was going to change the world, right,

682
00:47:34,239 --> 00:47:37,960
Speaker 1: But that was an outright fraud. That's very different. And

683
00:47:37,960 --> 00:47:42,440
Speaker 1: and Elizabeth Holmes, the founder and CEO, just settled fraud

684
00:47:42,640 --> 00:47:45,840
Speaker 1: accusations from the s with the sec The company is

685
00:47:45,920 --> 00:47:50,240
Speaker 1: laid off almost all of its staff. Heartily recommend Carrie

686
00:47:50,280 --> 00:47:53,799
Speaker 1: Row's book Bad Blood. It's a fascinating read. Uh. And

687
00:47:53,840 --> 00:47:56,359
Speaker 1: we'll get to books in a minute. But where there

688
00:47:56,400 --> 00:47:59,759
Speaker 1: aren't cases of outright where it isn't a scam, where

689
00:47:59,760 --> 00:48:03,560
Speaker 1: it's which a legitimate company who, for whatever reason, its

690
00:48:03,640 --> 00:48:07,800
Speaker 1: moment may have passed. How can you determine with any

691
00:48:07,800 --> 00:48:10,600
Speaker 1: degree of confidence this is a company that's going to

692
00:48:10,680 --> 00:48:13,600
Speaker 1: be around for ten or twenty years. You can't. You

693
00:48:13,680 --> 00:48:19,680
Speaker 1: can't um x andity. You can't say which of the popular,

694
00:48:19,760 --> 00:48:23,560
Speaker 1: beloved star companies are going to be around in ten years,

695
00:48:23,920 --> 00:48:26,600
Speaker 1: let alone which ones are going to outperform. If history

696
00:48:26,680 --> 00:48:31,000
Speaker 1: is an example, Uh, there were tech companies that were

697
00:48:31,800 --> 00:48:36,320
Speaker 1: uh uh described as being sensibly priced in two thousand

698
00:48:36,400 --> 00:48:40,440
Speaker 1: because they were only twenty times the two year forward earnings. Well,

699
00:48:40,440 --> 00:48:43,800
Speaker 1: look at Microsoft, which has since under the new CEO,

700
00:48:44,040 --> 00:48:48,840
Speaker 1: Saudia Nadela, has since returned to very quietly, has become

701
00:48:49,320 --> 00:48:53,160
Speaker 1: the third largest or third most profitable company in the world,

702
00:48:53,160 --> 00:48:56,080
Speaker 1: third largest market cap. I don't know if that's in

703
00:48:56,160 --> 00:48:59,840
Speaker 1: tech stocks or stocks in general, and that's something that

704
00:49:00,239 --> 00:49:03,680
Speaker 1: kind of was left for dead after the dot coms,

705
00:49:04,360 --> 00:49:07,440
Speaker 1: so no one would have very few people were predicting

706
00:49:07,480 --> 00:49:11,480
Speaker 1: Microsoft was going to make a comeback. I'm surprised Buffett,

707
00:49:11,560 --> 00:49:15,160
Speaker 1: with his friendship with Gates, didn't recognize the value and

708
00:49:15,200 --> 00:49:19,319
Speaker 1: Microsoft UM. But the question that all this comes to

709
00:49:19,600 --> 00:49:22,920
Speaker 1: is if history tells us that these top ten stocks,

710
00:49:22,960 --> 00:49:24,440
Speaker 1: most of them are going to drop out of the

711
00:49:24,440 --> 00:49:28,600
Speaker 1: top ten, is its simple valuation mean reversion or is

712
00:49:28,680 --> 00:49:32,120
Speaker 1: something else at work here? Part of it is valuation

713
00:49:32,160 --> 00:49:36,200
Speaker 1: mean reversion. Part of it. We wrote a piece UM

714
00:49:36,239 --> 00:49:39,520
Speaker 1: called too Big to Succeed, kind of a play on

715
00:49:39,600 --> 00:49:43,120
Speaker 1: the notion of too big to fail, and the point

716
00:49:43,120 --> 00:49:46,400
Speaker 1: of that paper was the largest market cap companies get

717
00:49:46,440 --> 00:49:51,080
Speaker 1: there because they're big, successful businesses and trading at high multiples.

718
00:49:51,719 --> 00:49:53,680
Speaker 1: You don't get to the top of a sector or

719
00:49:53,800 --> 00:49:56,920
Speaker 1: top of the market without both conditions generally being true.

720
00:49:58,280 --> 00:50:03,359
Speaker 1: For that reason, UH, these companies are likely to disappoint

721
00:50:03,440 --> 00:50:07,040
Speaker 1: just on evaluation basis. Now, add on top of that

722
00:50:07,160 --> 00:50:11,600
Speaker 1: the fact that now these companies are so visible that

723
00:50:11,760 --> 00:50:16,359
Speaker 1: everyone's taking shots at them. Their competitors are after them,

724
00:50:16,680 --> 00:50:20,920
Speaker 1: regulators want to put a new notch on their gun barrel. Uh.

725
00:50:21,160 --> 00:50:24,520
Speaker 1: The list goes on and on, and so Apple goes

726
00:50:24,600 --> 00:50:31,240
Speaker 1: from being a beloved trendy darling to being under attack

727
00:50:31,719 --> 00:50:36,200
Speaker 1: from regulators all over the world, UH, questioned and challenged

728
00:50:36,280 --> 00:50:39,320
Speaker 1: because of the buggy nous of some of their um

729
00:50:39,520 --> 00:50:42,440
Speaker 1: uh software these days. Look at General Electric is a

730
00:50:42,440 --> 00:50:46,239
Speaker 1: perfect example. They're a greater love darling than g in

731
00:50:46,280 --> 00:50:49,720
Speaker 1: the in the nineties, not at all, And I, best

732
00:50:49,719 --> 00:50:53,080
Speaker 1: of my recollection, it has not outperformed since then by

733
00:50:53,160 --> 00:50:57,360
Speaker 1: much it's gotten. So I have to ask you. You

734
00:50:57,520 --> 00:51:02,560
Speaker 1: keep referencing various white papers, um how smart batic could

735
00:51:02,600 --> 00:51:05,239
Speaker 1: go disastfully wrong, too big to succeed. There was one

736
00:51:05,239 --> 00:51:09,680
Speaker 1: other you mentioned When you and the research staff at

737
00:51:09,760 --> 00:51:13,720
Speaker 1: Research Affiliates are thinking about writing a white paper putting

738
00:51:13,719 --> 00:51:17,160
Speaker 1: it together, who is your target audience for that? Are

739
00:51:17,200 --> 00:51:20,479
Speaker 1: you writing that? You know? Daniel Borstein very famously said,

740
00:51:20,520 --> 00:51:22,960
Speaker 1: I write to figure out what I think. I'm going

741
00:51:23,000 --> 00:51:26,640
Speaker 1: to suggest that's not the motivation with your white papers.

742
00:51:27,000 --> 00:51:29,960
Speaker 1: You guys already know what you think. Who is the

743
00:51:30,040 --> 00:51:35,080
Speaker 1: target audience for those papers? You know it's a really

744
00:51:35,080 --> 00:51:39,120
Speaker 1: good question, because there's not a really good answer. Yeah,

745
00:51:39,239 --> 00:51:41,799
Speaker 1: we'd love for our papers to be accessible to the

746
00:51:41,880 --> 00:51:45,200
Speaker 1: ordinary Joe on the street, mom and pop. Investors aren't

747
00:51:45,239 --> 00:51:48,480
Speaker 1: really reading white papers. That they aren't reading them, and

748
00:51:49,400 --> 00:51:52,640
Speaker 1: the papers are in many cases too subtle, to mathematical,

749
00:51:52,719 --> 00:51:55,080
Speaker 1: too complicated. When I get to page seven and it's

750
00:51:55,120 --> 00:51:57,759
Speaker 1: all formulas, that's where it's happened. Well, we don't do that.

751
00:51:58,239 --> 00:52:01,439
Speaker 1: We don't do formulas the way I finance general would,

752
00:52:01,800 --> 00:52:06,239
Speaker 1: but we do. I think our general target would be

753
00:52:06,280 --> 00:52:11,399
Speaker 1: somebody with the sophistication of the average financial advisor. If

754
00:52:11,440 --> 00:52:14,240
Speaker 1: we're writing over the heads of the average financial advisor,

755
00:52:14,280 --> 00:52:18,400
Speaker 1: we're not doing anybody any good. If we're writing below

756
00:52:18,520 --> 00:52:23,600
Speaker 1: that level, we run a risk of oversimplifying. And uh,

757
00:52:24,640 --> 00:52:30,600
Speaker 1: we do love challenging conventional wisdom. I've made a career

758
00:52:30,960 --> 00:52:34,759
Speaker 1: out of looking at things that are generally perceived to

759
00:52:34,800 --> 00:52:38,360
Speaker 1: be true and testing them, and sometimes they are true

760
00:52:38,760 --> 00:52:41,680
Speaker 1: and sometimes they're not. And when they're not, you get

761
00:52:41,719 --> 00:52:46,759
Speaker 1: a paper out of it that rattle some cages and

762
00:52:47,040 --> 00:52:52,280
Speaker 1: that maybe even can change the business in a modest way.

763
00:52:52,680 --> 00:52:56,440
Speaker 1: So um, one of the things that I've found is

764
00:52:56,520 --> 00:52:59,600
Speaker 1: that when you write a paper that challenges conventional wisdom,

765
00:53:00,080 --> 00:53:02,840
Speaker 1: there's somebody out there. In fact, usually there's lots of

766
00:53:02,840 --> 00:53:05,000
Speaker 1: people out there who have made a career on that

767
00:53:05,080 --> 00:53:08,440
Speaker 1: conventional wisdom. And boy are they angry to say to

768
00:53:08,520 --> 00:53:12,560
Speaker 1: say the least. So one of the questions that keeps

769
00:53:12,600 --> 00:53:15,960
Speaker 1: coming up on the just low cost indexing side is,

770
00:53:16,760 --> 00:53:21,000
Speaker 1: given the growth of Vanguard and black Rock four trillion

771
00:53:21,040 --> 00:53:23,359
Speaker 1: and five trillion respectively, I think they're gonna be five

772
00:53:23,360 --> 00:53:26,480
Speaker 1: and six in a short period of time. The question

773
00:53:26,520 --> 00:53:30,239
Speaker 1: that always comes up with low cost passive indexes is hey,

774
00:53:30,360 --> 00:53:33,880
Speaker 1: how big can these get before it no longer works?

775
00:53:34,440 --> 00:53:39,879
Speaker 1: I look at smart Beta, from it tripled to over

776
00:53:39,960 --> 00:53:44,280
Speaker 1: six billion, it's a stone throw Uh now from a trillion,

777
00:53:44,880 --> 00:53:48,240
Speaker 1: it's almost as big as Apple, almost as biggest Apple,

778
00:53:48,760 --> 00:53:51,839
Speaker 1: almost as big as one stock. At what point? So

779
00:53:51,880 --> 00:53:54,920
Speaker 1: there's well, the biggest stock, But at what point does

780
00:53:54,960 --> 00:53:58,520
Speaker 1: this top out When does it become so large that

781
00:53:58,680 --> 00:54:03,360
Speaker 1: everybody's doing it and therefore there's no outperformance in it. Firstly,

782
00:54:03,920 --> 00:54:07,320
Speaker 1: smart beta has been to become a catch all phrase

783
00:54:07,840 --> 00:54:12,240
Speaker 1: that spans a lot of strategies, many of which aren't

784
00:54:12,360 --> 00:54:19,200
Speaker 1: smart at all. So the notion that it's just a label, right,

785
00:54:19,239 --> 00:54:23,360
Speaker 1: So let's stick forget smart beta. I don't like passive indexing.

786
00:54:23,360 --> 00:54:27,359
Speaker 1: I don't like smart beta. Let's stick with fundamental indexing,

787
00:54:27,520 --> 00:54:32,040
Speaker 1: and we'll stick with price to sales, price to earnings,

788
00:54:32,080 --> 00:54:34,640
Speaker 1: price to book value, price to dividend, which I think

789
00:54:34,719 --> 00:54:39,600
Speaker 1: is a fundamentals where where research affiliates began. And that's

790
00:54:39,719 --> 00:54:45,440
Speaker 1: did we say that's or more of the indices you create? Um,

791
00:54:45,440 --> 00:54:50,080
Speaker 1: it's it's about UM eight of our A U M.

792
00:54:50,880 --> 00:54:56,000
Speaker 1: The rest is global asset allocation strategies. Now, in terms

793
00:54:56,000 --> 00:55:00,760
Speaker 1: of capacity, different so called smart beta strategies have different capacity.

794
00:55:00,800 --> 00:55:04,560
Speaker 1: Some of them are very high turnover UM, heavily involved

795
00:55:04,560 --> 00:55:09,400
Speaker 1: in illiquid companies, for instance, um UH. Roger Robbertson's favorite

796
00:55:09,400 --> 00:55:13,400
Speaker 1: strategy is an aliquidity strategy. You're gonna focus on companies

797
00:55:13,400 --> 00:55:16,040
Speaker 1: with low liquidity. If you're trying to run a hundred

798
00:55:16,080 --> 00:55:19,279
Speaker 1: billion using that forget not even a chance that, but

799
00:55:19,360 --> 00:55:22,480
Speaker 1: that works for a small niche funds, and that's absolutely

800
00:55:22,719 --> 00:55:28,000
Speaker 1: now fundamental index still to this day pretty much unique

801
00:55:28,200 --> 00:55:35,040
Speaker 1: among the so called smart beta strategies, does have vast capacity.

802
00:55:35,239 --> 00:55:38,720
Speaker 1: Why you're spanning the broad macro economy, you're owning pretty

803
00:55:38,800 --> 00:55:44,520
Speaker 1: much everything you're trading consists of contratrading against the market's

804
00:55:44,600 --> 00:55:48,680
Speaker 1: most extreme bets. So when a price is soaring, you're trimming, well,

805
00:55:48,760 --> 00:55:52,640
Speaker 1: it's easy to find a counterparty to those trades. If

806
00:55:52,640 --> 00:55:54,799
Speaker 1: a stock is tumbling, you're buying. It's easy to find

807
00:55:54,800 --> 00:55:58,080
Speaker 1: a counter party of those trades. You're you're trading is

808
00:55:58,120 --> 00:56:02,640
Speaker 1: spread across a thousand and companies instead of concentrated as

809
00:56:02,680 --> 00:56:06,320
Speaker 1: with SMPI indexation in whatever stocks are being added or

810
00:56:06,360 --> 00:56:09,680
Speaker 1: deleted that particular week. So let me channel Jack Bogel

811
00:56:09,760 --> 00:56:13,400
Speaker 1: and say, all that makes perfect sense, But all that

812
00:56:13,440 --> 00:56:17,719
Speaker 1: trading is expensive, and the gains are offset by your

813
00:56:17,719 --> 00:56:21,080
Speaker 1: turnover and your trading costs. We're selling what people want

814
00:56:21,080 --> 00:56:23,840
Speaker 1: to buy, We're buying what people want to sell. So

815
00:56:23,880 --> 00:56:27,760
Speaker 1: are trading costs at this stage have been immeasurably small.

816
00:56:28,400 --> 00:56:31,799
Speaker 1: At some stage, at some size, we start to move

817
00:56:31,880 --> 00:56:34,960
Speaker 1: prices we're not there yet. How did eighty billion and

818
00:56:34,960 --> 00:56:36,799
Speaker 1: we're not there yet? You know, there's a ways to

819
00:56:36,840 --> 00:56:39,479
Speaker 1: go before you have to really be concerned about that.

820
00:56:40,000 --> 00:56:46,000
Speaker 1: The trading costs for classic capuated indexing are much more vivid,

821
00:56:46,200 --> 00:56:51,480
Speaker 1: much more substantial, measurably so because during that grace period

822
00:56:51,480 --> 00:56:55,120
Speaker 1: you get a nine percent spread between additions and discretionary deleting.

823
00:56:55,239 --> 00:56:58,600
Speaker 1: But that's not the trading costs. That's the actual na

824
00:56:58,719 --> 00:57:02,759
Speaker 1: V gain or loss from the trade. They what do

825
00:57:02,840 --> 00:57:04,799
Speaker 1: they do? They lose half a dozen stocks, they had

826
00:57:04,840 --> 00:57:07,439
Speaker 1: half a dozen stocks. They do that once a year.

827
00:57:07,880 --> 00:57:10,920
Speaker 1: Their trading costs accord. He does it multiple times a year,

828
00:57:11,440 --> 00:57:13,879
Speaker 1: one or two or three stocks, when there's mergers, when

829
00:57:13,880 --> 00:57:17,320
Speaker 1: there's takeouts, when there's things like that. But when I

830
00:57:17,360 --> 00:57:21,680
Speaker 1: look at something like a price to to earnings ratio,

831
00:57:22,640 --> 00:57:27,240
Speaker 1: when you're buying things fundamentally ranked by earnings that changes quarterly,

832
00:57:27,360 --> 00:57:30,640
Speaker 1: how often do you have to rebalance that that index?

833
00:57:30,760 --> 00:57:34,600
Speaker 1: There's two flavors of fundamental index. The original Footsie RAFFI

834
00:57:35,400 --> 00:57:39,080
Speaker 1: rebalances once a year and has ten to annual turnover.

835
00:57:39,120 --> 00:57:42,520
Speaker 1: That's all. And that ten to fifteen percent is spread

836
00:57:43,040 --> 00:57:47,200
Speaker 1: across a thousand companies. It's not concentrated in a dozen

837
00:57:47,320 --> 00:57:55,720
Speaker 1: additions or deletions. The um. Other flavors of Fundamental Index,

838
00:57:56,320 --> 00:58:00,520
Speaker 1: our own Raffi series and the Russell Fundamental Index are

839
00:58:00,960 --> 00:58:04,080
Speaker 1: use what we call quarterly staggered rebalancing, which means every

840
00:58:04,160 --> 00:58:06,680
Speaker 1: quarter you move one fourth of the way to the target.

841
00:58:06,800 --> 00:58:09,640
Speaker 1: Makes sense, you're letting it out. You let momentum run

842
00:58:09,640 --> 00:58:11,840
Speaker 1: on three fourths of the portfolio, and one four of

843
00:58:11,880 --> 00:58:15,240
Speaker 1: the portfolio. You say, okay, enough momentum, we're going to rebalance.

844
00:58:15,960 --> 00:58:17,960
Speaker 1: And by doing it that way, you have the same

845
00:58:18,000 --> 00:58:21,680
Speaker 1: turnover as once a year rebalancing. Still ten to fifteen

846
00:58:21,720 --> 00:58:26,280
Speaker 1: percent annual turnover. It's very low. It's very easy to trade.

847
00:58:26,920 --> 00:58:33,440
Speaker 1: So I don't I don't fret about implementation costs, trading costs,

848
00:58:33,560 --> 00:58:37,680
Speaker 1: moving market prices until we're in the trillion dollar range.

849
00:58:38,160 --> 00:58:40,960
Speaker 1: So we've talked about Van Goard as a competitor in

850
00:58:41,080 --> 00:58:44,160
Speaker 1: black Rock. Let's let's let me bring up something just

851
00:58:44,240 --> 00:58:46,840
Speaker 1: because I'm looking black Rocks. So one of our licensees

852
00:58:46,960 --> 00:58:49,640
Speaker 1: they run over ten, so they're an affiliate, not really

853
00:58:49,680 --> 00:58:53,400
Speaker 1: a true competitor. Yeah, they have competing product and they

854
00:58:53,440 --> 00:58:59,200
Speaker 1: have uh licensed product they're running over ten billion globally

855
00:58:59,240 --> 00:59:02,880
Speaker 1: and RAFFI strategies, so you're you're happy with them, Um,

856
00:59:02,920 --> 00:59:06,360
Speaker 1: I'm looking at price to sales, price to book. Let's

857
00:59:06,400 --> 00:59:10,840
Speaker 1: talk about Dimensional Funds, which is about six billion, and

858
00:59:11,040 --> 00:59:15,000
Speaker 1: their core fundamental index is similar to yours, and that

859
00:59:15,240 --> 00:59:19,000
Speaker 1: they look at price to book and other FAMA based

860
00:59:19,240 --> 00:59:23,200
Speaker 1: FAMA French based factors. But really that's the core of

861
00:59:23,200 --> 00:59:25,960
Speaker 1: of what they do. How do you look at them

862
00:59:26,000 --> 00:59:29,200
Speaker 1: relative to to research affiliates and what what you guys

863
00:59:29,280 --> 00:59:33,840
Speaker 1: do well. RAFFIE in US international and emerging markets has

864
00:59:33,880 --> 00:59:41,680
Speaker 1: has um pretty reliably trounced the d f A value

865
00:59:41,680 --> 00:59:46,920
Speaker 1: products and for an extremely simple reason. D f A

866
00:59:47,600 --> 00:59:50,640
Speaker 1: hues to the religion of efficient markets and says we're

867
00:59:50,640 --> 00:59:54,760
Speaker 1: gonna we're gonna anchor on cap waiting for I mean,

868
00:59:54,840 --> 00:59:58,000
Speaker 1: within their price to book, it'll be get they're gonna

869
00:59:58,080 --> 00:59:59,760
Speaker 1: cap you know, I'm gonna get pushed back and they're

870
00:59:59,760 --> 01:00:02,720
Speaker 1: gonna say here's wrong. I'm gonna forward you that email

871
01:00:02,720 --> 01:00:05,080
Speaker 1: and you can look forward to the email. But in

872
01:00:05,160 --> 01:00:07,800
Speaker 1: any event, they anchor on cap waiting, which is which

873
01:00:07,880 --> 01:00:12,280
Speaker 1: is a mistake, and they do wonderful work. I don't

874
01:00:12,280 --> 01:00:16,240
Speaker 1: want to be seen as suggesting that that they're doing

875
01:00:16,320 --> 01:00:21,240
Speaker 1: something bad. They're doing something better than conventional cap weighted indexing,

876
01:00:21,720 --> 01:00:25,920
Speaker 1: but they anchor on cap waiting. That's that's their starting point,

877
01:00:26,480 --> 01:00:29,800
Speaker 1: so and that's their mistake. I'm gonna have to follow

878
01:00:29,880 --> 01:00:31,960
Speaker 1: up with that. So I only have you for another

879
01:00:32,040 --> 01:00:35,480
Speaker 1: ten or fifteen minutes. I have dozens more questions. We'll

880
01:00:35,520 --> 01:00:38,640
Speaker 1: have to have you back for another time. But let

881
01:00:38,680 --> 01:00:41,920
Speaker 1: me jump into some of my favorite questions that we

882
01:00:41,960 --> 01:00:46,160
Speaker 1: ask everybody, and I'm looking forward to hearing your answers.

883
01:00:47,080 --> 01:00:51,040
Speaker 1: Tell us the most important thing that people don't know

884
01:00:51,120 --> 01:00:53,440
Speaker 1: about you. A lot of people see me as a

885
01:00:53,560 --> 01:00:58,520
Speaker 1: very serious UM research guy. Uh. In point of fact,

886
01:00:58,560 --> 01:01:03,680
Speaker 1: life is short. Having fun is absolutely crucial, and I

887
01:01:03,760 --> 01:01:07,520
Speaker 1: know that about you from camp Ko talk. And so

888
01:01:08,520 --> 01:01:12,840
Speaker 1: I don't do anything. I don't do any research. I

889
01:01:12,920 --> 01:01:18,160
Speaker 1: really don't do anything that isn't fun. I like that answer.

890
01:01:18,640 --> 01:01:21,000
Speaker 1: I'm going to skip ahead to my question number eight.

891
01:01:21,520 --> 01:01:24,720
Speaker 1: What do you do for fun outside of work? Well,

892
01:01:24,760 --> 01:01:30,200
Speaker 1: I collect vintage motorcycles, fastest of their era. I like

893
01:01:30,360 --> 01:01:35,800
Speaker 1: good wine, and I'm a movie junkie. I I have

894
01:01:35,840 --> 01:01:41,040
Speaker 1: watched UM. I write down the movies that I watch

895
01:01:41,240 --> 01:01:43,880
Speaker 1: and I rate them on a five star scale. And

896
01:01:44,480 --> 01:01:48,400
Speaker 1: I've watched over movies in the last six years. Wow,

897
01:01:48,440 --> 01:01:51,600
Speaker 1: that's a lot of movies. Do you modern stuff, classic movies?

898
01:01:51,640 --> 01:01:55,560
Speaker 1: What what's your favorite job? I don't have a favorite.

899
01:01:55,600 --> 01:01:58,040
Speaker 1: I don't watch a lot of the really old movies,

900
01:01:59,280 --> 01:02:04,240
Speaker 1: old as in twenties and thirties and forties, fifties. I

901
01:02:04,280 --> 01:02:06,560
Speaker 1: just saw Roman Holiday the other day. Oh, that's a

902
01:02:06,560 --> 01:02:11,040
Speaker 1: wonderful film. She's just delightful. Yeah, she was taken from

903
01:02:11,080 --> 01:02:15,400
Speaker 1: us much too young. But in any event, the the uh,

904
01:02:16,240 --> 01:02:20,320
Speaker 1: I like variety in films. Uh, that's grace who was

905
01:02:20,360 --> 01:02:22,720
Speaker 1: taken too young. I think Audrey Hepburn lived a fairly

906
01:02:22,800 --> 01:02:27,800
Speaker 1: long she was doing Katherine Hepburn lived a long time.

907
01:02:28,320 --> 01:02:31,520
Speaker 1: Audrey Hepburn I think died in her late sixties. You

908
01:02:31,600 --> 01:02:38,200
Speaker 1: could be right. I am just let's google that. Actually

909
01:02:38,280 --> 01:02:42,080
Speaker 1: this comes up as bing, which I don't know why. Uh,

910
01:02:42,160 --> 01:02:45,520
Speaker 1: sixty three, you are correct, very good memory. Yeah, yeah,

911
01:02:45,560 --> 01:02:48,240
Speaker 1: she had a cerebral hemorrhage while walking down the street

912
01:02:48,240 --> 01:02:54,680
Speaker 1: in Manhattan, just suddenly dropped. Really sad. But in any event,

913
01:02:55,760 --> 01:03:00,480
Speaker 1: very eclectic tastes in films, if it's mainstream Hollywood actable.

914
01:03:00,880 --> 01:03:05,120
Speaker 1: I'm bored. If it is weird and strange, I love it,

915
01:03:05,520 --> 01:03:08,000
Speaker 1: have you? Uh? I was a giant fan of the

916
01:03:08,040 --> 01:03:12,440
Speaker 1: original Blade Runner. Did you see the sequel? So the sequel,

917
01:03:12,520 --> 01:03:14,440
Speaker 1: and then I went back to watch the original for

918
01:03:14,440 --> 01:03:18,840
Speaker 1: a second time after the original is still an astounding

919
01:03:18,880 --> 01:03:22,600
Speaker 1: piece of it is I I saw the new one

920
01:03:22,840 --> 01:03:25,919
Speaker 1: in the theater and I walked out a tad disappointed.

921
01:03:25,960 --> 01:03:28,720
Speaker 1: But that was almost inevitable, and I'm waiting a full

922
01:03:28,840 --> 01:03:32,200
Speaker 1: year to see it again with a little more open minded.

923
01:03:32,720 --> 01:03:37,040
Speaker 1: Sequels are always done on the basis of an original

924
01:03:37,040 --> 01:03:42,200
Speaker 1: film that was brilliant, that was extraordinary, and so it

925
01:03:42,280 --> 01:03:47,480
Speaker 1: suffers by comparison. Patty Shack too, I did not see that.

926
01:03:47,640 --> 01:03:51,600
Speaker 1: Neither did I. But the assumption is um. So let's

927
01:03:51,640 --> 01:03:55,520
Speaker 1: talk about mentors. Who do you look at, who mentored

928
01:03:55,960 --> 01:03:59,720
Speaker 1: you earlier in your career, and who influenced your approach

929
01:03:59,800 --> 01:04:06,280
Speaker 1: to investment. Um. Early mentors were mostly people I worked

930
01:04:06,400 --> 01:04:12,840
Speaker 1: for who were brilliant. Dick Crowle at the Boston Company, UM,

931
01:04:13,000 --> 01:04:17,600
Speaker 1: Bob Lovell at Crumb and Forster at First Quadrant. He

932
01:04:17,640 --> 01:04:22,600
Speaker 1: founded First Quadrant and brought me in his uh president

933
01:04:22,720 --> 01:04:28,880
Speaker 1: and then as CEO later and um um those would

934
01:04:28,920 --> 01:04:31,880
Speaker 1: be two of my most influential mentors. People I didn't

935
01:04:31,880 --> 01:04:36,000
Speaker 1: work for who were mentors. Peter Bernstein was massively influential

936
01:04:36,360 --> 01:04:38,560
Speaker 1: god and the way I think, oh he was he

937
01:04:38,720 --> 01:04:44,160
Speaker 1: was such a mench He was against the against the gods. Well,

938
01:04:44,240 --> 01:04:47,640
Speaker 1: here here's the here's the pitch story. I want you

939
01:04:47,680 --> 01:04:50,120
Speaker 1: to publish my book. It's going to be a book

940
01:04:50,280 --> 01:04:55,000
Speaker 1: on the history of probability theory in finance, and it's

941
01:04:55,000 --> 01:04:58,640
Speaker 1: going to be a best seller. What And it was

942
01:04:58,680 --> 01:05:01,720
Speaker 1: the best seller. It's sold to famillion copies. Such an

943
01:05:01,720 --> 01:05:04,680
Speaker 1: amazing book. I'm slowly working my way through the rest

944
01:05:04,720 --> 01:05:07,640
Speaker 1: of his uh, the rest of his work. And he

945
01:05:07,760 --> 01:05:10,200
Speaker 1: influenced you from afar. Did you have talk with him

946
01:05:10,240 --> 01:05:13,800
Speaker 1: or meet? Oh? We became very good friends, very good friends.

947
01:05:14,560 --> 01:05:17,760
Speaker 1: We worked on two journal articles together, one for Harvard

948
01:05:17,760 --> 01:05:21,600
Speaker 1: Business Review and one for Financial Analyst Journal, which were

949
01:05:21,640 --> 01:05:24,840
Speaker 1: two of my favorite papers that I've been involved in,

950
01:05:25,400 --> 01:05:30,200
Speaker 1: partly because he's such an effortless writer. The paper for

951
01:05:30,240 --> 01:05:32,480
Speaker 1: the Harvard Business Review, he said, why don't you do

952
01:05:32,560 --> 01:05:36,440
Speaker 1: the first draft? I did, and he called me after

953
01:05:36,520 --> 01:05:40,160
Speaker 1: he received it and he said, Rob, there's some gems

954
01:05:40,160 --> 01:05:45,000
Speaker 1: in here, but this is a turgid mess and right

955
01:05:45,800 --> 01:05:49,120
Speaker 1: blunt a little a little blunt, but in a nice way,

956
01:05:49,160 --> 01:05:51,520
Speaker 1: in a in a hey, let me polish this up

957
01:05:51,560 --> 01:05:54,080
Speaker 1: a little bit. Yeah, let me do a complete, massive

958
01:05:54,120 --> 01:05:58,240
Speaker 1: rewrite and turn it into something that the average businessman

959
01:05:58,360 --> 01:06:01,840
Speaker 1: without any investment savvy to understand. And how did the

960
01:06:01,840 --> 01:06:04,280
Speaker 1: paper come out? Came out in the Harvard Business Review?

961
01:06:04,320 --> 01:06:06,320
Speaker 1: I mean, I mean what was what was your final

962
01:06:06,360 --> 01:06:08,440
Speaker 1: read of it? Did you like what he did? And

963
01:06:08,520 --> 01:06:12,080
Speaker 1: how could you not? How could I not? Um? The

964
01:06:12,160 --> 01:06:16,320
Speaker 1: paper was titled by Harvard Business Review. We didn't come

965
01:06:16,400 --> 01:06:21,600
Speaker 1: up with the title The Right Way to Manage your Pension. Uh.

966
01:06:21,640 --> 01:06:27,600
Speaker 1: That's a little arrogant. But it's attention grabbing. It's attention grabbing. Uh.

967
01:06:28,320 --> 01:06:31,640
Speaker 1: And it was. It was an influential paper. So since

968
01:06:31,680 --> 01:06:35,000
Speaker 1: we're talking about Peter Burnestein, let's talk about your favorite books.

969
01:06:35,000 --> 01:06:37,680
Speaker 1: What are some of the favorite things that you have read?

970
01:06:38,200 --> 01:06:41,400
Speaker 1: And I'm just gonna assume iron rand and move beyond that.

971
01:06:41,480 --> 01:06:46,040
Speaker 1: What I really like you did touch on politics, which

972
01:06:46,080 --> 01:06:50,480
Speaker 1: is another of my passions. The uh, for those um

973
01:06:50,520 --> 01:06:53,760
Speaker 1: who don't know me, I'm a libertarian. I believe in

974
01:06:53,840 --> 01:06:58,479
Speaker 1: limited government, which too few people do. The well there

975
01:06:58,480 --> 01:07:03,200
Speaker 1: are people who claim to be libertarians, but really they're

976
01:07:03,240 --> 01:07:06,120
Speaker 1: libertarians for a specific issue, and then they want the

977
01:07:06,160 --> 01:07:11,520
Speaker 1: government intervening elsewhere wherever it's convenient for them. That's human nature.

978
01:07:11,600 --> 01:07:17,480
Speaker 1: The the UH in investments against the gods would be

979
01:07:17,520 --> 01:07:22,200
Speaker 1: tough to beat. It's fantastic. Anything by Ben Graham is

980
01:07:22,320 --> 01:07:28,160
Speaker 1: a must read intelligent investor. Yeah, um, security and security analysis.

981
01:07:28,160 --> 01:07:33,520
Speaker 1: You want to dive in deep um. These are some

982
01:07:33,600 --> 01:07:39,400
Speaker 1: of the giants of our industry. Um. Outside of investing,

983
01:07:39,400 --> 01:07:42,400
Speaker 1: and actually most of my reading when I'm not working

984
01:07:42,680 --> 01:07:47,480
Speaker 1: is outside of investing. A couple of really fun books.

985
01:07:48,520 --> 01:07:53,560
Speaker 1: Four Really, there was a year after Columbus sailed for

986
01:07:53,600 --> 01:07:55,880
Speaker 1: the New There was a book called fourteen nine one

987
01:07:55,920 --> 01:08:03,160
Speaker 1: that examined the America's before Aumbus landed, and the population

988
01:08:03,160 --> 01:08:07,520
Speaker 1: of the America's back then was probably in the in

989
01:08:07,600 --> 01:08:12,320
Speaker 1: the couple of hundred million range. Really, I would not

990
01:08:12,440 --> 01:08:16,280
Speaker 1: have guessed that massively wiped out by measles and other

991
01:08:16,320 --> 01:08:23,120
Speaker 1: diseases where they had no no built up, no genetic

992
01:08:23,520 --> 01:08:28,040
Speaker 1: ability to fight those diseases, so wiped out a hundred

993
01:08:28,040 --> 01:08:33,040
Speaker 1: million plus people. Native Americans were here before Columbus landed.

994
01:08:33,120 --> 01:08:37,200
Speaker 1: I would earlier arrivers. Earlier arrivers said that the shores

995
01:08:37,920 --> 01:08:44,240
Speaker 1: were teeming with people. Fascinating. Uh was the sequel to

996
01:08:44,280 --> 01:08:46,320
Speaker 1: that book, and I think it was even better. It

997
01:08:46,479 --> 01:08:52,599
Speaker 1: shows how global commerce has reshaped the world and how

998
01:08:52,640 --> 01:08:58,400
Speaker 1: it creates seeds of tremendous risk. Another book that I've

999
01:08:58,400 --> 01:09:02,559
Speaker 1: been enjoying immensely, partly because it's so you can pick up,

1000
01:09:02,600 --> 01:09:04,479
Speaker 1: pick it up, read a page or two, and then

1001
01:09:04,520 --> 01:09:07,160
Speaker 1: put it down and come back to it a month

1002
01:09:07,240 --> 01:09:10,439
Speaker 1: later and you haven't missed a beat. It's Letters of Note,

1003
01:09:10,720 --> 01:09:15,320
Speaker 1: which is filled with letters written from person AID to

1004
01:09:15,400 --> 01:09:21,759
Speaker 1: person B going back three thousand years to modern times.

1005
01:09:22,320 --> 01:09:26,880
Speaker 1: Letters that are just fascinating. A letter from a woman

1006
01:09:27,880 --> 01:09:32,920
Speaker 1: in China to her husband headed off to war UM

1007
01:09:32,960 --> 01:09:41,200
Speaker 1: written four b C and the passion in her worrying

1008
01:09:41,240 --> 01:09:46,400
Speaker 1: about him, wanting him to be spectacular in battle, but

1009
01:09:46,560 --> 01:09:51,160
Speaker 1: please come home, and uh, you know, it just reminds

1010
01:09:51,240 --> 01:09:53,639
Speaker 1: you that all of these people were human beings. Even

1011
01:09:53,680 --> 01:09:58,959
Speaker 1: a letter from um Jack the ripper to the police

1012
01:09:59,160 --> 01:10:04,160
Speaker 1: taunting them, and you feel the evil humanity in the letter.

1013
01:10:04,479 --> 01:10:07,280
Speaker 1: It's just fascinating. I'm gonna definitely check that out. That

1014
01:10:07,280 --> 01:10:10,800
Speaker 1: that is quite impressive. Tell us about a time you

1015
01:10:10,920 --> 01:10:16,439
Speaker 1: failed and what you learned from the experience. Well, I

1016
01:10:16,479 --> 01:10:21,160
Speaker 1: was invited to be a global equity strategist at Solomon

1017
01:10:21,240 --> 01:10:27,519
Speaker 1: Brothers in nineteen eighty seven. UM. I lasted there for

1018
01:10:27,640 --> 01:10:35,919
Speaker 1: fourteen months. UM it was a wonderful learning experience. UM

1019
01:10:36,040 --> 01:10:41,160
Speaker 1: I naively, I was only thirty two at the time,

1020
01:10:41,880 --> 01:10:44,719
Speaker 1: and I naively thought I was coming into be global

1021
01:10:44,760 --> 01:10:50,040
Speaker 1: equity strategist, But in fact I was coming into UM

1022
01:10:50,400 --> 01:10:55,160
Speaker 1: reinforced sales. Of course, of course that's the job. And

1023
01:10:55,960 --> 01:10:58,479
Speaker 1: I remember I was brought into a T T S

1024
01:10:58,560 --> 01:11:03,479
Speaker 1: pension fund um UH the week after the market crash.

1025
01:11:04,280 --> 01:11:08,240
Speaker 1: They were using a mean variance optimizer mark What's optimizer

1026
01:11:08,720 --> 01:11:13,000
Speaker 1: to look at their asset allocation, and they were puzzled.

1027
01:11:13,320 --> 01:11:19,799
Speaker 1: They said, we did an optimization. We pushed up return

1028
01:11:19,880 --> 01:11:25,400
Speaker 1: expectations for stocks, pushed down return expectations for bonds because

1029
01:11:25,439 --> 01:11:30,120
Speaker 1: the yields tumbled, pushed up the risk assumption for everything.

1030
01:11:30,160 --> 01:11:36,840
Speaker 1: By it seemed like a very reasonable, even conservative approach.

1031
01:11:37,640 --> 01:11:42,040
Speaker 1: And our optimizer is telling us to get out of

1032
01:11:42,080 --> 01:11:45,439
Speaker 1: stocks and put it all in bonds, and we're it's

1033
01:11:45,439 --> 01:11:48,640
Speaker 1: setting wrong with that, optimizes. So I came in and

1034
01:11:48,680 --> 01:11:51,720
Speaker 1: I explained the mathematics of why the optimizer would do

1035
01:11:51,840 --> 01:11:56,200
Speaker 1: that and ackwards looking as it is. No, it was

1036
01:11:56,240 --> 01:11:59,200
Speaker 1: forward looking. It was forward looking. They were assuming higher

1037
01:11:59,240 --> 01:12:03,040
Speaker 1: the inputs coming from which is now happens on misremembering this,

1038
01:12:03,760 --> 01:12:07,519
Speaker 1: Their inputs were forward looking. So they pushed up the

1039
01:12:07,600 --> 01:12:11,320
Speaker 1: return for stocks because they'd crashed, down the return for

1040
01:12:11,400 --> 01:12:14,599
Speaker 1: bonds because the yields had crashed, and it still wanted them,

1041
01:12:14,600 --> 01:12:17,479
Speaker 1: and it still wanted to put more into bonds because

1042
01:12:17,520 --> 01:12:23,240
Speaker 1: they pushed the risk up for everything. Now I went

1043
01:12:23,360 --> 01:12:26,040
Speaker 1: through with them the mathematics of why it was saying that,

1044
01:12:26,120 --> 01:12:30,120
Speaker 1: and then I concluded by saying, throw out the optimizer

1045
01:12:30,200 --> 01:12:37,120
Speaker 1: by stocks. Well, oh my goodness. The reaction at Solomon

1046
01:12:37,720 --> 01:12:44,240
Speaker 1: in response to that was fascinating because they were salivating

1047
01:12:44,240 --> 01:12:49,480
Speaker 1: over the possibility of a ten billion dollar bond portfolio

1048
01:12:49,560 --> 01:12:55,639
Speaker 1: construction exercise that would make them uh thirty forty basis

1049
01:12:55,680 --> 01:12:59,879
Speaker 1: points on on ten billion an instant fifty million dollars.

1050
01:13:00,160 --> 01:13:03,599
Speaker 1: And here comes this strategist telling the client to do

1051
01:13:03,720 --> 01:13:09,960
Speaker 1: the opposite. Um. Uh, they weren't. They were livid. And

1052
01:13:11,240 --> 01:13:15,080
Speaker 1: the lesson. The lesson learned was you've got to know

1053
01:13:15,120 --> 01:13:18,479
Speaker 1: who your client is. You've got to know what they want,

1054
01:13:19,120 --> 01:13:21,400
Speaker 1: and you've got to know whether what they want is

1055
01:13:21,479 --> 01:13:25,840
Speaker 1: what you're comfortable delivering. And and our final question, tell

1056
01:13:25,920 --> 01:13:28,880
Speaker 1: us something you know about investing today that you wish

1057
01:13:28,880 --> 01:13:33,000
Speaker 1: you knew thirty plus years ago, oh even fifteen years ago.

1058
01:13:33,040 --> 01:13:36,880
Speaker 1: When we launched fundamental Index. We published the paper and

1059
01:13:36,920 --> 01:13:41,920
Speaker 1: immediately set about saying, if you can build an enhanced

1060
01:13:41,960 --> 01:13:44,400
Speaker 1: index relative to cap weight, you can build an enhanced

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Speaker 1: index relative to fundamental index. Who better to do that

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Speaker 1: than us? Well, why don't we figure out which of

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01:13:51,680 --> 01:13:58,800
Speaker 1: the fundamental measures works best and have the mix of

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01:13:58,880 --> 01:14:05,080
Speaker 1: fundamental metrics be dynamic. And what I didn't realize at

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01:14:05,080 --> 01:14:08,200
Speaker 1: the time, we failed miserably. Everything we tried didn't work.

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Speaker 1: Um what I didn't realize at the time was when

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01:14:13,840 --> 01:14:19,840
Speaker 1: a strategy becomes massively more expensive, it'll have wonderful past

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01:14:19,880 --> 01:14:23,680
Speaker 1: returns and terrible future returns. So we were zeroing in

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01:14:23,840 --> 01:14:27,599
Speaker 1: on the fundamental metrics that the market was loving more

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01:14:27,640 --> 01:14:32,679
Speaker 1: and was poised to disappoint. So the enhancements failed. Today,

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01:14:32,720 --> 01:14:36,919
Speaker 1: I have a better understanding that valuation matters for factors

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01:14:36,920 --> 01:14:39,240
Speaker 1: and strategies just as much as it does for stocks.

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Speaker 1: Quite fascinating, we have been speaking with Rob are not

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Speaker 1: of research affiliates. If you enjoy this conversation, be showing

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01:14:47,160 --> 01:14:50,760
Speaker 1: look up an inch or down an inch on Apple iTunes,

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01:14:50,840 --> 01:14:55,280
Speaker 1: Bloomberg dot com, Stitcher, overcast, wherever finer podcasts are sold,

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01:14:55,280 --> 01:14:57,799
Speaker 1: and you can see any of the other two hundred

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01:14:57,800 --> 01:15:02,200
Speaker 1: plus such conversations we've had. We love your comments, feedback

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01:15:02,200 --> 01:15:05,800
Speaker 1: and suggestions right to us at m IB podcast at

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01:15:05,840 --> 01:15:09,120
Speaker 1: Bloomberg dot net. I would be remiss if I did

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01:15:09,160 --> 01:15:11,559
Speaker 1: not thank the crack staff that helps me put together

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01:15:11,640 --> 01:15:15,799
Speaker 1: this conversation each week. Medina Parwanner is our producer, Slash

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01:15:15,840 --> 01:15:19,200
Speaker 1: audio engineer, Taylor Riggs is our booker. Michael bat Nick

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01:15:19,320 --> 01:15:22,200
Speaker 1: is our head of research. I'm Barry Hults. You've been

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01:15:22,240 --> 01:15:25,280
Speaker 1: listening to Masters in Business on Bloomberg Radio.