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Speaker 1: The future may not be clear, but our commitment is

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Speaker 1: Pierce Veteran Smith Incorporated, a register broker dealer, remember s

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Speaker 1: I PC. This is Masters in Business with Barry Ridholts

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Speaker 1: on Bloomberg Radio this week on the show, What a delight.

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Speaker 1: I don't even know how to begin to explain this.

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Speaker 1: Uh Bill Sharp, just a legend in finance, couldn't have

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Speaker 1: been more charming and more delightful. There was a lot

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Speaker 1: of back and forth in terms of when you guys

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Speaker 1: going to be on the West Coast. I'm here, I'm there,

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Speaker 1: I'm busy week and he no longer keeps in office

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Speaker 1: at Stanford, and he didn't want to come into San Francisco.

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Speaker 1: He lives down in Carmel, and the guys in Andrews

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Speaker 1: and Horowitz were nice enough to give me a podcasting

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Speaker 1: room to sit in and record the show. I can't

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Speaker 1: begin to tell you what a delight it was to

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Speaker 1: chat with him. He's just beyond knowledgeable. He really helped,

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Speaker 1: you know, the original concept of the show is speaking

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Speaker 1: to the minds who helped shape finance and investing in business,

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Speaker 1: and who who better than Bill Sharp. So many of

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Speaker 1: the principles that we just accept as as running the

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Speaker 1: middle ordinary things in finance, Bill Sharp helped to create.

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Speaker 1: He helped to create the first index fund, He helped

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Speaker 1: to create the capital asset pricing model. He helped to

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Speaker 1: create our understanding of risk. Just stop and think about

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Speaker 1: those achievements. We we talked about a number of really

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Speaker 1: fascinating things, and I can't begin to tell you how

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Speaker 1: just delightful it is. So my job was to kind

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Speaker 1: of get out of his way and just keep nudging

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Speaker 1: him in into expounding more and more. Uh, some of

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Speaker 1: the stuff is a little wonky. It's not your usual

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Speaker 1: market based conversation because he's the guy who built the

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Speaker 1: underlying infrastructure. Suffice it to say, it's absolutely fascinating. So,

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Speaker 1: with no further ado, my conversation with Nobel Laureate William Sharp,

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Speaker 1: Professor Bill Sharp, Welcome to Bloomberg, hosted here at Andres

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Speaker 1: and Harrowitz. Your dissertation was called the single index model.

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Speaker 1: Tell us what that was about. Okay, it'll be won't

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Speaker 1: be as short as possible answer, but it'll be a

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Speaker 1: lot shorter than the dissertation the Harry Marco What's His

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Speaker 1: work was what we would call normative, in the sense

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Speaker 1: that he was asking the question, your portfolio manager, there

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Speaker 1: are securities, there's a client. How do you build a

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Speaker 1: portfolio that's good for the client. And in his structure

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Speaker 1: he allowed for not only as tom to the expected

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Speaker 1: return on general motors, let's say, the risk of general

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Speaker 1: motors stock, but also the extent to which general motors

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Speaker 1: would move with Ford, with general foods, etcetera. A whole

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Speaker 1: lot of number precisely, so, a whole lot of numbers.

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Speaker 1: And he had a procedure to find a so called

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Speaker 1: set of efficient portfolios given that set of numbers, and

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Speaker 1: it took a big computer, a lot of time, a

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Speaker 1: lot of money to do it. Um. He also, in

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Speaker 1: his in his work, had suggested you might simplify the

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Speaker 1: relationships among securities, and he proposed a number of versions,

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Speaker 1: one of which was, well, you could say general motors

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Speaker 1: moved with the market to a certain extent, general foods

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Speaker 1: moved with the market to a certain extent, each of

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Speaker 1: them had movements on their own and leave it at that.

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Speaker 1: In other words, a very simple model in which there

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Speaker 1: was one single index, let's call it the market for

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Speaker 1: for now, which created all the correlation, all the co

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Speaker 1: movement among securities. So he essentially before we talked about data,

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Speaker 1: he had to come up with the concept of, well,

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Speaker 1: here's what the market actually well, and I hope I

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Speaker 1: recalling correctly. In his version, this was just, oh, you

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Speaker 1: might want to make this simple assumption. And then there

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Speaker 1: were a couple of other authors that were writing with

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Speaker 1: that same kind of structure, but there was no sense

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Speaker 1: this would be the market portfolio was just a thing,

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Speaker 1: a single index. But but so I. So what I

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Speaker 1: did was I I took that concept and I did

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Speaker 1: three things in the dissertation. One was I wrote a

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Speaker 1: computer algorithm that could take advantage of that simplified structure

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Speaker 1: and find efficient portfolios, you know, for orders of MAGA

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Speaker 1: it to less computer time. Then if you expanded it

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Speaker 1: to the full structure. So the first part of the

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Speaker 1: dissertation was an algorithm and a foretrend program. Probably the

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Speaker 1: first dissertation and economics at U c L A. That

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Speaker 1: included programs um The second I UH Fred Weston had

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Speaker 1: a friend who was an investment advisor, a real human

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Speaker 1: investment advisor, and so I worked with him to try

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Speaker 1: to get him to make probabilistic forecasts for a group

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Speaker 1: of securities, and then we ran them through the algorithm

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Speaker 1: to see what it implied. And then the third I

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Speaker 1: did what my training as a micro economist, which is

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Speaker 1: most of my training, UH, would cause me to do,

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Speaker 1: what if everybody did this? What if everybody did what

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Speaker 1: Harry said? What would the world look like? So Harry's

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Speaker 1: portfolio theorem would basically guide all the investors. That was

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Speaker 1: my assumption. So I turned from what should you do

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Speaker 1: normative to how might the world work? Positive theory, which

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Speaker 1: is what economists at that time in particular generally did,

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Speaker 1: and said, well, if everybody did this and markets cleared

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Speaker 1: and prices adjusted, what would be the relationship in equilibrium

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Speaker 1: between expected returns on securities and some measure of risk?

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Speaker 1: And the conclusion was again this was positing, this single

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Speaker 1: index model. Conclusion was that the thing, the common factor

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Speaker 1: that would matter would be the market portfolio. And that's

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Speaker 1: when the term beta came to be, and that expected

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Speaker 1: returns would be related only to beta's in a linear manner.

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Speaker 1: For that matter, were you aware at the time how

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Speaker 1: innovative and groundbreaking and influential this idea would be going forward?

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Speaker 1: Was it? Oh, I think I have a pretty nice

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Speaker 1: dissertation here the ladder, the ladder. And then I finished

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Speaker 1: the dissertation in June and then started teaching the University

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Speaker 1: of Washington in September, and you know, I'd written up

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Speaker 1: the algorithm for a paper, and uh now I was saying,

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Speaker 1: this is really a nifty result. This they'd you know,

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Speaker 1: x equilibrium result. But it's sort of like, Okay, I

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Speaker 1: pulled a rabbit out of a hat. But but I

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Speaker 1: put it in beforehand with this single index model assumption,

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Speaker 1: and I said, boy, it would be nice if I

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Speaker 1: could get that beautiful answer without making this assumption which

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Speaker 1: almost directly created the answer. So I I noodled around

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Speaker 1: and talked to colleagues and um thought of it, and

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Speaker 1: then all of a sudden, without you know, within two

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Speaker 1: or three or four months, said wait a minute, I

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Speaker 1: don't have to make that assumption. I can get that

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Speaker 1: result in a general situation. And at that point that

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Speaker 1: was the capital has at pricing model m HM and uh.

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Speaker 1: I first wrote it up and submitted it for publication

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Speaker 1: in sixty two, and it was rejected by a referee.

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Speaker 1: And then how fantastic is that? Yeah? And and I

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Speaker 1: finally published it in sixty four. It took you three

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Speaker 1: years to get your paper on capital, and that ultimately

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Speaker 1: is what leads That was what was cited in the

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Speaker 1: Nobel Prize. Yeah. Isn't that fascinating that such an interesting

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Speaker 1: innovative idea rejected for a few years. Well, you know,

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Speaker 1: I happen to know. I found out who the referee

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Speaker 1: was and his argument was, well, that's an unrealistic assumption.

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Speaker 1: And and I was taught by Milton Friedman indirectly in

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Speaker 1: others that you don't evaluate a theory of that sort

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Speaker 1: on the assumption. You evaluated on the conformance of the

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Speaker 1: results with the real world, because there is always making something.

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Speaker 1: The thesis is, we're gonna start with these assumptions. Where

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Speaker 1: does it lead to? It leads us here? Isn't that interesting? Exactly? So?

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Speaker 1: Um So? In any event, and as a matter of fact,

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Speaker 1: when it was finally published in the journal Finance, you know,

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Speaker 1: I asked as far as the refereeing. I asked it,

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Speaker 1: what could we have another referee? Please? None? They changed

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Speaker 1: editors and such. But when it was published, I thought, well,

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Speaker 1: this is the best thing I'm ever going to do,

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Speaker 1: and in that I was correct. And and I sat

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Speaker 1: by the phone. We didn't have computers in those days,

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Speaker 1: no no text or emails, waiting for the phone to ring.

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Speaker 1: And it didn't ring, and it didn't ring, and I thought,

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Speaker 1: you know, months passed and that, man, I've just written

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Speaker 1: the best paper I'm ever going to write, and nobody cares.

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Speaker 1: But eventually people started paying attention to That's a that's

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Speaker 1: a fascinating story. It's very it would be very different now. Well,

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Speaker 1: things seemed to ricochet around the world nearly much more rapidly,

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Speaker 1: although I don't think people recognize the depth with a

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Speaker 1: gravitas of certain ideas right away. But what are you

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Speaker 1: thinking during that period where you know you've found something

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Speaker 1: unique and valuable and nobody else's is recognizing that yet? Well, um,

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Speaker 1: needless to say, you have some Maybe it wasn't I

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Speaker 1: knew it was unique. Well, there's some dispute about that too.

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Speaker 1: Others were going down similar paths and in various ways.

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Speaker 1: But but I thought it was valuable, and I was,

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Speaker 1: and I thought, well, if this isn't valuable, let me

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Speaker 1: give you a little broader context. In those days, economics

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Speaker 1: was theory and equilibrium and all that sort of stuff.

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Speaker 1: Finance was very old timey by any modern standards. And

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Speaker 1: so I was one of the first economists that went

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Speaker 1: into the field of finance Fred Weston before me, to

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Speaker 1: try to bring some of the rigor of economics to finance.

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Speaker 1: And so there was a kind of a cultural issue

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Speaker 1: as well, that's interesting and so, but the journal Finance

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Speaker 1: had a number of I would call let's call them

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Speaker 1: scientific articles. It was not unusual, but it was. It

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Speaker 1: was a change for the field of finance, not only

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Speaker 1: in practice, but also in academics, which is, you know,

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Speaker 1: there was no field call financial economics. Now there is.

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Speaker 1: How how influence was Harry Markoitz and your work with

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Speaker 1: him it rans on you eventually creating the capital asset

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Speaker 1: pricing model. Well, obviously, if I hadn't read Harry's work,

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Speaker 1: if I hadn't done the work in the first part

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Speaker 1: of the dissertation, I wouldn't have moved to go to

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Speaker 1: the stage of asking the question what if everybody did this,

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Speaker 1: so in that sense crucial um it was interesting. Harry.

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Speaker 1: Two or three years later he said, oh, I just

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Speaker 1: re read your paper, and now I see you didn't

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Speaker 1: assume that you actually derived it. So so you know,

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Speaker 1: I will say that that part I think was pretty

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Speaker 1: much my work. Fascinating, although, as I say others, we're

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Speaker 1: beginning down a similar path. Jack Traynor, you know, he

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Speaker 1: didn't publish, but he was going down and he came

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Speaker 1: at it differently. Where was Jack Traynor? He was well,

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Speaker 1: he wasn't at heart. He he was working I think

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Speaker 1: as a student at Harvard. I believe he was at

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Speaker 1: Arthur D. Little when he was doing that work. He

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Speaker 1: came at it a different way. And I became aware

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Speaker 1: of his work a year or two after I had

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Speaker 1: submitted Mind for publication. So I put a footnote in saying,

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Speaker 1: you know, here's this other work you should be aware of.

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Speaker 1: So you become a professor emeritus and decided to spend

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Speaker 1: some time consulting at William F. Sharp Associates. What sort

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Speaker 1: of consulting work were you doing back? Actually was when

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Speaker 1: I finally bit the bullet and became an emeritus professor.

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Speaker 1: My wife and I started a research slash consulting firm

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Speaker 1: in eighty six which went through an eight nine and

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Speaker 1: I took leave. Then I went back and then I

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Speaker 1: thought I need to do it full time. UM and

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Speaker 1: so that lasted six years in different manifestations up. What

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Speaker 1: we were trying to do is bring I hate to

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Speaker 1: call it modern finance theory, I hate that term, but

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Speaker 1: financial economics theory, you know, empirical work to bear on

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Speaker 1: the problems faced by the manager of the General Motors

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Speaker 1: Pension Fund, the manager of the Stanford University endowment, so

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Speaker 1: professionals who were managing large pools of money in an

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Speaker 1: institutional setting. And so the idea was bring to bear

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Speaker 1: the research that existed, and do new research and bring

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Speaker 1: And so we set up this firm and again in

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Speaker 1: various manifestations, and we worked with General Motors, pension Fund, endowment, etcetera.

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Speaker 1: So a lot of really substantial institutions with a lot

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Speaker 1: were encountering in the real world that your theory helped resolve? UM. Well, first, uh,

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Speaker 1: what were the risks, Where were the risks? What was

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Speaker 1: their performance? How did it you know, was it good

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Speaker 1: or a bad relative to the risks that we're taking.

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Speaker 1: People really were not all that clued into risk adjusted

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Speaker 1: performance or had at that point. It was better understood

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Speaker 1: at one level, but not. For example, one of the

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Speaker 1: things that came out of that was what's called returns

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Speaker 1: based style analysis. How do you get your hand You've

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Speaker 1: got this portfolio've got a hundred different money managers out there,

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Speaker 1: how do you get your arms around it. Who's doing what?

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Speaker 1: How does this piece fit in with that piece? Are

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Speaker 1: level or less or more? Um. We take that for

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Speaker 1: granted that you could today run a spreadsheet, crunch froom

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Speaker 1: not simple to do best so so for example, returns

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Speaker 1: based anile analysis. And again the idea was to get

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Speaker 1: your arms around the whole portfolio, time managed portfolio and

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Speaker 1: evaluated as a whole. Try to figure out whether or

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Speaker 1: not you've got the right pieces and you've got them

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Speaker 1: in the right magnitudes and uh, you know, at the

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Speaker 1: end of the day, are you adding value? So so,

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Speaker 1: there are a number of problems and uh, we got

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Speaker 1: to deal with the real world very sophisticated clients and um,

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Speaker 1: try out some new ideas, developed some new ideas. It was,

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Speaker 1: it was, it was pretty heavy. So you joined the

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Speaker 1: Rand Corporation in nineteen fifty six and you meet Harry

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Speaker 1: Markowitz who ultimately helps you with your dissertation. Tell us

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Speaker 1: about what it was worth like working with the markt

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Speaker 1: Witz on your PhD project. Well, let me if I

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Speaker 1: may do a little more of the backstory. Make sure

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Speaker 1: when I first went to RAND out, I came out

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Speaker 1: of the service with a master's degree to RAND. And

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Speaker 1: when I first went, Harry was not there at that point,

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Speaker 1: um and um. So I was working on logistics issues,

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Speaker 1: big models and computer programs and what have you. And

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Speaker 1: I decided I wanted to teach. So I the path

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Speaker 1: of least resistance was to take some education courses get

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Speaker 1: a junior college credential. I took one education course at

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Speaker 1: night and decided no, I'd rather get a PhD and

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Speaker 1: teach at the university level. So I was able to

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Speaker 1: working full time supporting my family at rand because they

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Speaker 1: were very generous in that regard. Uh I started a

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Speaker 1: dissertation a totally different subject, transfer pricing, using a lot

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Speaker 1: of operations research methodology, and when Jack Hurst Lifer, whose

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Speaker 1: work I was building on, came to U c l A,

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Speaker 1: my adviser said, once you go talk to Jack, and

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Speaker 1: I did, and Jack read my half dissertation and said,

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Speaker 1: I don't I don't think there's a dissertation here that

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Speaker 1: has to be a little frustrating you. Half that was,

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Speaker 1: but I've remember Jack's dead now, but telling him more

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Speaker 1: than once that he did me a great favor because

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Speaker 1: then I went to Fred Weston, financial economist at U

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Speaker 1: c l A, and said, what am I gonna do?

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Speaker 1: He said, well, you really liked this work Harry Marco,

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Speaker 1: what's did? Harry has just come to rand On. Donce

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Speaker 1: you go talk to Harry. So I did, and I

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Speaker 1: worked out an arrangement between Fred and our i'm an

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Speaker 1: Alchin and at U c l A, and Harry, who

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Speaker 1: was not at U c l A, that I'd work

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Speaker 1: with Harry. So it was a little more. He wasn't

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Speaker 1: a professor at US, but he effectively acts as your

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Speaker 1: PhD advisor. Well, it was a little more collegial because

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Speaker 1: you know, we're both working around together. Um, and he

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Speaker 1: didn't have any authority, but but yes, basically he and

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Speaker 1: I chatted about this and that and wanting to try

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Speaker 1: that and and the rest and and so forth. So

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Speaker 1: there are worse PhD thesis advisors than Harry markets every day.

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Speaker 1: I'm thankful that that worked out. Let's let's talk a

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Speaker 1: little bit about capham and and how how that has

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Speaker 1: evolved over time. First, when you first introduced it, has

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Speaker 1: your thinking evolved on that since or is it still

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Speaker 1: what it was when you first thought it up? Now

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Speaker 1: it has evolved and people are sometimes surprised of this.

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Speaker 1: And let me if I may sort a little bit

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Speaker 1: of time. The capitalistic pricing model builds on Harry's view

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Speaker 1: of the world, which is that you think about the

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Speaker 1: world in terms of mean and variance, expected return and

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Speaker 1: risk variations mean variance. So here here's what we here's

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Speaker 1: what's average, here's how much you can how much volatility

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Speaker 1: around that is exactly what you should expect and and

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Speaker 1: what you might not get despite your expectations. And then

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Speaker 1: what I did in my equilibrium model is say, well,

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Speaker 1: what if everybody thinks about the world that way, and

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Speaker 1: they come to market and they trade with each other

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Speaker 1: and prices at just what would one expect to find,

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Speaker 1: and not surprisingly you find that securities in that world

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Speaker 1: would be priced so higher expected return goes with higher beta,

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Speaker 1: which is a measure of how things move together, and

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Speaker 1: it's related to this variance mean variance structure. And you know,

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Speaker 1: the economical line is it's market risk that matters. That's

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Speaker 1: what you get rewarded for. Other risks you don't get

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Speaker 1: paid for. That's sort of the bottom line. About the

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Speaker 1: time Harry was first working, can Arrow and Gerard Debreu

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Speaker 1: independently developed what came to be known as state preference theory,

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Speaker 1: which basically is a model of prices in an equilibrium framework.

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Speaker 1: And the basic idea there, to just make it overly simplified,

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Speaker 1: is that you know, how much would it cost you

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Speaker 1: to buy a security that pays you a dollar three

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Speaker 1: years from now, if at that point the market is up,

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Speaker 1: you know, give or take what would that cost, and

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Speaker 1: there's some number present value of that, and then you

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Speaker 1: just think of the world. There's a whole bunch of

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Speaker 1: those and when you put that into a security market context,

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Speaker 1: you again get the result that it's market risk that matters,

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Speaker 1: but it may matter in a different way instead of

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Speaker 1: in a certain kind of diagram. Instead of a straight line,

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Speaker 1: it may be a curve. Just to widely oversimplify. And

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Speaker 1: the great thing about that view is that it extends

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Speaker 1: very beautifully to multi periods. It's it's much more general.

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Speaker 1: And so that approach, which now in PhD programs and

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Speaker 1: finance often is called pricing kernel k E R N

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Speaker 1: E l UH. It has the same character, it has

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Speaker 1: many of the same pragmatic results, but it's more general,

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Speaker 1: and so that's what I use. I do not use

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Speaker 1: the capitalized pricing model in my models in my work,

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Speaker 1: which surprises some people. So you're you're using what is

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Speaker 1: effectively the natural evolution of that. Well, they happen to come.

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Speaker 1: They sort of the paths combined in some sense with

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Speaker 1: Jack kerchlife for at U C l A, and Mark

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Speaker 1: Rubinstein is his student there um. But so in some

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Speaker 1: sense they're not sequential. But yes, I do, and I

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Speaker 1: try to because I this is taught, as I say,

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Speaker 1: at the PhD level, I think it ought to be

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Speaker 1: taught at the NBA level and the undergraduate level. So

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Speaker 1: in two thousand and seven I published a book the

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Speaker 1: name of which I can't quite remember, that came out

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Speaker 1: of some lectures I gave at Princeton, trying to make

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Speaker 1: the case that yes, you can teach undergraduates and NBA

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Speaker 1: students this approach rather than mean variance slash C A

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Speaker 1: p M. Although again, as they say qualitatively, they're not

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Speaker 1: wildly different. So the two thousand and seven book is

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Speaker 1: called Investors and Markets Portfolio Choices, Asset Prices, and Investment Advice.

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Speaker 1: Assuming Amazon is giving me the correct information. Amazon never failed,

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Speaker 1: So so let's talk a little bit about that. Um,

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Speaker 1: you know, we used to have memories and now we

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Speaker 1: have these devices with us, and it's like I've outsourced.

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Speaker 1: I knew the name of that book, and I couldn't,

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Speaker 1: for the life of me recall it because it's just

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Speaker 1: so easy to pull it up. Um paper, you you

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Speaker 1: discussed earlier capital asset prices, the theory of market equilibrium

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Speaker 1: under conditions of risk. I'm gonna pull a quote from

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Speaker 1: that that I think is really powerful. Diversification enables the

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Speaker 1: investor to escape all but the risk resulting from swings

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Speaker 1: and economic activity. This type of risk remains even inefficient combinations.

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Speaker 1: That's very powerful. How did you come upon that because

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Speaker 1: it's not obvious or intuitive. Well, as I say that,

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Speaker 1: you know that statement really needs another sence. Okay, as

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Speaker 1: wonderful and I appreciate your finding it. The um the

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Speaker 1: basic idea is that is the risk for which you're

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Speaker 1: going to be rewarded if you expose you If you

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Speaker 1: expose yourself more dramatically to down markets, then in a

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Speaker 1: long run you should do better. In the short run,

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Speaker 1: you can be wiped out at least widely injured. Um So,

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Speaker 1: so that's the basic notion. And in the single index

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Speaker 1: model that's assumed in the more general capitalizet pricing model,

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Speaker 1: that's a conclusion. So so the assumptions, and you you

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Speaker 1: discussed this earlier about how it was potentially problematic um

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Speaker 1: for some of the people who were refereeing your your

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Speaker 1: initial papers. Were the assumptions fairly straightforward in order to

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Speaker 1: test the thesis or did you have to go out

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Speaker 1: on a ledge a little bit with with some of

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Speaker 1: your assumptions? Well, you know, I mean the the models,

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Speaker 1: either the dissertation or the subsequent one. You know our models,

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Speaker 1: You make some assumptions, and then you do some some

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Speaker 1: calculations and some operations. You get a conclusion. I did, ah,

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Speaker 1: a test that is so crude. I don't even we

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Speaker 1: don't remember how crude our data sets were. I mean,

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Speaker 1: I did a test in my dissertation. I used annual

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Speaker 1: returns on thirty mutual funds. That was my database, and

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Speaker 1: I had to put it on index cards and go

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Speaker 1: to the library and write down all the numbers and

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Speaker 1: use a hand, you know, a desk calculator. Um. So,

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Speaker 1: over the years, of course, we did more sophisticated test

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Speaker 1: had better databases. And that said, even today with all

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Speaker 1: that we have, um it's hard. You know, there's a

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Speaker 1: lot of noise and what happens in security shorts, so

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Speaker 1: it's hard to find what might be at the core

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Speaker 1: and a truth for the long term. Let us say, so,

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Speaker 1: let's let's talk a little bit about risk, because you

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Speaker 1: are certainly known for your work beyond the capitalist pressing

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Speaker 1: model on risk. What is the appropriate way to think

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Speaker 1: about risk as an investor? Well, let me give you

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Speaker 1: an anecdote for that question. The first time I met

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Speaker 1: Peter Bernstein, who is legendary and I suspect many of

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Speaker 1: your listeners his work. No, he was. He was a sweetheart.

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Speaker 1: First time I met him, we had lunch somewhere in

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Speaker 1: New York and he was then managing money for wealthy

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Speaker 1: clients and we were talking about risk and risk aversion

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Speaker 1: and risk tolerance and and he said, well, do you

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Speaker 1: know when I know what the true risk tolerance of

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Speaker 1: a client is? And I, being young and naive, said no.

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Speaker 1: When Peter he said, well, after the markets had a

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Speaker 1: really bad day and I get a call at two

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Speaker 1: am saying I can't take it. I can't take it

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Speaker 1: from a guy who said, oh I can take risk.

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Speaker 1: So so mes ng risk. But I think for most

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Speaker 1: human beings, risk is losing a lot of money in

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Speaker 1: a short period of time, unexpectedly. And then the question

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Speaker 1: is how do you I mean, that's a little too

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Speaker 1: amorphous to put in a nice, rigorous mathematical model, but

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Speaker 1: presumable if you if you take something that could go

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Speaker 1: up or it could go down, is more likely to

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Speaker 1: go up than to go down. Then risk is a

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Speaker 1: probability distribution, and the wider it is, the more risk

430
00:27:31,240 --> 00:27:34,600
Speaker 1: there is. And you can start using measures like variants

431
00:27:34,680 --> 00:27:38,560
Speaker 1: or standard deviation to try to try to simplify that,

432
00:27:38,680 --> 00:27:43,679
Speaker 1: but um, it's it's just But yes, the downside is

433
00:27:43,680 --> 00:27:46,040
Speaker 1: what we worry about. We don't worry about upside risk.

434
00:27:46,680 --> 00:27:49,080
Speaker 1: That's okay, it seems to take care of itself. But

435
00:27:49,200 --> 00:27:53,680
Speaker 1: they very often go together. To Peter Bernstein's point, when

436
00:27:53,680 --> 00:27:58,359
Speaker 1: we discuss risk tolerance, we think about it objectively, but

437
00:27:58,560 --> 00:28:01,960
Speaker 1: in reality, really asking people is how do you feel

438
00:28:01,960 --> 00:28:04,880
Speaker 1: about what has happened over the past month? And it's

439
00:28:05,400 --> 00:28:07,399
Speaker 1: and I know there have been lots of studies about this.

440
00:28:07,840 --> 00:28:11,520
Speaker 1: When the market is strong, people's claim to have Oh,

441
00:28:11,520 --> 00:28:14,240
Speaker 1: I have a very high restolerance, and when the market's

442
00:28:14,240 --> 00:28:16,720
Speaker 1: getting shell acted, so I listen, I can't lose any money.

443
00:28:16,680 --> 00:28:20,800
Speaker 1: I have a very low restolerance. It's amazing there's no

444
00:28:20,880 --> 00:28:24,040
Speaker 1: objective way to self measure ourselves. But that seems to

445
00:28:24,040 --> 00:28:26,639
Speaker 1: be the case. Yeah, this is, you know, strange you asked.

446
00:28:26,640 --> 00:28:31,280
Speaker 1: Just yesterday, I spent an hour with a woman who

447
00:28:31,320 --> 00:28:33,840
Speaker 1: she's actually a wife of a friend of mine in

448
00:28:33,960 --> 00:28:38,280
Speaker 1: totally different context, and she's a financial advisors to individuals,

449
00:28:38,800 --> 00:28:44,640
Speaker 1: generally young techie types, and her question was your question,

450
00:28:45,280 --> 00:28:49,160
Speaker 1: how do I how do I talk about risk to

451
00:28:49,280 --> 00:28:52,720
Speaker 1: my client? How do I estimate his or her tolerance

452
00:28:52,760 --> 00:28:56,720
Speaker 1: for risk? And you know there are questionnaires and psychological

453
00:28:56,800 --> 00:29:00,160
Speaker 1: this and that, and she's tried those two and they're

454
00:29:00,200 --> 00:29:04,520
Speaker 1: not very satisfying. It's it's it's very difficult. It's so

455
00:29:04,680 --> 00:29:07,200
Speaker 1: colored by what just happened. Let let me ask you

456
00:29:07,240 --> 00:29:10,560
Speaker 1: the same question differently, and this time I will invoke

457
00:29:10,760 --> 00:29:16,200
Speaker 1: the sharp ratio. The sharp ratio treats upside volatility equal

458
00:29:16,280 --> 00:29:19,800
Speaker 1: to downside volatility, but as you just point out, they're

459
00:29:19,840 --> 00:29:23,040
Speaker 1: really not equal. We were much more concerned about downside

460
00:29:23,080 --> 00:29:29,200
Speaker 1: volatility and downside deviation. UM is downside deviation a better

461
00:29:29,280 --> 00:29:35,440
Speaker 1: metric than than standard volatility. How should we really conceptualize this? Well,

462
00:29:35,520 --> 00:29:38,920
Speaker 1: let me go back and Harry's early work. He had

463
00:29:38,960 --> 00:29:42,080
Speaker 1: a section I think was in the book Uh saying well,

464
00:29:42,120 --> 00:29:44,720
Speaker 1: maybe we ought to use He focused on what's called

465
00:29:44,720 --> 00:29:49,440
Speaker 1: semi variance. Variance is risk squared. Let's call it up

466
00:29:49,480 --> 00:29:52,680
Speaker 1: and down semi variance as a measure of the downside

467
00:29:53,440 --> 00:29:55,440
Speaker 1: UM And it's like very you know, it takes all

468
00:29:55,480 --> 00:29:58,760
Speaker 1: the possible downsides and waits them and squares them and

469
00:29:58,840 --> 00:30:02,560
Speaker 1: such um and and people have come up. I think

470
00:30:02,560 --> 00:30:06,800
Speaker 1: Frank Sartino has a ratio which had at one point

471
00:30:06,880 --> 00:30:10,840
Speaker 1: that uses downside uh and yes, I mean there's no

472
00:30:10,880 --> 00:30:14,560
Speaker 1: doubt about it. And certainly the behavior literature tells us

473
00:30:14,600 --> 00:30:18,400
Speaker 1: that people wait downside much more heavily than they wait upside.

474
00:30:19,200 --> 00:30:23,120
Speaker 1: So so that all is very appealing and attractive. It's

475
00:30:23,160 --> 00:30:28,480
Speaker 1: extremely difficult to build equilibrium models because the mathematics gets

476
00:30:28,560 --> 00:30:33,080
Speaker 1: really squirrely UM and I've tried and failed. Others have tried,

477
00:30:33,320 --> 00:30:39,880
Speaker 1: perhaps failed as well. But the but in many cases,

478
00:30:41,200 --> 00:30:43,800
Speaker 1: the distribution, if you want to think of it that way,

479
00:30:43,920 --> 00:30:48,720
Speaker 1: is symmetric enough. So if you measure the square deviation

480
00:30:48,880 --> 00:30:52,600
Speaker 1: from the mean, which is standard deviation and or related

481
00:30:52,640 --> 00:30:56,480
Speaker 1: that's variants UM or you measure the square deviation on

482
00:30:56,520 --> 00:31:02,280
Speaker 1: the downside, you get similar numbers and uh more securities

483
00:31:02,280 --> 00:31:04,560
Speaker 1: in your portfolio than more likely that is to be

484
00:31:04,640 --> 00:31:09,600
Speaker 1: the case in most circumstances. So so although we've talked

485
00:31:09,600 --> 00:31:15,880
Speaker 1: about that and thought about that at the portfolio security level, UM,

486
00:31:15,920 --> 00:31:19,400
Speaker 1: because the mathematics gets so so ugly, we tend to

487
00:31:19,400 --> 00:31:23,200
Speaker 1: stay with variants and in the sense that maybe it's

488
00:31:23,280 --> 00:31:29,320
Speaker 1: it's it's close enough approximation. So you just referenced um

489
00:31:30,520 --> 00:31:34,440
Speaker 1: investor expectations as part of a risk model of thinking

490
00:31:34,480 --> 00:31:37,640
Speaker 1: about what actual risk is. How has your thinking on

491
00:31:37,800 --> 00:31:41,480
Speaker 1: investor expectations evolved over the years? UM, While I was,

492
00:31:42,200 --> 00:31:44,960
Speaker 1: I'm reminded of I think it was George Stiegler who

493
00:31:44,960 --> 00:31:51,240
Speaker 1: wrote about firms maximizing profits, etcetera, and said, anytime I

494
00:31:51,360 --> 00:31:53,240
Speaker 1: visit the manager of a real firm, I have to

495
00:31:53,240 --> 00:31:57,040
Speaker 1: go back and reread my textbooks. UM, the same thing

496
00:31:57,080 --> 00:32:00,840
Speaker 1: with investors. I guess my view as we all know

497
00:32:01,760 --> 00:32:07,760
Speaker 1: that your neighbor is a is not a very sophisticated investor.

498
00:32:08,400 --> 00:32:13,080
Speaker 1: Introspection will tell us that we're not sophisticated investors. But

499
00:32:14,120 --> 00:32:18,160
Speaker 1: you've got to think about security markets. It's not democracy.

500
00:32:18,560 --> 00:32:22,200
Speaker 1: Not every investor gets the same votes. Rich investors have

501
00:32:22,240 --> 00:32:25,240
Speaker 1: a lot more votes, and they have a lot more resources,

502
00:32:25,240 --> 00:32:29,480
Speaker 1: They do a lot more research, and they presumably can

503
00:32:29,600 --> 00:32:34,840
Speaker 1: be more intelligent about trying to estimate risk. Nobody can

504
00:32:34,960 --> 00:32:39,200
Speaker 1: really estimate risk, because you know, it only manifests itself

505
00:32:39,240 --> 00:32:43,160
Speaker 1: in an outcome every day or a minute. But you know,

506
00:32:43,240 --> 00:32:46,880
Speaker 1: I think I would prefer to think of the market

507
00:32:47,800 --> 00:32:52,600
Speaker 1: as setting prices, taking into account as best one can

508
00:32:52,880 --> 00:32:57,280
Speaker 1: information about the uncertain future. There are some other aspects

509
00:32:57,480 --> 00:32:59,920
Speaker 1: that will probably talk about we don't even have to

510
00:33:00,040 --> 00:33:04,440
Speaker 1: think the markets that intelligent. But um, but no, I

511
00:33:04,440 --> 00:33:08,680
Speaker 1: mean when you go meet a real investor or introspect

512
00:33:08,720 --> 00:33:11,800
Speaker 1: on your own investment, you say, how can. But there's

513
00:33:11,800 --> 00:33:14,960
Speaker 1: also in this book I referenced the two thousand seven books.

514
00:33:15,200 --> 00:33:19,000
Speaker 1: I did a lot of simulations, and it's really fun.

515
00:33:19,160 --> 00:33:22,720
Speaker 1: You can simulate a world in which you have a

516
00:33:22,760 --> 00:33:26,040
Speaker 1: little bit of information. I have a little bit. None

517
00:33:26,040 --> 00:33:29,400
Speaker 1: of us really knows what we're doing, and yet magically

518
00:33:29,440 --> 00:33:33,240
Speaker 1: the prices end up incorporating all the information. I mean,

519
00:33:33,240 --> 00:33:36,120
Speaker 1: this is not a new finding. But what's fun is

520
00:33:36,160 --> 00:33:40,200
Speaker 1: to write just a smallish and you know, simulation program

521
00:33:40,240 --> 00:33:44,960
Speaker 1: and see how how remarkably efficient. Uh, it's the idea

522
00:33:45,000 --> 00:33:47,320
Speaker 1: that all of us is smarter than any one of us.

523
00:33:47,840 --> 00:33:52,240
Speaker 1: Collect The collective understands what's going on any single person.

524
00:33:52,400 --> 00:33:56,320
Speaker 1: And it's remarkable how easy it is to demonstrate the

525
00:33:56,400 --> 00:34:00,400
Speaker 1: power of that. Let's talk about your most recent product, jecked,

526
00:34:00,520 --> 00:34:03,880
Speaker 1: you started working on something a few years ago. Uh

527
00:34:04,000 --> 00:34:09,839
Speaker 1: that I I mispronounced rizz riz pat my term. Tell

528
00:34:10,239 --> 00:34:13,200
Speaker 1: us what rizmat is. Well, this is sort of I've

529
00:34:13,200 --> 00:34:16,920
Speaker 1: sort of moved. As we spoke about earlier, There was

530
00:34:16,960 --> 00:34:19,640
Speaker 1: a phase in my life in which I focused on

531
00:34:19,680 --> 00:34:24,400
Speaker 1: the problems of managers of large institutional funds, pensions, endowments,

532
00:34:24,880 --> 00:34:27,600
Speaker 1: and then when I went back to Stanford in the

533
00:34:27,640 --> 00:34:32,520
Speaker 1: early nineties, I started focusing four oh one case we're

534
00:34:32,520 --> 00:34:36,600
Speaker 1: coming into being, So I started focusing my research on

535
00:34:36,640 --> 00:34:39,680
Speaker 1: the problems of the individual investor trying to figure out

536
00:34:40,080 --> 00:34:43,480
Speaker 1: how to accumulate, how to invest in their for O

537
00:34:43,560 --> 00:34:47,040
Speaker 1: one K plans, let's call it uh, and then followed

538
00:34:47,040 --> 00:34:51,720
Speaker 1: that up with Financial Engines as a firm devoted again

539
00:34:52,200 --> 00:34:57,400
Speaker 1: at that point to the individual's accumulation phase. And for

540
00:34:57,440 --> 00:35:01,960
Speaker 1: the last few years I have been focusing pretty much

541
00:35:01,960 --> 00:35:07,959
Speaker 1: singlemindedly on the individual's decumulation phase, meaning how they draw

542
00:35:08,040 --> 00:35:11,200
Speaker 1: that money down over time. I mean, my prototype is

543
00:35:11,840 --> 00:35:16,239
Speaker 1: Bob and Sue Smith. She's sixty five, sixty seven, They've

544
00:35:16,280 --> 00:35:20,800
Speaker 1: started social Security, They've got some savings from rollover iras,

545
00:35:21,280 --> 00:35:24,799
Speaker 1: what have you? And uh, what do they do now?

546
00:35:26,200 --> 00:35:28,680
Speaker 1: How do they buy an annuity? If so, what do

547
00:35:28,800 --> 00:35:31,840
Speaker 1: they invest in mutual funds? If so, where it's do

548
00:35:31,960 --> 00:35:36,040
Speaker 1: they buy some other sophisticated financial product. If they do

549
00:35:36,080 --> 00:35:39,080
Speaker 1: their own investment, how do they invest how do they

550
00:35:39,200 --> 00:35:42,719
Speaker 1: decide how much to spend each year. Uh and so

551
00:35:42,880 --> 00:35:47,400
Speaker 1: this is trying to get my arms around many at

552
00:35:47,440 --> 00:35:51,279
Speaker 1: least of the problems and issues associated with that set

553
00:35:51,320 --> 00:35:55,800
Speaker 1: of decisions. And uh so the project involves an e

554
00:35:55,960 --> 00:36:00,080
Speaker 1: book um which is very large. It would be for

555
00:36:00,160 --> 00:36:04,560
Speaker 1: we're physical and suite of software. And it's all public domain,

556
00:36:05,040 --> 00:36:07,040
Speaker 1: will be when it's released. So you're gonna give this

557
00:36:07,080 --> 00:36:09,799
Speaker 1: away to whoever wants to use as I see it's

558
00:36:09,880 --> 00:36:15,120
Speaker 1: it's under a Creative Commons Attribution License number something and

559
00:36:15,440 --> 00:36:17,640
Speaker 1: meaning people can use it. They just can't resell it

560
00:36:17,680 --> 00:36:20,640
Speaker 1: for commercial benefit. They can do anything they want as

561
00:36:20,640 --> 00:36:24,160
Speaker 1: long as they spell my name right. So, so tell

562
00:36:24,200 --> 00:36:28,040
Speaker 1: me about what what motivated this? How did this come about?

563
00:36:28,840 --> 00:36:33,240
Speaker 1: You're in your early eighties. Theoretically you should be golfing

564
00:36:33,320 --> 00:36:36,319
Speaker 1: or fishing, but you're still deep at work in the

565
00:36:36,320 --> 00:36:41,759
Speaker 1: theory of financial asset pricing and management. What made you

566
00:36:41,840 --> 00:36:44,320
Speaker 1: say three or four years ago, I know I'll create

567
00:36:44,400 --> 00:36:49,399
Speaker 1: this giant project and give it away. Well, I don't

568
00:36:49,400 --> 00:36:52,040
Speaker 1: golf and I don't fish, but I could sure go

569
00:36:52,160 --> 00:36:55,400
Speaker 1: to more symphonies and operas and sail. I don't have

570
00:36:55,440 --> 00:36:57,520
Speaker 1: a sail boat anymore. I have a boat but go

571
00:36:57,560 --> 00:37:02,080
Speaker 1: out on the boat more often. UM. Well it uh,

572
00:37:02,239 --> 00:37:05,720
Speaker 1: it's kind of the same thing that motivated my last

573
00:37:06,239 --> 00:37:12,000
Speaker 1: two phases. Here's a really important problem. It's a problem

574
00:37:12,960 --> 00:37:17,040
Speaker 1: which is appealing because it affects you know, ordinary people

575
00:37:18,120 --> 00:37:23,000
Speaker 1: and uh, and it's really nasty. It's the nastiest, hardest

576
00:37:23,040 --> 00:37:25,000
Speaker 1: problem I've ever looked at. And I can't say I've

577
00:37:25,040 --> 00:37:28,480
Speaker 1: found some magic solution because I haven't. You're you're saying

578
00:37:28,520 --> 00:37:31,279
Speaker 1: this is this is harder than capital asset pricing. This

579
00:37:31,360 --> 00:37:34,760
Speaker 1: is harder than risk analysis. This is the hardest project

580
00:37:34,840 --> 00:37:38,960
Speaker 1: you've ever seen. It is for two reasons. One because

581
00:37:40,080 --> 00:37:42,560
Speaker 1: you can't just say, well, let's assume there is one

582
00:37:42,680 --> 00:37:46,200
Speaker 1: period left in the world, and you know, you have

583
00:37:46,239 --> 00:37:49,400
Speaker 1: to say there there are many years, it's continuing rolling

584
00:37:49,480 --> 00:37:52,000
Speaker 1: and you never So you've got a multi period problem,

585
00:37:52,239 --> 00:37:55,120
Speaker 1: which means you have to have a multi period pricing theory.

586
00:37:55,320 --> 00:37:57,000
Speaker 1: And you don't know how many periods there are going

587
00:37:57,040 --> 00:37:59,759
Speaker 1: to be, and you have the actualially shoes to deal with.

588
00:38:00,239 --> 00:38:03,440
Speaker 1: You don't know how long people can live. Um, and

589
00:38:03,520 --> 00:38:07,640
Speaker 1: so there are many, many issues. So it's a multidimensional

590
00:38:07,719 --> 00:38:11,520
Speaker 1: problem in some senses. Where we chose to treat the

591
00:38:11,560 --> 00:38:15,719
Speaker 1: others as a single dimension. And uh, so it's it's

592
00:38:16,160 --> 00:38:19,840
Speaker 1: good and juicy in terms of hard to do. Um

593
00:38:20,040 --> 00:38:23,000
Speaker 1: And uh, you can, as far as I can see,

594
00:38:23,040 --> 00:38:27,319
Speaker 1: you can only deal with it with computations. And I

595
00:38:27,320 --> 00:38:32,080
Speaker 1: write programs for fun. I love programming, so um and

596
00:38:32,120 --> 00:38:35,040
Speaker 1: it's it's important. So it had all the things that

597
00:38:35,440 --> 00:38:38,879
Speaker 1: you know turn me on as an economist. So so

598
00:38:39,000 --> 00:38:41,560
Speaker 1: what is you said? You're not too far away from

599
00:38:41,560 --> 00:38:45,200
Speaker 1: compleating this. This eventually goes on. By the time this broadcast,

600
00:38:45,280 --> 00:38:48,120
Speaker 1: it should be online, I would hope. So yeah, it'll

601
00:38:48,160 --> 00:38:51,279
Speaker 1: be probably first on my website at Stanford. We may

602
00:38:51,360 --> 00:38:54,600
Speaker 1: move it to another site at Stanford. But so what

603
00:38:54,600 --> 00:38:57,000
Speaker 1: was what did you learn doing this project? What's the

604
00:38:57,120 --> 00:39:01,520
Speaker 1: takeaway for how people should draw down? Because one of

605
00:39:01,520 --> 00:39:03,839
Speaker 1: the standard things we hear is well, you're gonna draw

606
00:39:03,880 --> 00:39:06,080
Speaker 1: a five to seven percent a year for the next

607
00:39:06,120 --> 00:39:09,160
Speaker 1: twenty years, and that's just such a rough rule of

608
00:39:09,200 --> 00:39:12,719
Speaker 1: Thumb's YEA, Well, if I may give you a little

609
00:39:12,760 --> 00:39:17,279
Speaker 1: bit of the structure. Uh, the project has the word

610
00:39:17,360 --> 00:39:23,719
Speaker 1: matrices in it, and the book has programs and matrix algebra.

611
00:39:23,840 --> 00:39:30,640
Speaker 1: It's only somebody in a financial engineering program would love this. Probably, Um,

612
00:39:30,800 --> 00:39:35,480
Speaker 1: but the idea is, think about a matrix and boy,

613
00:39:35,600 --> 00:39:38,040
Speaker 1: I think use the word table, spreadsheet, call it is

614
00:39:38,040 --> 00:39:41,880
Speaker 1: spreadheat and every row is a possible scenario for the

615
00:39:41,920 --> 00:39:45,760
Speaker 1: next fifty years. And there are a bunch of rows.

616
00:39:45,800 --> 00:39:48,319
Speaker 1: In fact, there are a hundred thousand rows, because there

617
00:39:48,320 --> 00:39:50,200
Speaker 1: are a lot of things that could happen to have

618
00:39:50,200 --> 00:39:56,000
Speaker 1: a hundred thousand different scenarios, and each column is a year. Okay,

619
00:39:56,160 --> 00:40:00,360
Speaker 1: so you've got that let's call it spread sheet. But

620
00:40:00,400 --> 00:40:03,719
Speaker 1: you've got a lot of these spreadsheets. So, for example,

621
00:40:03,760 --> 00:40:09,760
Speaker 1: there's one spreadsheet that's built out of actuarial tables that

622
00:40:09,760 --> 00:40:13,880
Speaker 1: that basically says, okay, in this scenario, Bob and Sue

623
00:40:13,920 --> 00:40:18,759
Speaker 1: my protagonists or whomever you want, determine who there are,

624
00:40:18,840 --> 00:40:21,640
Speaker 1: how old they are, they live, both of them live

625
00:40:21,719 --> 00:40:24,720
Speaker 1: for the first three years, then Bob dies, so lives

626
00:40:24,760 --> 00:40:27,640
Speaker 1: five more years, then Sue dies, and then what's left

627
00:40:27,640 --> 00:40:30,279
Speaker 1: goes to the estate. So that's the sort of what

628
00:40:30,320 --> 00:40:32,800
Speaker 1: I call personal states matrix. So you have a hundred

629
00:40:32,800 --> 00:40:36,040
Speaker 1: thousand different things that could have you know, in terms

630
00:40:36,040 --> 00:40:39,839
Speaker 1: of mortality, let's call it. Then you have another one

631
00:40:39,840 --> 00:40:42,840
Speaker 1: of those spreadsheets for what happens to the returns on

632
00:40:42,840 --> 00:40:47,560
Speaker 1: the market portfolio, which in my version is a world

633
00:40:47,640 --> 00:40:52,279
Speaker 1: bond and stock portfolio index fund low cost. So each

634
00:40:52,280 --> 00:40:54,520
Speaker 1: of those is this year it did eight percent, the

635
00:40:54,560 --> 00:40:56,919
Speaker 1: next year it did twelve percent, the next year it lost.

636
00:40:58,080 --> 00:41:01,360
Speaker 1: So you have a hundred thousand different story for the market.

637
00:41:01,840 --> 00:41:05,960
Speaker 1: You've got another one for inflation, hundred thousand different inflation stories,

638
00:41:06,960 --> 00:41:10,920
Speaker 1: another one for what happens to tips Treasury protected securities.

639
00:41:11,160 --> 00:41:16,440
Speaker 1: Those are my two investments. And then you say, and

640
00:41:16,480 --> 00:41:19,000
Speaker 1: then you've got Bob and Sue, or you've got one

641
00:41:19,040 --> 00:41:23,320
Speaker 1: for socialists. Then the other sort of fill up with incomes.

642
00:41:24,000 --> 00:41:26,759
Speaker 1: So in this scenario, in this year, how much do

643
00:41:26,840 --> 00:41:30,680
Speaker 1: Bob and Sue get from social Security? And there's another one,

644
00:41:30,840 --> 00:41:33,319
Speaker 1: so you got a whole bunch of those. Another one

645
00:41:33,360 --> 00:41:36,400
Speaker 1: on how much do they get from Let's take the

646
00:41:36,440 --> 00:41:40,520
Speaker 1: strategy you alluded to so call four percent rule. Put

647
00:41:40,560 --> 00:41:45,200
Speaker 1: your money in whatever investments, take out four percent the

648
00:41:45,239 --> 00:41:49,239
Speaker 1: first year. Every year, keep taking out an amount with

649
00:41:49,280 --> 00:41:52,080
Speaker 1: the same purchasing power as what you took out initially

650
00:41:52,719 --> 00:41:54,960
Speaker 1: until you either die or run out of money, and

651
00:41:55,040 --> 00:41:58,640
Speaker 1: good luck to you. UM and I and a couple

652
00:41:58,640 --> 00:42:01,760
Speaker 1: of my colleagues at financi Engines have written about that rule.

653
00:42:02,280 --> 00:42:04,640
Speaker 1: It's it's not the worst possible rule, but it's right

654
00:42:04,719 --> 00:42:08,479
Speaker 1: up there. It's it's just a simple rule of thumb

655
00:42:08,520 --> 00:42:12,719
Speaker 1: that people use but clearly subopticate precisely. But you know,

656
00:42:12,800 --> 00:42:15,919
Speaker 1: do I have Can I say I have an optimizer

657
00:42:15,960 --> 00:42:18,160
Speaker 1: that will tell you the optimal rule? No, I do not,

658
00:42:19,120 --> 00:42:22,960
Speaker 1: um nor does anybody else. If you, if you were

659
00:42:22,960 --> 00:42:28,960
Speaker 1: to give me multidimensional utility functions, don't ask multi dimension

660
00:42:29,040 --> 00:42:32,080
Speaker 1: utility functions. Okay, So here is the utility of income

661
00:42:32,120 --> 00:42:35,480
Speaker 1: for me in next year, and then here's another one

662
00:42:36,120 --> 00:42:39,800
Speaker 1: the following year. Then in principle I might be able

663
00:42:39,840 --> 00:42:44,120
Speaker 1: to give you an optimal strategy, but nobody does. Nobody

664
00:42:44,160 --> 00:42:47,880
Speaker 1: has those utility functions. What I can do is infer

665
00:42:48,000 --> 00:42:51,000
Speaker 1: I said, look, if you choose this strategy or this

666
00:42:51,080 --> 00:42:54,800
Speaker 1: combination of strategies, then I can tell you, first of all,

667
00:42:54,920 --> 00:42:58,719
Speaker 1: it's not efficient. You can do better. Or if it

668
00:42:58,840 --> 00:43:01,840
Speaker 1: is efficient, I can say, well, you're acting as if

669
00:43:01,880 --> 00:43:05,279
Speaker 1: these were your utility functions, and you could perhaps look

670
00:43:05,320 --> 00:43:09,160
Speaker 1: at those and work backwards and right. So let let

671
00:43:09,160 --> 00:43:11,520
Speaker 1: me make sure I understand what we have. So you

672
00:43:11,640 --> 00:43:16,160
Speaker 1: have a variety of scenarios of longevity and mortality and

673
00:43:16,239 --> 00:43:22,600
Speaker 1: all the variations there too, various market returns, various inflation returns,

674
00:43:22,680 --> 00:43:27,120
Speaker 1: various tips returns, and you have hundreds thousands of each

675
00:43:27,120 --> 00:43:30,239
Speaker 1: of these. And now you combine all these and you

676
00:43:30,360 --> 00:43:35,439
Speaker 1: end up, aside from the extraordinary number crunching, with a

677
00:43:35,600 --> 00:43:41,760
Speaker 1: huge assortment of possible outcomes for possible scenarios, and almost

678
00:43:41,800 --> 00:43:46,440
Speaker 1: like an exponential Monte Carlo simulation. It is moontcano. I

679
00:43:46,440 --> 00:43:48,799
Speaker 1: don't like to use that term U and so so

680
00:43:48,920 --> 00:43:53,320
Speaker 1: what's the takeaway of that for the investor? Okay, first

681
00:43:53,320 --> 00:43:55,600
Speaker 1: of all, let me say if I were teaching, I

682
00:43:55,640 --> 00:43:58,520
Speaker 1: wish you were it would be in my class, because

683
00:43:59,360 --> 00:44:01,880
Speaker 1: you know that that's that you've a quick learner. But

684
00:44:01,960 --> 00:44:06,480
Speaker 1: we knew that you happen to I'm familiar with your

685
00:44:06,640 --> 00:44:11,279
Speaker 1: entire body of work, and it is what you're describing.

686
00:44:11,360 --> 00:44:14,320
Speaker 1: I had a few moments to think about beforehand, so

687
00:44:14,520 --> 00:44:17,680
Speaker 1: when we discussed this previously, So as much as I

688
00:44:17,719 --> 00:44:23,600
Speaker 1: appreciate that the it's on you not make the the

689
00:44:23,600 --> 00:44:28,480
Speaker 1: the um the intellectual firepowers on that side of the table,

690
00:44:28,480 --> 00:44:33,720
Speaker 1: we won't. We will argue offline. But there's a whole

691
00:44:33,920 --> 00:44:37,279
Speaker 1: series of analytic routines which you can apply. Once you've

692
00:44:37,320 --> 00:44:40,719
Speaker 1: done this for a particular strategy or set of strategies,

693
00:44:40,760 --> 00:44:45,959
Speaker 1: you could add them together. And so, for example, I've

694
00:44:46,000 --> 00:44:50,319
Speaker 1: talked about multidimensional probability distribution. What's the range of things

695
00:44:50,320 --> 00:44:52,719
Speaker 1: that I could incomes I could get next year, what's

696
00:44:52,719 --> 00:44:55,359
Speaker 1: the range of the following year. Well, there're at least

697
00:44:55,360 --> 00:44:59,440
Speaker 1: two ways to show that. One is you show one

698
00:44:59,520 --> 00:45:02,560
Speaker 1: distribute Susan and I have a particular pet way to

699
00:45:02,800 --> 00:45:05,520
Speaker 1: show it that I think individuals can relate to better.

700
00:45:06,400 --> 00:45:09,000
Speaker 1: And then you it's an animated graph. You show one

701
00:45:09,080 --> 00:45:10,919
Speaker 1: and then the next comes up, and then the next.

702
00:45:11,719 --> 00:45:15,360
Speaker 1: And another way is what's called an income map, where

703
00:45:15,400 --> 00:45:18,960
Speaker 1: you're sort of like looking down from the sky on

704
00:45:18,719 --> 00:45:24,400
Speaker 1: a on a terrain three dimensional exactly. And I have

705
00:45:24,440 --> 00:45:28,560
Speaker 1: a bunch of analytic tools and in the software you

706
00:45:28,560 --> 00:45:30,960
Speaker 1: can just say, well, let's try this one with that

707
00:45:31,040 --> 00:45:33,480
Speaker 1: and that and that, and you can say, well, let's

708
00:45:33,480 --> 00:45:36,440
Speaker 1: look at what happens if they're both alive, separately from

709
00:45:36,480 --> 00:45:39,759
Speaker 1: what happens if one is alive. Because with ensuring annuities

710
00:45:39,840 --> 00:45:43,200
Speaker 1: you have different payouts. With social security, you have different payouts.

711
00:45:43,840 --> 00:45:48,960
Speaker 1: So you can you can do diagnostics, you can do

712
00:45:49,040 --> 00:45:52,240
Speaker 1: as I say, and for well, this would be optimal

713
00:45:52,320 --> 00:45:55,759
Speaker 1: for somebody with a utility function like this, or this

714
00:45:55,880 --> 00:45:59,640
Speaker 1: is suboptimal. You can get the same probability distributions cheaper

715
00:46:00,320 --> 00:46:03,920
Speaker 1: if you do it more efficiently, so I can diagnose that.

716
00:46:04,800 --> 00:46:13,400
Speaker 1: Um so, so this sounds very sophisticated and complex. Well sophisticated, yes, complex.

717
00:46:13,480 --> 00:46:16,600
Speaker 1: Unfortunately on the website, is this going to be easy

718
00:46:16,640 --> 00:46:20,200
Speaker 1: for the average person or or adviser to plug into

719
00:46:20,239 --> 00:46:22,839
Speaker 1: this and say, here's so I can figure out what

720
00:46:22,880 --> 00:46:26,400
Speaker 1: I should be drawing down each year. I don't think so. No,

721
00:46:26,920 --> 00:46:32,320
Speaker 1: um what I'm hoping I mentioned financial engineers. There are programs,

722
00:46:32,360 --> 00:46:36,439
Speaker 1: and there are a lot of them, typically master's programs,

723
00:46:36,480 --> 00:46:40,840
Speaker 1: sometimes an engineering or math or sometimes economics, sometimes business

724
00:46:40,840 --> 00:46:46,000
Speaker 1: schools for financial engineers. Though, and these people, for example,

725
00:46:47,600 --> 00:46:50,400
Speaker 1: you know this may sound You mentioned something that about

726
00:46:50,480 --> 00:46:53,919
Speaker 1: run time, where you run time on one of these

727
00:46:53,960 --> 00:46:57,839
Speaker 1: really complex analyzes with all these scenarios can be under

728
00:46:57,840 --> 00:47:01,400
Speaker 1: a minute, sometimes well under a minute, because it's programmed

729
00:47:01,400 --> 00:47:05,200
Speaker 1: in a language which is designed for matrix operations, matt

730
00:47:05,280 --> 00:47:08,360
Speaker 1: Lab from Math works. And it turns out in almost

731
00:47:08,400 --> 00:47:11,160
Speaker 1: all of these programs, most of the students on their

732
00:47:11,200 --> 00:47:15,680
Speaker 1: resumes say they know Matt Lab, so the programming aspect

733
00:47:15,760 --> 00:47:19,719
Speaker 1: isn't going to frighten them. And the majority of them,

734
00:47:19,760 --> 00:47:22,279
Speaker 1: as far as I can tell with the breakdown, the

735
00:47:22,320 --> 00:47:26,040
Speaker 1: majority of graduates of those programs go into unit you

736
00:47:26,080 --> 00:47:29,560
Speaker 1: guessed at Wall Street creating connovatives. Not a single one

737
00:47:29,600 --> 00:47:33,000
Speaker 1: that I could find in the summary went into working

738
00:47:33,000 --> 00:47:37,720
Speaker 1: with a financial advisor who's working with retirees or near retirees.

739
00:47:38,000 --> 00:47:42,200
Speaker 1: But I would hope that this would be in some

740
00:47:42,239 --> 00:47:46,640
Speaker 1: sort of electives in those programs, and or that good

741
00:47:46,640 --> 00:47:50,560
Speaker 1: technical people would be able to go through my material,

742
00:47:51,520 --> 00:47:54,360
Speaker 1: go through the programs, learn how to use them, and

743
00:47:54,480 --> 00:47:59,560
Speaker 1: then provide the back office for say a financial advisor.

744
00:47:59,600 --> 00:48:02,680
Speaker 1: And that's the reason I was meeting with this UH

745
00:48:03,120 --> 00:48:07,840
Speaker 1: person yesterday. She's a single person and she doesn't advise

746
00:48:07,880 --> 00:48:10,759
Speaker 1: any retirees, so it wouldn't work. But I'm trying to

747
00:48:10,800 --> 00:48:14,040
Speaker 1: find and I have a friend who does advise retirees

748
00:48:14,080 --> 00:48:16,319
Speaker 1: and I'm trying to see if I can get him

749
00:48:16,440 --> 00:48:19,840
Speaker 1: to incorporate that in his practice. So, but it sounds

750
00:48:19,920 --> 00:48:23,880
Speaker 1: like the way you've built this, you want universities and

751
00:48:24,000 --> 00:48:27,880
Speaker 1: graduate level programs. I know Columbia has a School of

752
00:48:27,920 --> 00:48:36,279
Speaker 1: Financial Engineering within them runs it. You want, you want

753
00:48:36,320 --> 00:48:38,600
Speaker 1: these folks to build upon what you've done, and I would.

754
00:48:38,880 --> 00:48:41,440
Speaker 1: I would like there to be an elective on retirement income,

755
00:48:42,200 --> 00:48:45,520
Speaker 1: and there currently isn't. There's no such um I have

756
00:48:45,600 --> 00:48:47,919
Speaker 1: not done an exhaustive survey, but I'm willing to bet

757
00:48:47,960 --> 00:48:50,440
Speaker 1: there is not because it sounds like, I mean, there

758
00:48:50,440 --> 00:48:55,160
Speaker 1: maybe one on accumulation, but decumulation. It sounds like it's

759
00:48:55,200 --> 00:48:59,240
Speaker 1: the sort of problem that's ready made for somebody's PhD

760
00:48:59,280 --> 00:49:03,239
Speaker 1: dissertation or is it too complex for that. Well, I'm

761
00:49:03,280 --> 00:49:09,239
Speaker 1: not sure you know every your standards for PhD dissertation.

762
00:49:09,280 --> 00:49:12,320
Speaker 1: I'm not sure that there would be a lot new.

763
00:49:13,200 --> 00:49:15,239
Speaker 1: I mean you can certainly propose new. I mean I

764
00:49:15,320 --> 00:49:18,480
Speaker 1: in this I have techniques that nobody's ever used. I

765
00:49:18,560 --> 00:49:22,640
Speaker 1: have constructs that nobody's ever implemented. So there are things

766
00:49:22,640 --> 00:49:25,800
Speaker 1: in there that that could give rise to new financial

767
00:49:25,880 --> 00:49:30,239
Speaker 1: products and investment and insurance products. Um. So I don't

768
00:49:30,280 --> 00:49:34,680
Speaker 1: know about a PhD dissertation. I'm thinking more M S

769
00:49:34,840 --> 00:49:39,200
Speaker 1: M a financial engineering level. Uh, it's certainly not NBA

770
00:49:39,480 --> 00:49:42,080
Speaker 1: level material and people will be able to find this

771
00:49:42,280 --> 00:49:47,520
Speaker 1: at Stanford dot edge you slash. It'll be originally my website. Uh,

772
00:49:47,560 --> 00:49:49,920
Speaker 1: if you just go online and say w F sharp

773
00:49:50,000 --> 00:49:53,319
Speaker 1: or something, you'll find it and um and then as

774
00:49:53,360 --> 00:49:55,680
Speaker 1: I say we may, it may get a website of

775
00:49:55,680 --> 00:49:58,600
Speaker 1: its own at Stanford. What could your future hold more

776
00:49:58,600 --> 00:50:00,759
Speaker 1: than you think because it merely we work with you

777
00:50:00,800 --> 00:50:03,680
Speaker 1: to create a strategy built around your priorities. Visit mL

778
00:50:03,719 --> 00:50:06,040
Speaker 1: dot com and learn more about Merrill Lynch. An affiliated

779
00:50:06,080 --> 00:50:08,520
Speaker 1: Bank of America, Mary Lynch makes available products and services

780
00:50:08,520 --> 00:50:10,800
Speaker 1: offered by Merrill Lynch Pierce Federan Smith Incorporated or registered

781
00:50:10,800 --> 00:50:14,120
Speaker 1: broker dealer. Remember s I pc UM. Let's talk about

782
00:50:14,120 --> 00:50:17,080
Speaker 1: Financial Engines for a moment. A prior guest was Jeff

783
00:50:17,160 --> 00:50:20,760
Speaker 1: Magian Calda. He was a CEO, you were the chairman

784
00:50:20,840 --> 00:50:25,680
Speaker 1: from to two thousand and three and the co founder. Uh.

785
00:50:25,800 --> 00:50:28,680
Speaker 1: Financial Engines are one of those companies that the average

786
00:50:28,719 --> 00:50:31,960
Speaker 1: person walking down the street has probably never heard of.

787
00:50:32,080 --> 00:50:35,200
Speaker 1: But it's a publicly traded company. I think they manage

788
00:50:35,200 --> 00:50:37,520
Speaker 1: about a hundred and twenty or a hundred and forty

789
00:50:37,560 --> 00:50:41,640
Speaker 1: billion dollars these days. Tell us about Financial Engines and

790
00:50:41,680 --> 00:50:46,000
Speaker 1: how the idea came about? Well, listen, you know everybody

791
00:50:46,040 --> 00:50:50,040
Speaker 1: has a founding story and they obviously get better as

792
00:50:50,640 --> 00:50:53,160
Speaker 1: they're told more often. I thank you so much for

793
00:50:53,200 --> 00:50:55,839
Speaker 1: saying that, because my wife gives me grief all the time.

794
00:50:56,239 --> 00:50:59,440
Speaker 1: How come your stories don't sound anything like the honey

795
00:50:59,440 --> 00:51:03,120
Speaker 1: it's called to get over time, that's it's it's your

796
00:51:03,200 --> 00:51:06,200
Speaker 1: working on you. So so I'll try to I'll try

797
00:51:06,200 --> 00:51:09,000
Speaker 1: to give it to you as I believe it happened.

798
00:51:09,800 --> 00:51:13,200
Speaker 1: I had As I mentioned earlier, I had a phase

799
00:51:13,280 --> 00:51:17,719
Speaker 1: when I had a research slash consulting firm trying to

800
00:51:17,760 --> 00:51:22,680
Speaker 1: help people managing large pension and endowment funds. And after

801
00:51:22,719 --> 00:51:26,520
Speaker 1: I went back to teaching full time, I decided that

802
00:51:26,640 --> 00:51:30,000
Speaker 1: four O one case were for good or ill the

803
00:51:30,000 --> 00:51:33,120
Speaker 1: wave of the future, and there are a whole lot

804
00:51:33,200 --> 00:51:36,239
Speaker 1: of people who needed help. And this is the risk

805
00:51:36,280 --> 00:51:39,439
Speaker 1: of laws past in seventy four or so. When when

806
00:51:39,520 --> 00:51:41,839
Speaker 1: were you coming to the realization that have these four

807
00:51:41,840 --> 00:51:45,720
Speaker 1: own k things are problematic for so many people. Probably

808
00:51:46,040 --> 00:51:48,680
Speaker 1: pretty much in the early nineties. And I went back

809
00:51:48,680 --> 00:51:53,080
Speaker 1: to Stanford, so I had time to work on anything

810
00:51:53,120 --> 00:51:56,040
Speaker 1: I wanted to and in terms of my research, so

811
00:51:56,120 --> 00:52:00,440
Speaker 1: I focused my research on that problem and I was

812
00:52:01,040 --> 00:52:06,000
Speaker 1: writing pieces. I had an early Internet account, back before

813
00:52:06,560 --> 00:52:09,200
Speaker 1: most people knew what it was I was writing little

814
00:52:09,239 --> 00:52:11,520
Speaker 1: programs to put on the Internet for people to use.

815
00:52:12,400 --> 00:52:15,160
Speaker 1: And uh, a friend of mine, Joe Grunfest, the professor

816
00:52:15,160 --> 00:52:17,960
Speaker 1: of the law school at Stanford, said let's have coffee.

817
00:52:17,960 --> 00:52:21,440
Speaker 1: I've got an idea. So we did, and he said,

818
00:52:21,680 --> 00:52:24,800
Speaker 1: you know you're not gonna affect enough people with this work.

819
00:52:26,000 --> 00:52:29,239
Speaker 1: We need to start a firm. And I said, been there,

820
00:52:29,360 --> 00:52:33,680
Speaker 1: done that, No, thanks very much, Hell yeah, tell you what,

821
00:52:33,760 --> 00:52:36,520
Speaker 1: Let's just at least talk to my friend Craig Johnson.

822
00:52:36,560 --> 00:52:39,439
Speaker 1: And Craig had a firm that came he came out

823
00:52:39,440 --> 00:52:43,880
Speaker 1: of the legal side, but they had developed a practice

824
00:52:43,920 --> 00:52:50,440
Speaker 1: specializing in helping people bring ideas to fruition via startups,

825
00:52:51,120 --> 00:52:56,120
Speaker 1: in particular academics ideas. And so Craig and Joe and

826
00:52:56,160 --> 00:52:59,359
Speaker 1: I talked about, well, let's see if we can't set

827
00:52:59,440 --> 00:53:03,400
Speaker 1: up a firm to provide financial advice. Two people in

828
00:53:03,520 --> 00:53:08,359
Speaker 1: four oh one k plans through their employer. So this

829
00:53:08,440 --> 00:53:11,719
Speaker 1: was very much accumulation phase too, use the term I've

830
00:53:11,800 --> 00:53:15,880
Speaker 1: used before, and uh, more or less the rest is history.

831
00:53:17,360 --> 00:53:21,040
Speaker 1: You mentioned Jeff Imagine called Joe had had some contact

832
00:53:21,080 --> 00:53:23,960
Speaker 1: with Jeff and said, I think Jeff would be great

833
00:53:24,000 --> 00:53:27,920
Speaker 1: to lead this effort and UH, so we talked to

834
00:53:28,040 --> 00:53:32,360
Speaker 1: Jeff and I remember I think it was Craig said, well, Jeff,

835
00:53:32,400 --> 00:53:36,239
Speaker 1: I hope you understand that in a year we might

836
00:53:36,280 --> 00:53:39,440
Speaker 1: replace you, you know, as the way these things happened.

837
00:53:39,480 --> 00:53:43,440
Speaker 1: And Jeff said, I can take that chance. So we

838
00:53:43,480 --> 00:53:49,000
Speaker 1: started with Jeff, and then Craig uh brought Ian part time,

839
00:53:49,880 --> 00:53:56,040
Speaker 1: really experienced CFO people, head of engineering to help us

840
00:53:56,080 --> 00:53:59,280
Speaker 1: get started and to help us find people to hire.

841
00:54:00,160 --> 00:54:04,560
Speaker 1: Jeff went out beating the bushes to get venture capital. UM.

842
00:54:04,600 --> 00:54:07,000
Speaker 1: I went along on one or two of those presentations,

843
00:54:07,320 --> 00:54:11,920
Speaker 1: decided it was too brutal for me. But and so

844
00:54:12,120 --> 00:54:14,560
Speaker 1: now that's that's how it all came about. And now

845
00:54:14,640 --> 00:54:20,040
Speaker 1: Financial Engine So they eventually pivot towards managing on an

846
00:54:20,040 --> 00:54:25,480
Speaker 1: institutional basis, and so they're the UM provider of record

847
00:54:25,600 --> 00:54:30,279
Speaker 1: for various companies, substantial companies. UH and it's fairly low

848
00:54:30,320 --> 00:54:37,040
Speaker 1: cost and it's fairly well structured indexes, primarily for corporate

849
00:54:37,040 --> 00:54:39,399
Speaker 1: for a one K plan. Let me say first, I've

850
00:54:39,400 --> 00:54:41,719
Speaker 1: been retired from the firm for quite a while, so

851
00:54:41,760 --> 00:54:45,760
Speaker 1: I don't really know much about what they're doing now. UM,

852
00:54:45,800 --> 00:54:51,120
Speaker 1: but basically we actually went through I think depending on

853
00:54:51,160 --> 00:54:56,640
Speaker 1: how you count four or five business plans um and

854
00:54:56,680 --> 00:54:59,520
Speaker 1: there was we at one point we're on the A

855
00:54:59,640 --> 00:55:01,560
Speaker 1: O L side, we were going to do it directed

856
00:55:02,040 --> 00:55:05,439
Speaker 1: to retail, to call it B two C and all

857
00:55:05,440 --> 00:55:09,480
Speaker 1: the rest of that um. But what we settled down

858
00:55:10,480 --> 00:55:14,959
Speaker 1: providing advice to all the employees in a firm once

859
00:55:14,960 --> 00:55:20,080
Speaker 1: the firm signed up, and then providing management of accounts

860
00:55:20,120 --> 00:55:24,600
Speaker 1: to a subset of the employees who wanted that. And

861
00:55:25,560 --> 00:55:29,160
Speaker 1: certainly my goal, uh and I think that of almost

862
00:55:29,160 --> 00:55:33,080
Speaker 1: everybody in the firm from the start, and I hope

863
00:55:33,120 --> 00:55:37,080
Speaker 1: still is to do it as low enough costs actually

864
00:55:37,560 --> 00:55:41,000
Speaker 1: keep bread on the table and paint a little more

865
00:55:41,040 --> 00:55:45,480
Speaker 1: than bread. And well it's worth well because they've they've

866
00:55:45,520 --> 00:55:50,000
Speaker 1: accumulated a substantial amount of clients and assets, and people

867
00:55:50,040 --> 00:55:52,840
Speaker 1: generally seem to be happy with and and you know,

868
00:55:52,920 --> 00:55:54,920
Speaker 1: we we tried to bring to bear. The whole idea

869
00:55:55,040 --> 00:55:58,120
Speaker 1: was that was what I'd done it my former incarnations

870
00:55:59,680 --> 00:56:04,840
Speaker 1: try to bring financial economics, let's call it broadly, to

871
00:56:05,000 --> 00:56:08,480
Speaker 1: bear on that problem. You know, what we knew, what

872
00:56:08,600 --> 00:56:12,560
Speaker 1: we thought we knew about markets, index funds being very

873
00:56:12,640 --> 00:56:18,439
Speaker 1: attractive investments, et cetera. Trying to help the accumulator, let's

874
00:56:18,480 --> 00:56:22,440
Speaker 1: call it, get some sense of the risk return trade

875
00:56:22,480 --> 00:56:27,279
Speaker 1: offs in terms of if we do this portfolio, then

876
00:56:27,320 --> 00:56:29,680
Speaker 1: the range of things that happened in terms of the

877
00:56:29,719 --> 00:56:31,879
Speaker 1: amount of money would have to buy an annuity. Let's

878
00:56:31,880 --> 00:56:35,279
Speaker 1: say as an example, that retirement is this. If we

879
00:56:35,360 --> 00:56:38,440
Speaker 1: do that, it's that. And trying to give them a

880
00:56:38,560 --> 00:56:42,880
Speaker 1: chance to experiment and find something that makes sense for

881
00:56:42,960 --> 00:56:46,919
Speaker 1: their situation. And and and I we did a lot

882
00:56:46,960 --> 00:56:50,440
Speaker 1: of over the years. Certainly I was involved affirm, did

883
00:56:50,440 --> 00:56:53,399
Speaker 1: a lot of research and some of my early work

884
00:56:53,440 --> 00:56:58,719
Speaker 1: on the decumulation phase was done with Jason Scott and

885
00:56:58,920 --> 00:57:05,040
Speaker 1: John Watson, UH, two PhDs in the research at the firm.

886
00:57:05,080 --> 00:57:08,720
Speaker 1: So you referenced index funds, you worked on the first

887
00:57:08,760 --> 00:57:12,520
Speaker 1: index funds or certainly one of the first index was

888
00:57:12,600 --> 00:57:17,960
Speaker 1: going to say so, so, given given how the world

889
00:57:18,000 --> 00:57:21,560
Speaker 1: has changed, tell us a little bit about what you

890
00:57:21,600 --> 00:57:24,240
Speaker 1: did back then, and then we could fast forward and

891
00:57:24,600 --> 00:57:29,640
Speaker 1: talk about whether index funds are going to eat the world? Okay, Um, certainly, UM,

892
00:57:30,800 --> 00:57:37,000
Speaker 1: the you know I was, I was. I become friends

893
00:57:37,000 --> 00:57:42,400
Speaker 1: with Bill Files at at Wells Fargo Investment Advisors, UM,

894
00:57:42,440 --> 00:57:46,360
Speaker 1: and he had talked to my class. Uh and I

895
00:57:46,400 --> 00:57:49,000
Speaker 1: had of course been pushing the idea of index funds

896
00:57:49,120 --> 00:57:52,760
Speaker 1: or something equivalent, and I had a call out of

897
00:57:52,760 --> 00:57:55,520
Speaker 1: the blue from a young man who had just finished

898
00:57:55,520 --> 00:58:00,200
Speaker 1: an NBA program at Chicago, Chicago, and said, we look,

899
00:58:00,240 --> 00:58:03,600
Speaker 1: you know, I think I've got this right. My my

900
00:58:03,680 --> 00:58:10,440
Speaker 1: dad run owns run Sampson I luggage company, and they

901
00:58:10,480 --> 00:58:12,280
Speaker 1: have to find a manager for the pension fund I

902
00:58:12,280 --> 00:58:15,920
Speaker 1: believe it was. And I learned about the capital asset

903
00:58:16,000 --> 00:58:18,120
Speaker 1: pricing and adel and all, and it seemed to me

904
00:58:18,200 --> 00:58:22,440
Speaker 1: that made sense to just put this in the market somehow.

905
00:58:23,720 --> 00:58:25,520
Speaker 1: And he said, do you know anybody who can do that?

906
00:58:26,400 --> 00:58:28,280
Speaker 1: And I said, well, so I put him in touch

907
00:58:28,320 --> 00:58:33,400
Speaker 1: with Bill Fauss that Wells Fargo, and they had come

908
00:58:33,480 --> 00:58:37,400
Speaker 1: up originally with a scheme in which they had maybe

909
00:58:37,400 --> 00:58:41,920
Speaker 1: five hundred stocks, but they were equal weighted, not in

910
00:58:42,040 --> 00:58:46,200
Speaker 1: market cap weights, which had I known about it, I

911
00:58:46,200 --> 00:58:48,400
Speaker 1: would have told him instantly was a really dumb idea.

912
00:58:48,480 --> 00:58:50,960
Speaker 1: But why do you why do you say that that's interesting? Well,

913
00:58:51,000 --> 00:58:53,680
Speaker 1: because in the first place, it's not representative the market,

914
00:58:53,720 --> 00:58:56,920
Speaker 1: it's not consistent with the capital asser pricing, right. It

915
00:58:56,960 --> 00:59:00,360
Speaker 1: involves all kinds of turnment over to balance everything short

916
00:59:01,120 --> 00:59:04,440
Speaker 1: um and uh as opposed to doing an annual semi

917
00:59:04,480 --> 00:59:08,840
Speaker 1: annual rebalance. Yeah or not. Well, with a market based portfolio,

918
00:59:09,240 --> 00:59:12,400
Speaker 1: you only rebalance for new issues and things of that sort.

919
00:59:12,600 --> 00:59:17,200
Speaker 1: So um, you know, if it's broad enough. So fortunately

920
00:59:17,360 --> 00:59:21,000
Speaker 1: that idea that somebody all was far ago figured that out,

921
00:59:21,880 --> 00:59:25,520
Speaker 1: and so I believe that was their first implementation. Now

922
00:59:26,000 --> 00:59:28,720
Speaker 1: you mentioned the first, there was work going on at

923
00:59:28,720 --> 00:59:31,640
Speaker 1: a bank in Chicago. I think Jack Trayner was involved

924
00:59:31,640 --> 00:59:35,360
Speaker 1: in that. And um there was also a venture that

925
00:59:35,400 --> 00:59:38,400
Speaker 1: I was supposed to be on the board of the

926
00:59:38,400 --> 00:59:43,120
Speaker 1: Teamsters Union wanted to do an index fund and we

927
00:59:43,120 --> 00:59:47,040
Speaker 1: were gonna establish but that fell through for reasons having

928
00:59:47,080 --> 00:59:51,440
Speaker 1: to do with the Teamsters Union in San Francisco. So um,

929
00:59:51,920 --> 00:59:55,720
Speaker 1: so I think Wells was, if not the first, certainly

930
00:59:55,760 --> 01:00:00,320
Speaker 1: one of the very first institutional index funds. Jack Vogel

931
01:00:00,360 --> 01:00:03,000
Speaker 1: of course came along on the on the well I

932
01:00:03,000 --> 01:00:05,480
Speaker 1: would call it personal side rather than institution. What it

933
01:00:05,520 --> 01:00:08,400
Speaker 1: was a mutual fund at the time, but it was

934
01:00:08,440 --> 01:00:11,120
Speaker 1: certainly an idea that was that was in the in

935
01:00:11,160 --> 01:00:15,640
Speaker 1: the ether because of the academic work. And so what

936
01:00:15,680 --> 01:00:17,880
Speaker 1: did you do with the Wells Fargo? Did you help

937
01:00:17,920 --> 01:00:20,760
Speaker 1: them put that together or was it just and by

938
01:00:20,760 --> 01:00:23,760
Speaker 1: the way, who was the PhD from Chicago with Samsonite?

939
01:00:23,760 --> 01:00:25,480
Speaker 1: Do you remember? The name was actually an Nba and

940
01:00:25,520 --> 01:00:27,880
Speaker 1: I don't remember his name, and I apologize for that.

941
01:00:28,680 --> 01:00:31,600
Speaker 1: Could have been Samsonite, I don't know. I've heard, but

942
01:00:31,680 --> 01:00:35,960
Speaker 1: I've heard the name sam Unite from other people telling

943
01:00:35,960 --> 01:00:37,920
Speaker 1: the story, and I don't remember if it was David

944
01:00:37,960 --> 01:00:41,280
Speaker 1: Booth or someone else. You know, the person who caught

945
01:00:41,280 --> 01:00:43,400
Speaker 1: me was actually the son I believe of the owner

946
01:00:43,520 --> 01:00:47,880
Speaker 1: founder of Samsonite, where he who is classy, had taken

947
01:00:47,920 --> 01:00:50,920
Speaker 1: I don't know that Chicago could have been Jane Farma

948
01:00:51,080 --> 01:00:56,400
Speaker 1: but um, but in any event, um, I'm sorry to

949
01:00:56,400 --> 01:01:00,360
Speaker 1: go back to the question. We were so so from

950
01:01:00,360 --> 01:01:03,880
Speaker 1: making the introduction to Wells Fargo, what what else was

951
01:01:03,920 --> 01:01:06,840
Speaker 1: your role in the development of that initial index fund

952
01:01:07,160 --> 01:01:09,760
Speaker 1: moving them towards market cap waiting. Is that one of

953
01:01:09,800 --> 01:01:12,400
Speaker 1: the contributions you had or did they find it on

954
01:01:12,440 --> 01:01:15,760
Speaker 1: their own right? Well, they I think found that in

955
01:01:15,800 --> 01:01:18,520
Speaker 1: that particular instance. I don't believe I was consulting with them.

956
01:01:18,640 --> 01:01:23,400
Speaker 1: Then I did consult subsequently for quite a while, and uh,

957
01:01:24,040 --> 01:01:27,840
Speaker 1: we did all sorts of things. I remember. Um, we

958
01:01:27,960 --> 01:01:31,400
Speaker 1: developed a yield tilt fund. And there's an argument that

959
01:01:31,440 --> 01:01:33,520
Speaker 1: could be made, and I made it in my textbook,

960
01:01:33,560 --> 01:01:38,960
Speaker 1: and my colleague Bob Blitzenberger and Kristen Ramaswamy, then one

961
01:01:39,000 --> 01:01:42,000
Speaker 1: of our PhD students, did quite a bit of work

962
01:01:42,080 --> 01:01:46,440
Speaker 1: on this that you know, if you have differential taxation

963
01:01:46,480 --> 01:01:49,440
Speaker 1: of dividends and gains, as we did, and at the

964
01:01:49,480 --> 01:01:53,840
Speaker 1: time the differential was big um, then you can imagine

965
01:01:53,880 --> 01:01:58,200
Speaker 1: a sort of a sorting out where it pays individuals

966
01:01:58,360 --> 01:02:03,400
Speaker 1: to and non acxible entities to tilt away from market

967
01:02:03,440 --> 01:02:09,280
Speaker 1: proportions towards higher yield because you don't pay taxes and

968
01:02:09,320 --> 01:02:14,400
Speaker 1: their price presumably to reflect their inferior tax position, and

969
01:02:14,480 --> 01:02:17,600
Speaker 1: for people who pay taxes to tilt in the other direction.

970
01:02:18,040 --> 01:02:20,800
Speaker 1: So you can make that argument. And there were academic

971
01:02:20,800 --> 01:02:25,080
Speaker 1: papers and then papers from Miller and Myron Scholes saying

972
01:02:26,080 --> 01:02:28,520
Speaker 1: that's not true or the evidence doesn't support it. And

973
01:02:28,600 --> 01:02:31,480
Speaker 1: even so we brought to market this yield tilt fund.

974
01:02:31,480 --> 01:02:35,120
Speaker 1: It was an institutional fund that had a dividend tilt,

975
01:02:35,560 --> 01:02:39,120
Speaker 1: if you will, because of the tax differential. Yeah, that

976
01:02:39,240 --> 01:02:41,480
Speaker 1: was it was. It was an equilibrium argument in a

977
01:02:41,720 --> 01:02:45,680
Speaker 1: society with differential taxations. So so, really, do I get

978
01:02:45,680 --> 01:02:50,240
Speaker 1: to credit you for creating smart data before any No,

979
01:02:50,360 --> 01:02:53,440
Speaker 1: we'll talk about that separately, but but I will tell

980
01:02:53,480 --> 01:02:58,560
Speaker 1: you the dividend tilt came out, and high yield stocks

981
01:02:58,920 --> 01:03:02,160
Speaker 1: just relative to other stocks, just went into a tailspin.

982
01:03:02,640 --> 01:03:05,440
Speaker 1: I mean, it was one of those periods when I

983
01:03:05,560 --> 01:03:08,320
Speaker 1: call them value. We'd call them value stocks now maybe,

984
01:03:08,680 --> 01:03:12,080
Speaker 1: but they just got creamed by growth stocks for whatever reason.

985
01:03:12,720 --> 01:03:15,520
Speaker 1: And the client we didn't have many clients, and the

986
01:03:15,560 --> 01:03:19,520
Speaker 1: clients we had sort of started departing. Finally, somebody turned

987
01:03:19,560 --> 01:03:21,240
Speaker 1: out the light when they closed the door, and the

988
01:03:21,320 --> 01:03:26,280
Speaker 1: yield tilt TONU funds did not last very long. Um,

989
01:03:26,320 --> 01:03:28,480
Speaker 1: but I suppose that was one of the first institutional

990
01:03:28,560 --> 01:03:31,480
Speaker 1: quote value funds. So so what years is? Are we

991
01:03:31,560 --> 01:03:37,240
Speaker 1: talking seventies or eighties? We're talking I I think I'm

992
01:03:37,280 --> 01:03:39,560
Speaker 1: guessing eighties, but don't early eighties, but don't hold me

993
01:03:39,600 --> 01:03:42,080
Speaker 1: to that. So with that point, technology were starting to

994
01:03:42,160 --> 01:03:46,120
Speaker 1: real nobody really wants to look at value. Yeah. I

995
01:03:46,400 --> 01:03:50,240
Speaker 1: actually some while ago, for some other reason, I looked

996
01:03:50,240 --> 01:03:52,320
Speaker 1: that up and I couldn't find any traces of it

997
01:03:52,680 --> 01:03:55,560
Speaker 1: on the internet, so it was buried. But do you

998
01:03:55,560 --> 01:03:58,280
Speaker 1: want to talk smart d sure. Let's let's let's talk

999
01:03:58,320 --> 01:04:05,440
Speaker 1: about the idea of creating indicas by using methodologies other

1000
01:04:05,520 --> 01:04:10,200
Speaker 1: than market capitalization. First, I think I've been I'm in

1001
01:04:10,240 --> 01:04:14,160
Speaker 1: print somewhere from saying this in public. The term smart

1002
01:04:14,160 --> 01:04:18,640
Speaker 1: beta makes me sick. I mean beta is it's We

1003
01:04:18,800 --> 01:04:22,040
Speaker 1: defined it in finance for decades as a measure of

1004
01:04:22,080 --> 01:04:26,000
Speaker 1: the extent to which a stock or something moves with

1005
01:04:26,120 --> 01:04:29,640
Speaker 1: the market. That's the definition. Whether it's smarter dumb is

1006
01:04:29,640 --> 01:04:33,840
Speaker 1: a relevant. Now. What all that is is what we've

1007
01:04:33,880 --> 01:04:37,080
Speaker 1: known about and written about for years called factor tilts.

1008
01:04:37,600 --> 01:04:40,800
Speaker 1: So you have a multi factor model of Fama, French

1009
01:04:41,040 --> 01:04:47,760
Speaker 1: and everything value. So there's a factor model, and you tilt.

1010
01:04:48,200 --> 01:04:51,920
Speaker 1: You hold more exposure to yield, let's say, or to

1011
01:04:52,120 --> 01:04:56,960
Speaker 1: value than to growth. You hold more exposure to small

1012
01:04:57,280 --> 01:05:01,960
Speaker 1: relative to large, and so all of those arguments say,

1013
01:05:02,440 --> 01:05:05,320
Speaker 1: you know, there are two classes of argument for those strategies.

1014
01:05:05,360 --> 01:05:09,480
Speaker 1: One is the market screws up, you know, and you

1015
01:05:09,520 --> 01:05:14,400
Speaker 1: know there are dumb investors who think growth stocks are

1016
01:05:14,440 --> 01:05:19,120
Speaker 1: so wonderful they overpriced them. And there are smart investors

1017
01:05:19,160 --> 01:05:23,280
Speaker 1: who know that and underweight those growth stocks. And then

1018
01:05:23,320 --> 01:05:26,680
Speaker 1: there are there are there's myself and my friends who

1019
01:05:26,680 --> 01:05:31,720
Speaker 1: are in the middle, you know, and meaning a balanced portfolio.

1020
01:05:32,080 --> 01:05:35,120
Speaker 1: By it all that the market doesn't screw up, and

1021
01:05:35,120 --> 01:05:39,720
Speaker 1: if it does, you'll never figure out how. And so

1022
01:05:39,720 --> 01:05:42,720
Speaker 1: so basically that argument, and this gets to a very

1023
01:05:42,760 --> 01:05:47,200
Speaker 1: simple argument I wrote about years ago. Um, if you

1024
01:05:47,280 --> 01:05:51,520
Speaker 1: take all the people who own shares in the US market,

1025
01:05:51,600 --> 01:05:55,760
Speaker 1: let's say put them in one room, and you say,

1026
01:05:55,800 --> 01:06:00,520
Speaker 1: here are the indexers broad based market index you know,

1027
01:06:00,600 --> 01:06:04,720
Speaker 1: before costs the market. If the market does ten percent,

1028
01:06:05,200 --> 01:06:08,160
Speaker 1: all every one of them will do ten percent. The

1029
01:06:08,160 --> 01:06:10,120
Speaker 1: rest of the room, the active managers. I don't care

1030
01:06:10,120 --> 01:06:13,920
Speaker 1: if they're smart beta yield till you know whatever, some

1031
01:06:14,040 --> 01:06:17,760
Speaker 1: of them will do better. Someone will do. They have

1032
01:06:17,840 --> 01:06:21,160
Speaker 1: to earn before costs the same that the other guys do,

1033
01:06:21,920 --> 01:06:24,840
Speaker 1: and after costs they're gonna earn less. Now that doesn't

1034
01:06:24,880 --> 01:06:27,760
Speaker 1: mean that some of them may not routinely do better

1035
01:06:27,880 --> 01:06:32,360
Speaker 1: than the indexers. But if so, somebody's routinely doing worse.

1036
01:06:32,840 --> 01:06:36,120
Speaker 1: So there's you know, that that story they're smart, that's

1037
01:06:36,160 --> 01:06:40,760
Speaker 1: the smart beta. There's you know, sort of dumb, that's

1038
01:06:40,920 --> 01:06:44,160
Speaker 1: we indexers. And then there's the really dumb you know,

1039
01:06:44,200 --> 01:06:46,160
Speaker 1: who are the people on the other side. Of the

1040
01:06:46,200 --> 01:06:49,960
Speaker 1: trades with the smart beta people. And of course one

1041
01:06:50,040 --> 01:06:53,640
Speaker 1: presumes eventually that don't really dumb people will say I

1042
01:06:53,640 --> 01:06:55,640
Speaker 1: think maybe I'll buy an index fund, and then the

1043
01:06:55,680 --> 01:06:59,720
Speaker 1: game's over for smart PA. But you know those factors, yes,

1044
01:07:00,000 --> 01:07:04,000
Speaker 1: do we know? We know that there are extended periods

1045
01:07:04,000 --> 01:07:07,480
Speaker 1: when value beats both, and they're extended periods when growth

1046
01:07:07,520 --> 01:07:11,560
Speaker 1: beats value um. And if you are oppression and can

1047
01:07:11,680 --> 01:07:15,760
Speaker 1: tell which which is coming, then tilt away and have

1048
01:07:15,800 --> 01:07:18,520
Speaker 1: a good time and you'll be you'll be very wealthy.

1049
01:07:18,640 --> 01:07:21,600
Speaker 1: There's very little evidence that people can tell in advance

1050
01:07:21,640 --> 01:07:25,640
Speaker 1: what's coming next, and over time it's pretty much averages

1051
01:07:25,680 --> 01:07:30,400
Speaker 1: out um. And but we've got a lot of data,

1052
01:07:30,520 --> 01:07:33,720
Speaker 1: we've got really fast computers, we've got a lot of

1053
01:07:33,840 --> 01:07:36,320
Speaker 1: smart people. We've got a lot of good marketing people.

1054
01:07:36,920 --> 01:07:40,720
Speaker 1: So you're gonna be hearing about this. And my friend

1055
01:07:40,760 --> 01:07:43,680
Speaker 1: Bill Files that I mentioned earlier, one said he never

1056
01:07:43,720 --> 01:07:47,080
Speaker 1: met a back test. He didn't like, you know, somebody

1057
01:07:47,080 --> 01:07:49,240
Speaker 1: will come along. If we had just done this the

1058
01:07:49,320 --> 01:07:53,120
Speaker 1: last ten years, you would be so rich. So someone

1059
01:07:53,200 --> 01:07:56,320
Speaker 1: wants I don't know who who it was described it

1060
01:07:56,360 --> 01:08:02,760
Speaker 1: as smart marketing. Yes, that so I just one caveat.

1061
01:08:02,960 --> 01:08:06,600
Speaker 1: There's a very subtle argument which a friend of mine,

1062
01:08:06,800 --> 01:08:09,280
Speaker 1: I won't bother you with his name. It's an academic

1063
01:08:09,280 --> 01:08:11,880
Speaker 1: who works in the industry, you know, has made that.

1064
01:08:12,440 --> 01:08:16,320
Speaker 1: My arithmetic argument which I referenced, which was arithmetic of

1065
01:08:16,360 --> 01:08:20,960
Speaker 1: active management was the title of the piece. Um. Well,

1066
01:08:21,000 --> 01:08:25,799
Speaker 1: but we smart institutions know when to buy a new issue.

1067
01:08:26,160 --> 01:08:30,480
Speaker 1: You know, there are issues coming and going, bond repurchases,

1068
01:08:31,120 --> 01:08:35,519
Speaker 1: expirations of bondsman maturation, what have you. And some of

1069
01:08:35,600 --> 01:08:40,600
Speaker 1: us active managers can play that game and other indexers

1070
01:08:40,720 --> 01:08:44,479
Speaker 1: can't do it. So maybe and that's the claim for

1071
01:08:44,479 --> 01:08:46,920
Speaker 1: how they managed to outperform. Yeah, and there's and there's

1072
01:08:46,960 --> 01:08:52,160
Speaker 1: and there's another argument that you sometimes hear, well, the

1073
01:08:52,240 --> 01:08:56,320
Speaker 1: smart active managers do better than the market, and yes,

1074
01:08:56,400 --> 01:08:58,519
Speaker 1: somebody has to be doing worse, and it's the dumb

1075
01:08:58,520 --> 01:09:03,240
Speaker 1: individual investors. Um and and yet when we look at

1076
01:09:03,320 --> 01:09:07,640
Speaker 1: the league tables for how well active mutual fund managers

1077
01:09:07,640 --> 01:09:12,920
Speaker 1: have done in a good year, the beat their benchmark.

1078
01:09:13,439 --> 01:09:17,559
Speaker 1: But in most years, right, it's always somebody different from

1079
01:09:17,640 --> 01:09:20,840
Speaker 1: year to year. There the people who wait a minute,

1080
01:09:20,920 --> 01:09:23,519
Speaker 1: I know about Warren Buffett, and I know, but you

1081
01:09:23,560 --> 01:09:27,720
Speaker 1: hear about the outliers, but their outliers and the vast majority.

1082
01:09:28,520 --> 01:09:31,439
Speaker 1: Van Goard has done a ton of studies. Yeah, yeah,

1083
01:09:31,479 --> 01:09:33,439
Speaker 1: you know. If I bet you know this. If I

1084
01:09:33,520 --> 01:09:36,000
Speaker 1: bet with you on a coin flip, and I always

1085
01:09:36,000 --> 01:09:39,559
Speaker 1: call heads, there will be some periods of ten ten

1086
01:09:39,640 --> 01:09:41,639
Speaker 1: in a row, I won't win them all, but I'll

1087
01:09:41,640 --> 01:09:44,760
Speaker 1: win more than half. And that's when you go open

1088
01:09:44,760 --> 01:09:47,960
Speaker 1: a hedge fund. That's exactly that, and then when I lose,

1089
01:09:48,000 --> 01:09:49,760
Speaker 1: I close it and I opened a new hitge fund.

1090
01:09:50,200 --> 01:09:54,760
Speaker 1: We know how that works. It's amazing that the academic

1091
01:09:54,880 --> 01:09:58,759
Speaker 1: literature on this is pretty unambiguous. People can debate around

1092
01:09:58,840 --> 01:10:03,599
Speaker 1: the fringes, but the concept of now there's a whole

1093
01:10:03,640 --> 01:10:07,760
Speaker 1: behavioral side of it, and people um Meyer Stateman at

1094
01:10:07,800 --> 01:10:10,840
Speaker 1: Santa Clara talks about people have an expressive need with

1095
01:10:10,880 --> 01:10:15,120
Speaker 1: their portfolio. It's not purely utilitarian. What do I need

1096
01:10:15,160 --> 01:10:18,000
Speaker 1: to do with my By the way, his mayor's work,

1097
01:10:18,160 --> 01:10:20,519
Speaker 1: you know, I haven't seen him for a while, but

1098
01:10:20,840 --> 01:10:23,759
Speaker 1: he's a very smart guy, and I his work is excellent,

1099
01:10:23,920 --> 01:10:28,720
Speaker 1: and and his mentor was Peter Bernstein. So to bring

1100
01:10:28,720 --> 01:10:31,599
Speaker 1: that back full circle, I didn't realize it was that close.

1101
01:10:31,640 --> 01:10:35,320
Speaker 1: I know they were, they were close. So so we

1102
01:10:35,320 --> 01:10:38,840
Speaker 1: we mentioned the consulting with Wells Fargo. You also consulted

1103
01:10:38,880 --> 01:10:42,680
Speaker 1: with Merrill Lynch in the nineties seventies. These are are

1104
01:10:42,960 --> 01:10:46,519
Speaker 1: companies that have gone through enormous changes over the passage decades.

1105
01:10:47,040 --> 01:10:50,599
Speaker 1: Are you surprised how how that side of the business

1106
01:10:50,640 --> 01:10:55,160
Speaker 1: has evolved. These big firms are not what they once were.

1107
01:10:56,080 --> 01:10:57,680
Speaker 1: How do you how do you see that? Well, let

1108
01:10:57,720 --> 01:11:02,040
Speaker 1: me differentiate a little. The Meryl work with Jack Trayner

1109
01:11:02,120 --> 01:11:07,160
Speaker 1: and Gil Hammer and others was we were basically providing

1110
01:11:07,400 --> 01:11:13,320
Speaker 1: services for institutional money managers like pension funds, etcetera. We

1111
01:11:13,400 --> 01:11:15,400
Speaker 1: did the first beta book where you could look up

1112
01:11:15,479 --> 01:11:18,720
Speaker 1: the beta of a stock. We did the some of

1113
01:11:18,760 --> 01:11:24,040
Speaker 1: the first performance measurement and analytic performance measurement. So this

1114
01:11:24,160 --> 01:11:29,799
Speaker 1: was Maryland's providing this service to large clients of theirs. UM.

1115
01:11:29,840 --> 01:11:33,240
Speaker 1: So that was all on the performance measurement, if you

1116
01:11:33,360 --> 01:11:35,720
Speaker 1: will side. And it's not like today where anyone could

1117
01:11:35,760 --> 01:11:39,840
Speaker 1: log into a Bloomberg terminal conscious number. Back then it

1118
01:11:39,960 --> 01:11:42,519
Speaker 1: was serious computer power in order to do that, and

1119
01:11:42,560 --> 01:11:44,640
Speaker 1: nobody had to get it on Google, Yahoo. Do you

1120
01:11:44,760 --> 01:11:49,599
Speaker 1: name it UM and UM and then UM. The Wells work,

1121
01:11:49,680 --> 01:11:52,439
Speaker 1: they were money managers, so it was index funds and

1122
01:11:52,479 --> 01:11:55,160
Speaker 1: things of that sort. So so they're very different gigs,

1123
01:11:55,200 --> 01:11:57,679
Speaker 1: if you will. So let's talk a little bit about

1124
01:11:57,680 --> 01:12:00,800
Speaker 1: the sharp ratio, which is something that comes up frequently.

1125
01:12:01,320 --> 01:12:04,000
Speaker 1: I hear that from people all the time. But what's

1126
01:12:04,040 --> 01:12:08,559
Speaker 1: the fund sharp ratio? You've written that the sharp ratio

1127
01:12:08,680 --> 01:12:11,320
Speaker 1: has been misused by a lot of the investing public.

1128
01:12:11,400 --> 01:12:15,200
Speaker 1: So let's start with explain to us exactly what the

1129
01:12:15,360 --> 01:12:19,479
Speaker 1: sharp ratio is. First thing, I'm not as emaniacal as

1130
01:12:19,520 --> 01:12:22,040
Speaker 1: you might imagine. I called it, and I still think

1131
01:12:22,040 --> 01:12:26,840
Speaker 1: it's a better term reward to variability ratio, reward to variability,

1132
01:12:26,840 --> 01:12:30,040
Speaker 1: and I'll expand on that. Somebody else, and I don't

1133
01:12:30,439 --> 01:12:33,120
Speaker 1: really know exactly who it was, started calling at the

1134
01:12:33,120 --> 01:12:36,080
Speaker 1: sharp ratio, and the name stuck so so well, it

1135
01:12:36,160 --> 01:12:39,439
Speaker 1: certainly rolls off. It's uneasier than rewards a variability ratio.

1136
01:12:39,479 --> 01:12:42,840
Speaker 1: And I can tell you my wife's an artist, so

1137
01:12:43,479 --> 01:12:46,360
Speaker 1: she's not deep into finance. Let us say, and we're

1138
01:12:46,360 --> 01:12:49,320
Speaker 1: watching a sitcom, or no, it's not a sitcom, it's

1139
01:12:49,320 --> 01:12:53,560
Speaker 1: a drama billions, which is sure hitch fund manager, etcetera,

1140
01:12:53,960 --> 01:12:57,320
Speaker 1: which is sort of my guilty pleasure that that show,

1141
01:12:57,479 --> 01:12:59,960
Speaker 1: and they're sitting at the table and one of the

1142
01:13:00,080 --> 01:13:02,040
Speaker 1: people that's beend and saying, well, you know, we're losing

1143
01:13:02,080 --> 01:13:05,479
Speaker 1: customers because our sharp ratio is low. And my wife said,

1144
01:13:05,640 --> 01:13:10,559
Speaker 1: what yes, yes, So so I'm glad they didn't say

1145
01:13:10,600 --> 01:13:16,080
Speaker 1: reward to variability. Um. They might claim to fame. Um.

1146
01:13:16,200 --> 01:13:19,680
Speaker 1: The it's kind of the ant Well, let me go

1147
01:13:19,720 --> 01:13:23,200
Speaker 1: back the original context and a parallels some work of

1148
01:13:23,280 --> 01:13:26,760
Speaker 1: Jack Trayner's, which Jack went in a different direction. But

1149
01:13:26,920 --> 01:13:30,960
Speaker 1: the idea was, how do you evaluate X an ex

1150
01:13:31,040 --> 01:13:35,040
Speaker 1: post or anticipate X andy, but let's talk about ex

1151
01:13:35,080 --> 01:13:39,120
Speaker 1: post how well you've done. And the idea was, and

1152
01:13:39,479 --> 01:13:42,880
Speaker 1: I won't speak for Jack, my idea was to say, well,

1153
01:13:43,080 --> 01:13:47,200
Speaker 1: the expected return if we're looking forward, or the average

1154
01:13:47,240 --> 01:13:51,599
Speaker 1: return if we're looking backward, is a measure of goodness.

1155
01:13:51,680 --> 01:13:55,320
Speaker 1: That's a good thing, but there's also an issue. What

1156
01:13:55,400 --> 01:13:59,960
Speaker 1: was the journey like? So over the place very bill

1157
01:14:01,200 --> 01:14:04,120
Speaker 1: and and so the idea was, what did you get

1158
01:14:04,120 --> 01:14:07,520
Speaker 1: an expected return per unit of risk that you took

1159
01:14:07,880 --> 01:14:12,200
Speaker 1: or will take if it's forward looking. And my original

1160
01:14:12,400 --> 01:14:16,120
Speaker 1: setting was this is for a whole portfolio. And so

1161
01:14:16,160 --> 01:14:21,960
Speaker 1: the idea was you compare your situation with treasury bills.

1162
01:14:22,040 --> 01:14:25,800
Speaker 1: Let's call it the riskless asset. So in the numerator

1163
01:14:25,920 --> 01:14:28,839
Speaker 1: the top of the fraction you put my average return

1164
01:14:28,960 --> 01:14:32,240
Speaker 1: over the treasury bill, how much did I earn over

1165
01:14:32,360 --> 01:14:35,120
Speaker 1: for taking risk? And in the bottom you put how

1166
01:14:35,160 --> 01:14:38,160
Speaker 1: much risk did I take? And the idea is the

1167
01:14:38,200 --> 01:14:42,720
Speaker 1: more return you've got, the more reward per unit of variability,

1168
01:14:42,880 --> 01:14:47,120
Speaker 1: the better it was, and more reward relative to variability,

1169
01:14:48,320 --> 01:14:51,080
Speaker 1: and and you know, and so we'll talk about another

1170
01:14:51,120 --> 01:14:54,559
Speaker 1: issue with it, but stun the face of it, if

1171
01:14:54,600 --> 01:14:57,599
Speaker 1: you had to choose one number to evaluate some an

1172
01:14:57,680 --> 01:15:03,680
Speaker 1: investment prospectively or you know, after the fact um retrospectively,

1173
01:15:04,240 --> 01:15:05,960
Speaker 1: then you know it's not a bad number. That's a

1174
01:15:05,960 --> 01:15:08,880
Speaker 1: pretty good number. We've got computers now, we don't need

1175
01:15:08,960 --> 01:15:14,040
Speaker 1: to restrict ourselves to one in number. Um So, so

1176
01:15:14,200 --> 01:15:17,720
Speaker 1: then you go to, well, what if this is not

1177
01:15:17,880 --> 01:15:20,880
Speaker 1: my whole portfolio but a piece of it. If it's

1178
01:15:20,920 --> 01:15:22,920
Speaker 1: one fund and I've got twenty, or if it's one

1179
01:15:22,960 --> 01:15:27,080
Speaker 1: investment manager my pension fund, I've got a hundred, and

1180
01:15:27,800 --> 01:15:33,080
Speaker 1: this is not the right measure for that. Uh So,

1181
01:15:33,160 --> 01:15:36,400
Speaker 1: how do people how are people misusing it? Well, so

1182
01:15:36,400 --> 01:15:39,000
Speaker 1: so let me just finish that thought. So what you

1183
01:15:39,120 --> 01:15:42,680
Speaker 1: can do is come up with a benchmark. So this

1184
01:15:42,760 --> 01:15:46,320
Speaker 1: is a growth manager, I'll get a growth index fund

1185
01:15:46,360 --> 01:15:49,679
Speaker 1: as a benchmark, and in the numeraator I'll put on average,

1186
01:15:49,720 --> 01:15:54,120
Speaker 1: how did this fund do minus how the index fund did,

1187
01:15:55,240 --> 01:15:58,799
Speaker 1: and in the bottom I'll put the variability between the two.

1188
01:15:59,560 --> 01:16:03,280
Speaker 1: You know, there it's the variability of the difference. But again,

1189
01:16:03,360 --> 01:16:07,439
Speaker 1: so that's another measure nobody's ever kind of So that's alpha,

1190
01:16:07,640 --> 01:16:11,760
Speaker 1: the differential versus over over residual risk. Let's call it

1191
01:16:12,120 --> 01:16:15,240
Speaker 1: something like that. And again there are variations on that theme.

1192
01:16:15,760 --> 01:16:18,600
Speaker 1: Nobody's ever given a name to those kinds of measures.

1193
01:16:19,200 --> 01:16:22,120
Speaker 1: But again, but the only case in which for a

1194
01:16:22,200 --> 01:16:28,360
Speaker 1: single manager in a multi managed portfolio the sharp ratio

1195
01:16:29,120 --> 01:16:34,160
Speaker 1: maybe applicable is in a hedge fund that has zero beta.

1196
01:16:34,520 --> 01:16:37,880
Speaker 1: Has zero beta. Now, the other measure was Jack Trainer used.

1197
01:16:39,160 --> 01:16:41,280
Speaker 1: It was the same in the numerator, but the dominator

1198
01:16:41,320 --> 01:16:45,439
Speaker 1: had beta as opposed to as opposed to total risk,

1199
01:16:45,560 --> 01:16:47,880
Speaker 1: so it had in effect the part of the risk

1200
01:16:47,920 --> 01:16:50,240
Speaker 1: that's due to the market. And and there are some

1201
01:16:50,360 --> 01:16:55,280
Speaker 1: arguments in terms of capital asset pricing that that can

1202
01:16:55,320 --> 01:16:58,160
Speaker 1: be helpful, But in terms of just you say, look,

1203
01:16:58,200 --> 01:17:02,000
Speaker 1: here's my whole portfolio in the last twenty years, my

1204
01:17:02,160 --> 01:17:07,040
Speaker 1: average expercent and the standard deviation was why and the

1205
01:17:07,080 --> 01:17:10,679
Speaker 1: Treasury bill was z. You know, what do you think?

1206
01:17:11,040 --> 01:17:14,000
Speaker 1: And and I can compare that, say with investing in

1207
01:17:14,800 --> 01:17:18,880
Speaker 1: let's say a total stock market fund, if that's your comparison,

1208
01:17:19,520 --> 01:17:22,840
Speaker 1: and say which sharp ratio is higher? So how are

1209
01:17:22,880 --> 01:17:26,439
Speaker 1: people abusing the ratio? Let me count? Let me count

1210
01:17:26,479 --> 01:17:31,000
Speaker 1: the ways, because because I always see it in hedge funds,

1211
01:17:31,040 --> 01:17:33,320
Speaker 1: I always see it in back tests, I always see it.

1212
01:17:33,920 --> 01:17:36,920
Speaker 1: In fact, there are some people who hedge funds inhangements

1213
01:17:37,120 --> 01:17:41,719
Speaker 1: because if they're really truly hedged dent heads almost none are,

1214
01:17:41,840 --> 01:17:44,599
Speaker 1: of course none are. Most of them have some beta

1215
01:17:44,640 --> 01:17:48,040
Speaker 1: relative the stocks and some data relative the bonds, so

1216
01:17:48,240 --> 01:17:50,920
Speaker 1: you need to do a little regression analysis and do

1217
01:17:51,000 --> 01:17:55,240
Speaker 1: something more sophisticated. So the sharp ratio just oversimplifies what

1218
01:17:55,400 --> 01:17:58,240
Speaker 1: the risk of a hedge fund an unhedged hedge fund

1219
01:17:58,280 --> 01:18:03,400
Speaker 1: actually is. Yes, it's it's it's amazing because of all

1220
01:18:03,520 --> 01:18:08,240
Speaker 1: the metrics we see, it's the one that it seems

1221
01:18:08,280 --> 01:18:10,640
Speaker 1: to be the first question. It's easy. I mean, you know,

1222
01:18:10,880 --> 01:18:14,360
Speaker 1: it's it's easy, and uh, you know it's not without

1223
01:18:14,400 --> 01:18:18,719
Speaker 1: information because it has a good thing in the numerator

1224
01:18:18,760 --> 01:18:21,960
Speaker 1: and has a bad thing in the denominator. But you know,

1225
01:18:22,040 --> 01:18:24,439
Speaker 1: it's just not as sophisticated as it should be in

1226
01:18:24,479 --> 01:18:28,040
Speaker 1: a lot of applications. So so given all of that,

1227
01:18:28,439 --> 01:18:35,080
Speaker 1: how should the average investor think about risk adjusted returns? Um?

1228
01:18:35,320 --> 01:18:39,040
Speaker 1: Maybe not think about it? Do we do? We do?

1229
01:18:39,080 --> 01:18:42,680
Speaker 1: You are you suggesting we overemphasize risk adjusted returns? Well? Yeah,

1230
01:18:42,720 --> 01:18:45,320
Speaker 1: I think the average investor should all broad very broad

1231
01:18:45,360 --> 01:18:49,000
Speaker 1: based index funds to begin with. And you should think

1232
01:18:49,040 --> 01:18:55,800
Speaker 1: about both retrospectively and prospectively what on average you might

1233
01:18:55,840 --> 01:18:59,120
Speaker 1: get from this or did get, and how much variation

1234
01:18:59,160 --> 01:19:02,200
Speaker 1: there was, because that tells you something about how bad

1235
01:19:02,200 --> 01:19:05,960
Speaker 1: it could be the two together. Um, And so for

1236
01:19:06,439 --> 01:19:09,720
Speaker 1: that kind of a strategy of sharp ratio is not irrelevant.

1237
01:19:09,760 --> 01:19:13,200
Speaker 1: It's worth looking at. But don't you know, don't break

1238
01:19:13,240 --> 01:19:17,080
Speaker 1: it down into pieces, just take the whole thing. Quite

1239
01:19:17,320 --> 01:19:20,080
Speaker 1: quite fascinating. I mean, my if you want to know

1240
01:19:20,120 --> 01:19:25,200
Speaker 1: what you know, we mentioned my current work, UM, my

1241
01:19:26,439 --> 01:19:36,479
Speaker 1: ideal portfolio risky portfolio for an individual retiree. Certainly, UM,

1242
01:19:36,520 --> 01:19:40,160
Speaker 1: who isn't desirous of taking a whole lot of risk

1243
01:19:41,560 --> 01:19:47,880
Speaker 1: is a portfolio maybe four different index funds, US non

1244
01:19:48,000 --> 01:19:51,880
Speaker 1: US bonds and stocks in more or less market cap proportions,

1245
01:19:51,880 --> 01:19:55,000
Speaker 1: a global bond stock portfolio. So now that's interesting. You

1246
01:19:55,040 --> 01:19:58,960
Speaker 1: mentioned in market cap proportions because there are so many

1247
01:19:59,400 --> 01:20:01,960
Speaker 1: I want to miss I understand you correctly. There are

1248
01:20:02,040 --> 01:20:04,599
Speaker 1: so many more bonds. The bond market is so much

1249
01:20:04,640 --> 01:20:07,320
Speaker 1: bigger than the stock market, don't Yes and no if

1250
01:20:07,360 --> 01:20:11,799
Speaker 1: you look worldwide, huh, it's I don't have the current figures.

1251
01:20:11,840 --> 01:20:17,759
Speaker 1: I should have them. Um, but it's somewhere around bonds

1252
01:20:18,960 --> 01:20:21,720
Speaker 1: and um. So that would be thought of as a

1253
01:20:21,800 --> 01:20:25,639
Speaker 1: fairly risk averse portfolio. Well again, as as I mentioned,

1254
01:20:25,640 --> 01:20:31,320
Speaker 1: I'm focusing on retirees, and very few retirees seem to

1255
01:20:31,320 --> 01:20:34,640
Speaker 1: have the stomach for much more risk than that. And

1256
01:20:34,680 --> 01:20:37,479
Speaker 1: then I would mix that for those who want less

1257
01:20:37,600 --> 01:20:42,960
Speaker 1: risk with My preferred vehicle would be tips, because you know,

1258
01:20:44,479 --> 01:20:47,040
Speaker 1: is a real danger for a retiree. That's the biggest

1259
01:20:47,160 --> 01:20:50,840
Speaker 1: risk is that's there. They have two investments, one of

1260
01:20:50,840 --> 01:20:55,000
Speaker 1: which may require one of which the world bond stock portfolio.

1261
01:20:55,439 --> 01:20:57,600
Speaker 1: I continue to badge your my friends at some of

1262
01:20:57,600 --> 01:21:00,920
Speaker 1: the index fund companies to create it a single fund.

1263
01:21:01,640 --> 01:21:03,439
Speaker 1: So I don't have to do it with four different

1264
01:21:04,400 --> 01:21:07,960
Speaker 1: So when you're looking world stuck, you're doing emerging market

1265
01:21:08,040 --> 01:21:11,240
Speaker 1: developed x US and then what else. Well, that gets

1266
01:21:11,280 --> 01:21:15,639
Speaker 1: into the the nuances of indexing and prices and such.

1267
01:21:15,720 --> 01:21:18,960
Speaker 1: But yeah, I mean you certainly mean the funds that

1268
01:21:19,040 --> 01:21:22,320
Speaker 1: I happen to use our Vanguard funds, and we can't

1269
01:21:22,320 --> 01:21:25,120
Speaker 1: get Jack Boglar Bill McNab to create an index for you.

1270
01:21:25,200 --> 01:21:28,080
Speaker 1: I haven't I haven't seen Jack or Bill for a while,

1271
01:21:28,160 --> 01:21:31,040
Speaker 1: but I've been dealing with some of the others, including

1272
01:21:31,200 --> 01:21:34,960
Speaker 1: one of our former PhD students, and um, you know,

1273
01:21:35,080 --> 01:21:37,559
Speaker 1: they all say, yeah, it's a good idea, but I

1274
01:21:37,600 --> 01:21:39,519
Speaker 1: think one of the counter arguments, well, you can do it,

1275
01:21:39,600 --> 01:21:41,800
Speaker 1: and you do do it, but but it's a pain.

1276
01:21:42,080 --> 01:21:44,559
Speaker 1: It takes it takes three funds instead of takes four

1277
01:21:45,000 --> 01:21:48,880
Speaker 1: four funds. Yeah, so we we talked earlier about the

1278
01:21:48,920 --> 01:21:50,800
Speaker 1: ariskle laws and the rise of four oh one K.

1279
01:21:52,120 --> 01:21:56,080
Speaker 1: Tell us your perspective on how things have evolved from

1280
01:21:56,320 --> 01:22:01,759
Speaker 1: defined benefits and pensions to define contribution and self directed

1281
01:22:01,800 --> 01:22:06,320
Speaker 1: retirement funds. Well, it's when when that trend first started.

1282
01:22:06,320 --> 01:22:08,280
Speaker 1: I mean, as as you know well, and many do

1283
01:22:09,920 --> 01:22:12,479
Speaker 1: borrow and k was never intended to be your main

1284
01:22:12,640 --> 01:22:16,120
Speaker 1: retirement vehicle. It was supposed to be a supplement precisely,

1285
01:22:17,200 --> 01:22:20,559
Speaker 1: and so for good or bad reasons, we we moved

1286
01:22:21,280 --> 01:22:24,519
Speaker 1: in the private sector. Now, in the public sector we

1287
01:22:24,600 --> 01:22:29,719
Speaker 1: still have a preponderance, although it's changing slowly, of defined benefit.

1288
01:22:29,920 --> 01:22:33,479
Speaker 1: And that's an area which I've done some work also

1289
01:22:33,560 --> 01:22:36,799
Speaker 1: over the years and and involved with a project that's

1290
01:22:36,840 --> 01:22:40,320
Speaker 1: working on it to this day at Stanford. And that's

1291
01:22:40,360 --> 01:22:43,599
Speaker 1: a really serious problem of its own. But let's deal

1292
01:22:43,640 --> 01:22:49,679
Speaker 1: with the private world. Um, it's I mean, freedom is great.

1293
01:22:49,840 --> 01:22:52,400
Speaker 1: It's wonderful. You can decide how much to say, you

1294
01:22:52,439 --> 01:22:55,080
Speaker 1: can decide how to invest it. When you retire, you

1295
01:22:55,120 --> 01:22:58,040
Speaker 1: can decide what to do with the money, whether invested

1296
01:22:58,080 --> 01:23:00,600
Speaker 1: and newitize it. I mean, you've got this world of

1297
01:23:00,760 --> 01:23:05,200
Speaker 1: choice now constrained when you're retired, you're not constrained at all.

1298
01:23:05,320 --> 01:23:08,519
Speaker 1: Generally when you're working, you're constrained by the menu that

1299
01:23:08,560 --> 01:23:14,000
Speaker 1: your employer offers you. But um, but it's I mean,

1300
01:23:14,200 --> 01:23:16,120
Speaker 1: the good news is you've got you can choose lots

1301
01:23:16,120 --> 01:23:18,559
Speaker 1: of different things. The bad news is you can choose

1302
01:23:18,600 --> 01:23:22,000
Speaker 1: lots of different things. And I think it's incumbent upon

1303
01:23:22,040 --> 01:23:27,160
Speaker 1: employers do at least try to limit your choice set

1304
01:23:27,280 --> 01:23:33,800
Speaker 1: within the forellen Care four three B plan two sensible investments,

1305
01:23:33,960 --> 01:23:37,240
Speaker 1: and to provide some sort of assistance so that you

1306
01:23:37,439 --> 01:23:40,879
Speaker 1: to help you make informed choices. Now that's self serving.

1307
01:23:41,360 --> 01:23:45,400
Speaker 1: I'm not involved with the financial engines anymore, but um,

1308
01:23:45,439 --> 01:23:49,120
Speaker 1: but it's it's it's very frightening, and I think we

1309
01:23:49,240 --> 01:23:52,400
Speaker 1: have evidence. Just as we have evidence in the public

1310
01:23:52,439 --> 01:23:56,680
Speaker 1: sector that employers in the public sector are not sufficiently

1311
01:23:56,760 --> 01:24:01,080
Speaker 1: funding there to find benefit plans, we have evidence that many,

1312
01:24:01,080 --> 01:24:05,479
Speaker 1: many individuals are not sufficiently funding there for owen K

1313
01:24:05,680 --> 01:24:09,439
Speaker 1: four three B plans. Some don't even have access to any.

1314
01:24:10,479 --> 01:24:14,719
Speaker 1: And then it's sort of hard to know what people

1315
01:24:14,760 --> 01:24:18,240
Speaker 1: are doing with that money when they retire. But my

1316
01:24:18,320 --> 01:24:21,320
Speaker 1: guess is it's not a pretty picture. No, to say

1317
01:24:21,360 --> 01:24:25,360
Speaker 1: the least. Um. You mentioned pension funds. I want to

1318
01:24:25,439 --> 01:24:27,559
Speaker 1: run a pet theory by you that I that I

1319
01:24:27,640 --> 01:24:32,679
Speaker 1: have a lot of pension funds have over the past

1320
01:24:32,760 --> 01:24:36,599
Speaker 1: decade or two really ramped up their exposure to hedge

1321
01:24:36,600 --> 01:24:40,960
Speaker 1: funds and the only these the only explanation I could

1322
01:24:40,960 --> 01:24:44,320
Speaker 1: find is well, we have this expected return for bonds,

1323
01:24:44,360 --> 01:24:46,800
Speaker 1: and we have that expected return for stocks. But look,

1324
01:24:46,840 --> 01:24:49,320
Speaker 1: I expected return for hedge funds is so much greater.

1325
01:24:50,800 --> 01:24:53,320
Speaker 1: Is that remotely you've got it? And and as a

1326
01:24:53,320 --> 01:24:55,840
Speaker 1: matter of fact, it's some research out of the group

1327
01:24:55,880 --> 01:24:59,840
Speaker 1: in Europe. But but on US pension funds you have

1328
01:25:00,000 --> 01:25:05,280
Speaker 1: of this bizarre tail wagging the dog. The way public

1329
01:25:05,320 --> 01:25:10,920
Speaker 1: pension funds work is that the actuary comes up with quote,

1330
01:25:10,920 --> 01:25:14,719
Speaker 1: an expected rate of return for the fund and then

1331
01:25:15,439 --> 01:25:21,080
Speaker 1: does calculations using that. The assumption that you use that

1332
01:25:21,160 --> 01:25:25,840
Speaker 1: to discount everything you've got, including the contributions the state

1333
01:25:25,880 --> 01:25:29,000
Speaker 1: has to make too. Well, no, I'm talking about just yes,

1334
01:25:29,040 --> 01:25:33,920
Speaker 1: including contributions, but future contributions. But if if you just ask, well,

1335
01:25:33,920 --> 01:25:36,559
Speaker 1: what's what are the assets worth? Well, we know that

1336
01:25:36,680 --> 01:25:40,400
Speaker 1: we have market values. What are the liabilities worth? I

1337
01:25:40,439 --> 01:25:44,360
Speaker 1: have promised Joe and the police force that in five

1338
01:25:44,439 --> 01:25:48,040
Speaker 1: years he'll retire and he'll get X dollars per month

1339
01:25:48,080 --> 01:25:52,960
Speaker 1: till he dies. What's that worth? Well? Any economists would say, well,

1340
01:25:53,320 --> 01:25:56,920
Speaker 1: you get the actuarial tables, you figure out life expectancy,

1341
01:25:57,000 --> 01:26:00,400
Speaker 1: and then you discount those payments at the treasure rate.

1342
01:26:01,160 --> 01:26:04,120
Speaker 1: You claim you're going to make them their riskless, their

1343
01:26:04,200 --> 01:26:08,000
Speaker 1: bonds and they should be valued like bonds. No, the

1344
01:26:08,080 --> 01:26:12,479
Speaker 1: state actuaries take those claims, those payments, discount them at

1345
01:26:12,479 --> 01:26:16,040
Speaker 1: the expected return of the fund, which was seven and

1346
01:26:16,040 --> 01:26:18,840
Speaker 1: a half percent or so, which is it seems to

1347
01:26:18,880 --> 01:26:21,200
Speaker 1: be made up. It's just so who came up with

1348
01:26:21,280 --> 01:26:23,519
Speaker 1: that number? Okay? Well, but then I want to go

1349
01:26:23,560 --> 01:26:26,400
Speaker 1: I want to complete the thought to your Okay, So

1350
01:26:27,479 --> 01:26:32,320
Speaker 1: you know, the politicians, if you will, and maybe the unions,

1351
01:26:32,400 --> 01:26:35,240
Speaker 1: and maybe the people in the office of the Chief

1352
01:26:35,240 --> 01:26:40,000
Speaker 1: Actuary and the people running the pension fund are heavily

1353
01:26:40,040 --> 01:26:44,839
Speaker 1: pressured to make those liabilities values as small as possible

1354
01:26:45,720 --> 01:26:48,160
Speaker 1: so that they look good relative to the value of

1355
01:26:48,160 --> 01:26:51,360
Speaker 1: the assets. They value the assets at market. So that's fine,

1356
01:26:52,040 --> 01:26:55,800
Speaker 1: but the value of the liability. So let's see, if

1357
01:26:55,840 --> 01:27:00,559
Speaker 1: we increase our expected return, we can discount the promised

1358
01:27:00,560 --> 01:27:03,559
Speaker 1: payments at a higher rate, and their present value will

1359
01:27:03,600 --> 01:27:07,599
Speaker 1: be lower and our funding will be better and there

1360
01:27:07,640 --> 01:27:13,439
Speaker 1: by reducing how much money and precisely so so and

1361
01:27:13,520 --> 01:27:17,439
Speaker 1: that's exactly, and you've raised the point. So we really

1362
01:27:17,479 --> 01:27:19,960
Speaker 1: need to get that expected return. How how can we

1363
01:27:20,000 --> 01:27:24,760
Speaker 1: do it? Well, private equity, hedge funds, etcetera. We can

1364
01:27:24,800 --> 01:27:27,360
Speaker 1: take what we expect from the stock market and had

1365
01:27:27,439 --> 01:27:31,280
Speaker 1: three hundred basis points three more, because after all, their

1366
01:27:31,360 --> 01:27:35,120
Speaker 1: their golden instruments, and that'll get our expected return up.

1367
01:27:35,160 --> 01:27:37,880
Speaker 1: And as I mentioned as a study, I'm blocking now

1368
01:27:37,880 --> 01:27:41,040
Speaker 1: on the authors where they very carefully looked at pension

1369
01:27:41,040 --> 01:27:44,559
Speaker 1: funds in great detail, and you can see it. You

1370
01:27:44,600 --> 01:27:48,680
Speaker 1: can see them they're putting more money in those asset classes,

1371
01:27:49,560 --> 01:27:53,599
Speaker 1: not probably because they think they're wonderful or maybe they

1372
01:27:53,640 --> 01:27:56,360
Speaker 1: don't even think they can get three hundred basis points

1373
01:27:56,439 --> 01:28:01,040
Speaker 1: more than stocks net net, but because that will enable

1374
01:28:01,120 --> 01:28:04,040
Speaker 1: them to cook the books and make the situation look

1375
01:28:04,080 --> 01:28:06,679
Speaker 1: even better then it does now, which is a lot

1376
01:28:06,720 --> 01:28:10,519
Speaker 1: better than it really is, and put a plug in. Uh.

1377
01:28:10,560 --> 01:28:14,200
Speaker 1: There's a project at Stanford called pension tracker dot org

1378
01:28:14,960 --> 01:28:17,759
Speaker 1: where we go through this process for all the major

1379
01:28:17,800 --> 01:28:21,960
Speaker 1: pension funds in the country really and and city pension

1380
01:28:22,200 --> 01:28:27,080
Speaker 1: and county PENSI pension tracker dot org, and we compute

1381
01:28:27,080 --> 01:28:30,519
Speaker 1: not only quote actual aerial values, but also what we

1382
01:28:30,560 --> 01:28:33,120
Speaker 1: call market values where we try to correct for this.

1383
01:28:33,600 --> 01:28:39,400
Speaker 1: So so the United States pension plans public pension plans

1384
01:28:40,320 --> 01:28:45,360
Speaker 1: is a built on an assumption that's false, tweaked to

1385
01:28:45,400 --> 01:28:49,160
Speaker 1: show better expected returns than anybody should reasonably expect. Well,

1386
01:28:49,200 --> 01:28:51,760
Speaker 1: I won't go that far, but to show I mean,

1387
01:28:55,080 --> 01:28:59,519
Speaker 1: I'm sitting here with will Sharp, Nobel laureate and inventor

1388
01:28:59,560 --> 01:29:02,639
Speaker 1: of cap in the short ratio, and you've essentially made

1389
01:29:02,800 --> 01:29:06,880
Speaker 1: a case for fraud. These guys are defrauding the public

1390
01:29:06,920 --> 01:29:11,080
Speaker 1: and the taxpayer. I'm gonna draw that conclusion. Hey, I'm

1391
01:29:11,120 --> 01:29:13,799
Speaker 1: not a lawyer, so I'm not going to talk about fraud.

1392
01:29:14,439 --> 01:29:17,320
Speaker 1: And b I'm sure there are plenty of people within

1393
01:29:17,400 --> 01:29:21,280
Speaker 1: these organizations, let's say the state pension funds, who honestly

1394
01:29:21,320 --> 01:29:25,439
Speaker 1: believe that you really should plan to on average get

1395
01:29:26,000 --> 01:29:29,479
Speaker 1: a bonus of three basis points net from hedge funds

1396
01:29:29,479 --> 01:29:31,479
Speaker 1: and private equity. You know if you I don't happen

1397
01:29:31,479 --> 01:29:34,479
Speaker 1: to be among them. If this was the nineties, I

1398
01:29:34,520 --> 01:29:36,960
Speaker 1: could say, hey, there have been a lot of funds

1399
01:29:37,000 --> 01:29:39,719
Speaker 1: doing really well, and maybe you can make a good

1400
01:29:39,760 --> 01:29:43,559
Speaker 1: faith argument for that. But we have two decades of

1401
01:29:43,720 --> 01:29:47,720
Speaker 1: significant especially as three trillion dollars have flown into hedge

1402
01:29:47,720 --> 01:29:50,840
Speaker 1: funds from a tiny percentage of that UH and the

1403
01:29:51,040 --> 01:29:54,320
Speaker 1: number of hedge funds have scaled up ten x. Maybe

1404
01:29:54,400 --> 01:29:56,639
Speaker 1: at one point in time when there were a small

1405
01:29:56,760 --> 01:30:00,320
Speaker 1: number of hedge funds managing a small amount of money

1406
01:30:00,520 --> 01:30:04,880
Speaker 1: that alpha was legitimate, but at this point that's just

1407
01:30:04,960 --> 01:30:07,880
Speaker 1: a fantasy. Well, you know, there have been very, very

1408
01:30:07,880 --> 01:30:10,680
Speaker 1: careful academic studies. It's hard to do, it's hard to

1409
01:30:10,680 --> 01:30:15,040
Speaker 1: get the data. But my read, at least of some

1410
01:30:15,160 --> 01:30:18,439
Speaker 1: of the more recent ones is that if you can

1411
01:30:18,479 --> 01:30:21,719
Speaker 1: get in the top x per cent, and I say

1412
01:30:21,720 --> 01:30:23,160
Speaker 1: this is given the fact that we're sitting in the

1413
01:30:23,200 --> 01:30:27,920
Speaker 1: offices of one of these, and if you can get in,

1414
01:30:28,760 --> 01:30:31,360
Speaker 1: then maybe you can get an edge, probably not three

1415
01:30:31,520 --> 01:30:34,519
Speaker 1: D basis points, but not an edge. Um. But to

1416
01:30:34,560 --> 01:30:38,200
Speaker 1: be perfectly frank, the public pension funds can't get in

1417
01:30:38,240 --> 01:30:41,920
Speaker 1: a lot of these because they don't want the disclosures.

1418
01:30:42,680 --> 01:30:48,639
Speaker 1: So you know, but again I'm not I consulted with

1419
01:30:49,200 --> 01:30:54,080
Speaker 1: CalPERS for many, many years on risk analysis and performance analysis,

1420
01:30:54,760 --> 01:30:57,599
Speaker 1: and there are very good people and and a lot

1421
01:30:57,600 --> 01:31:00,639
Speaker 1: of these organizations, and some of them, I'm sure believe

1422
01:31:00,720 --> 01:31:03,320
Speaker 1: that that's true. But I don't believe it's true. And

1423
01:31:03,360 --> 01:31:06,320
Speaker 1: I think it's an unfortunate thing. And I think that

1424
01:31:06,439 --> 01:31:08,680
Speaker 1: the problem and you can look at the statistics on

1425
01:31:08,720 --> 01:31:14,440
Speaker 1: our on our websites, but um, it's it's it's crisis

1426
01:31:14,479 --> 01:31:19,360
Speaker 1: proportions and and footnote to Caliper's two years ago, they

1427
01:31:19,600 --> 01:31:22,519
Speaker 1: tossed out all their hedge funds and moved closer to

1428
01:31:22,760 --> 01:31:27,360
Speaker 1: a a more bill sharp type of investing strategy. So

1429
01:31:27,560 --> 01:31:29,040
Speaker 1: and not that it was a lot of money. It

1430
01:31:29,120 --> 01:31:32,519
Speaker 1: was I want to say, two or four billion dollars

1431
01:31:32,880 --> 01:31:35,920
Speaker 1: billion here, billion there. Eventually, while it's real money, it

1432
01:31:36,000 --> 01:31:39,280
Speaker 1: starts there, Well, what are they these days? Two seven years?

1433
01:31:39,760 --> 01:31:42,439
Speaker 1: I'm not some un godly You can find it on

1434
01:31:42,479 --> 01:31:45,719
Speaker 1: the site, so give us that that don't pension tracker

1435
01:31:45,800 --> 01:31:48,200
Speaker 1: dot org, pension tracker dot all. You know, I could

1436
01:31:48,280 --> 01:31:51,160
Speaker 1: talk to you about this stuff for all, for hours

1437
01:31:51,200 --> 01:31:53,680
Speaker 1: and hours. I have my favorite questions I want to

1438
01:31:53,680 --> 01:31:56,240
Speaker 1: get to. Before I get to them, I have to

1439
01:31:56,280 --> 01:32:00,320
Speaker 1: ask you about long term capital management. Since were talking

1440
01:32:00,320 --> 01:32:05,640
Speaker 1: about hedge funds, you got sort of an interesting perspective

1441
01:32:05,680 --> 01:32:09,960
Speaker 1: on what happened there. Tell us about it. Let me

1442
01:32:09,960 --> 01:32:12,200
Speaker 1: tell you a pre story. I worked with them with

1443
01:32:12,320 --> 01:32:16,640
Speaker 1: a private family um that actually was one of the

1444
01:32:16,720 --> 01:32:19,840
Speaker 1: early investors in long term capital. And there came a

1445
01:32:19,920 --> 01:32:23,160
Speaker 1: point at which long Term Capital said, and we were

1446
01:32:23,200 --> 01:32:26,439
Speaker 1: talking to Byron Chules who was involved there. Um, well

1447
01:32:26,479 --> 01:32:29,680
Speaker 1: we're giving you and others all your money back, and

1448
01:32:29,720 --> 01:32:31,439
Speaker 1: we said, we don't want our money back. We made

1449
01:32:31,479 --> 01:32:34,400
Speaker 1: a lot of money, you've been doing a great job, etcetera.

1450
01:32:34,800 --> 01:32:37,120
Speaker 1: And he said, no, I'm sorry, but we're cutting back

1451
01:32:37,120 --> 01:32:40,800
Speaker 1: on clients, were men at, etcetera. So reluctantly we took

1452
01:32:40,800 --> 01:32:45,439
Speaker 1: our money back and then everything broke loose. Um long

1453
01:32:45,560 --> 01:32:50,800
Speaker 1: term capital was You know, it's very hard to tell

1454
01:32:50,840 --> 01:32:55,360
Speaker 1: from the outside, but I take it just the simple

1455
01:32:55,520 --> 01:32:59,519
Speaker 1: version is that leverage can make you a lot of money,

1456
01:32:59,520 --> 01:33:02,160
Speaker 1: and it can lose you a lot of money. And

1457
01:33:02,240 --> 01:33:07,720
Speaker 1: there sophisticated and unsophisticated ways of getting leverage, but but

1458
01:33:07,920 --> 01:33:11,040
Speaker 1: they all have the same If you're really smart, they

1459
01:33:11,040 --> 01:33:14,240
Speaker 1: can make you a lot of money, and it's slightly

1460
01:33:14,280 --> 01:33:16,439
Speaker 1: more probable than they will lose you a lot of money.

1461
01:33:16,520 --> 01:33:18,640
Speaker 1: And they were running a hundred x or so. Is

1462
01:33:18,680 --> 01:33:22,960
Speaker 1: that different people of computer, different numbers, but I've heard

1463
01:33:23,000 --> 01:33:25,160
Speaker 1: thirty anyway, But it was way up there, and it

1464
01:33:25,240 --> 01:33:29,120
Speaker 1: was done in very convoluted, sophisticated ways. It wasn't just

1465
01:33:29,160 --> 01:33:31,200
Speaker 1: a matter of how much money have you barred from

1466
01:33:31,240 --> 01:33:34,640
Speaker 1: the bank. So they had all sorts of complex positions.

1467
01:33:35,200 --> 01:33:37,639
Speaker 1: And I'll tell you, you know, both the academics there

1468
01:33:37,720 --> 01:33:41,559
Speaker 1: and the practitioners, and I've known some of each. We're

1469
01:33:41,600 --> 01:33:46,519
Speaker 1: about as smart as you can get. And why it happened,

1470
01:33:46,880 --> 01:33:52,639
Speaker 1: who knows, But um, it's certainly certainly tarnished a lot

1471
01:33:52,680 --> 01:33:56,519
Speaker 1: of a lot of reputations. You know, it's funny you

1472
01:33:56,560 --> 01:33:59,200
Speaker 1: mentioned a lot of smart people. That was the title

1473
01:33:59,240 --> 01:34:02,439
Speaker 1: for lon Stein book when Genius failed. There was a

1474
01:34:02,520 --> 01:34:08,400
Speaker 1: tremendous amount of intellectual capital there and not enough appreciation

1475
01:34:08,640 --> 01:34:12,880
Speaker 1: for um. I want to I keep want to call

1476
01:34:12,920 --> 01:34:16,600
Speaker 1: it the sharp ratio, but not enough term recognition of

1477
01:34:16,640 --> 01:34:20,559
Speaker 1: the potential risk of all that level. From what I understand,

1478
01:34:20,560 --> 01:34:25,160
Speaker 1: their risk models were very complicated and very sophisticated, as

1479
01:34:25,200 --> 01:34:30,200
Speaker 1: you might well imagine, But sometimes simple is better than complicated.

1480
01:34:30,360 --> 01:34:33,080
Speaker 1: And you know, who knows what the motivations of any

1481
01:34:33,160 --> 01:34:36,800
Speaker 1: of the partners or employees might have been. But there

1482
01:34:36,800 --> 01:34:39,160
Speaker 1: are times when you know, if you think you've got

1483
01:34:39,160 --> 01:34:41,240
Speaker 1: an edge to take a gamble knowing you might lose,

1484
01:34:42,040 --> 01:34:44,320
Speaker 1: and uh, at thirty X there is not a lot

1485
01:34:44,360 --> 01:34:47,400
Speaker 1: of room for that's right, And and so maybe maybe

1486
01:34:47,439 --> 01:34:49,559
Speaker 1: they knew what chance they were taking. I don't know.

1487
01:34:50,439 --> 01:34:54,960
Speaker 1: We have been speaking to Bill Sharp of Stanford University,

1488
01:34:55,080 --> 01:34:59,320
Speaker 1: the capital asset pricing model, the sharp ratio. If you

1489
01:34:59,400 --> 01:35:02,599
Speaker 1: enjoy this conversation, be sure and check out the podcast extras,

1490
01:35:02,640 --> 01:35:05,200
Speaker 1: where we keep the tape rolling and continue to talk

1491
01:35:05,240 --> 01:35:10,720
Speaker 1: about all things risk, uh and return related. Check out

1492
01:35:10,760 --> 01:35:13,880
Speaker 1: my daily column on Bloomberg View dot com or follow

1493
01:35:13,880 --> 01:35:17,519
Speaker 1: me on Twitter at Ritolts. I'm Barry Hults. You're listening

1494
01:35:17,560 --> 01:35:23,639
Speaker 1: to Masters in Business on Bloomberg Radio. Welcome to the podcast.

1495
01:35:23,640 --> 01:35:25,760
Speaker 1: Thank you Bill so much for being so generous with

1496
01:35:25,800 --> 01:35:29,639
Speaker 1: your time. This is really endlessly fascinating, to great pleasure.

1497
01:35:29,680 --> 01:35:32,720
Speaker 1: I'm having a good time. I'm so glad to hear that. Um.

1498
01:35:32,760 --> 01:35:37,160
Speaker 1: So let's let's talk about, um, some of the standard

1499
01:35:37,280 --> 01:35:41,000
Speaker 1: questions I ask all of my guests, and and these

1500
01:35:41,040 --> 01:35:44,360
Speaker 1: are these are where I really get to learn, um

1501
01:35:44,400 --> 01:35:49,479
Speaker 1: about somebody in ways that perhaps they the public doesn't

1502
01:35:49,479 --> 01:35:52,960
Speaker 1: necessarily know about them. So what's the most important thing

1503
01:35:53,360 --> 01:35:58,280
Speaker 1: about you and your background that people don't know? Oh? My,

1504
01:35:59,560 --> 01:36:04,439
Speaker 1: that that that's difficult? Um, all right, obvious. Since we're

1505
01:36:04,439 --> 01:36:10,000
Speaker 1: talking finance, I'll tell you a financial story. UM. When

1506
01:36:10,000 --> 01:36:14,160
Speaker 1: I was an undergraduate, I took I was an economics mansion,

1507
01:36:14,200 --> 01:36:16,760
Speaker 1: but I took a course the beginning course and investments

1508
01:36:17,280 --> 01:36:21,120
Speaker 1: from a wonderful person men named John Clynden and and

1509
01:36:21,280 --> 01:36:25,240
Speaker 1: it was a very traditional finance course. And I was

1510
01:36:25,280 --> 01:36:26,880
Speaker 1: a junior, I think, and I said, well, you know,

1511
01:36:28,000 --> 01:36:30,439
Speaker 1: this is pretty good. And I had five hundred dollars,

1512
01:36:30,479 --> 01:36:32,400
Speaker 1: which at the time was a lot of money which

1513
01:36:32,400 --> 01:36:35,200
Speaker 1: I had saved up. I worked in garages and service

1514
01:36:35,200 --> 01:36:38,880
Speaker 1: stations and such to buy a car. And and at

1515
01:36:38,880 --> 01:36:41,160
Speaker 1: that time you could buy a car for five hundred dollars,

1516
01:36:41,960 --> 01:36:44,680
Speaker 1: but I, for various reasons, I wasn't going to buy

1517
01:36:44,720 --> 01:36:47,520
Speaker 1: the car for a few months. So I thought, well,

1518
01:36:47,240 --> 01:36:51,000
Speaker 1: I'll do a little investment, and so I did my

1519
01:36:51,520 --> 01:36:55,040
Speaker 1: securities research as I was told, and found a company,

1520
01:36:55,080 --> 01:36:57,479
Speaker 1: I think it was Learner Stores or Learner Brothers or

1521
01:36:57,560 --> 01:37:00,960
Speaker 1: something sure as a women's clothing store, and they were

1522
01:37:01,080 --> 01:37:07,400
Speaker 1: in new management, expansion to shopping models, whatever. And of course,

1523
01:37:07,439 --> 01:37:10,040
Speaker 1: not knowing at the time that things of that sort

1524
01:37:10,120 --> 01:37:13,280
Speaker 1: we're supposed to be incorporated in the price, I and

1525
01:37:13,560 --> 01:37:16,599
Speaker 1: went down to my local Meryll Lynch office and bought

1526
01:37:16,600 --> 01:37:20,479
Speaker 1: five hundred dollars worth of Learner Brothers Stores. Well you know,

1527
01:37:20,560 --> 01:37:23,160
Speaker 1: of course, what happened. In three months, It became three

1528
01:37:23,200 --> 01:37:27,160
Speaker 1: hundred dollars and so I had a work all summer

1529
01:37:27,200 --> 01:37:30,680
Speaker 1: before I could buy my car. And maybe that was

1530
01:37:30,760 --> 01:37:34,840
Speaker 1: the beginning of my suspicion that markets were efficient. I'm

1531
01:37:34,840 --> 01:37:39,479
Speaker 1: not sure, but it certainly you told me that that

1532
01:37:39,840 --> 01:37:43,160
Speaker 1: a career and investments was as a practitioner at least

1533
01:37:43,240 --> 01:37:46,840
Speaker 1: was not for me. So you've mentioned a bunch of mentors.

1534
01:37:47,520 --> 01:37:50,120
Speaker 1: Tell us who who are Who are the people that

1535
01:37:50,680 --> 01:37:54,880
Speaker 1: really mentored your thought process and your career. Well, it

1536
01:37:54,960 --> 01:37:59,280
Speaker 1: always comes down to two I've mentioned in in our conversations,

1537
01:38:00,080 --> 01:38:05,760
Speaker 1: um J. Fred Weston. Fred Weston was an economist from Chicago,

1538
01:38:05,920 --> 01:38:09,719
Speaker 1: University of Chicago in the finance department in the Business School,

1539
01:38:10,360 --> 01:38:13,320
Speaker 1: and I was his one of his research assistants as

1540
01:38:13,320 --> 01:38:18,000
Speaker 1: an undergraduate UM And then when I took my PhD,

1541
01:38:18,080 --> 01:38:22,000
Speaker 1: I found one of the five fields could be in finance.

1542
01:38:22,600 --> 01:38:25,120
Speaker 1: So even it was the business school, one of my

1543
01:38:25,200 --> 01:38:28,840
Speaker 1: five fields from my economics PhD was with Fred, and

1544
01:38:29,240 --> 01:38:32,760
Speaker 1: Fred and I were close and he was a huge influence.

1545
01:38:33,320 --> 01:38:36,880
Speaker 1: And then the other main influence was Armin Auchin, who

1546
01:38:37,080 --> 01:38:45,360
Speaker 1: was a micro economist, brilliant, quixotic, um, quirky um, who

1547
01:38:45,400 --> 01:38:49,400
Speaker 1: from whom I took micro economics. UH the beginning of

1548
01:38:49,520 --> 01:38:52,400
Speaker 1: the PhD program. I guess I took in the NBA

1549
01:38:52,520 --> 01:38:56,600
Speaker 1: program who taught me to think like an economist? And

1550
01:38:57,600 --> 01:39:01,600
Speaker 1: um So in many ways the C A P M.

1551
01:39:01,640 --> 01:39:06,000
Speaker 1: I can trace to those two people. Who else affected

1552
01:39:06,040 --> 01:39:11,000
Speaker 1: your thought process about investing? What? What investors have influenced

1553
01:39:11,000 --> 01:39:17,479
Speaker 1: how you look at the world of of pricing and returns? Well,

1554
01:39:18,320 --> 01:39:23,240
Speaker 1: I wouldn't say any investors, particularly have academics. I came

1555
01:39:23,280 --> 01:39:28,120
Speaker 1: at from an academic viewpoint, and again, um I was

1556
01:39:28,160 --> 01:39:34,479
Speaker 1: bringing economics into finance, as was Fred and as were

1557
01:39:34,560 --> 01:39:38,519
Speaker 1: some others. But in very early days, and in a sense,

1558
01:39:38,600 --> 01:39:42,280
Speaker 1: we were bringing uncertainty in the economics. Most economic theory

1559
01:39:43,400 --> 01:39:47,360
Speaker 1: was in a world of certainty, where you knew when

1560
01:39:47,360 --> 01:39:49,920
Speaker 1: you put these inputs in, you're gonna get those outputs out,

1561
01:39:49,960 --> 01:39:53,280
Speaker 1: and the prices were known, so there wasn't a lot.

1562
01:39:53,320 --> 01:39:58,439
Speaker 1: There were early traces of dealing with uncertainty within economic theory,

1563
01:39:59,120 --> 01:40:02,760
Speaker 1: but but only if you and so. I was part

1564
01:40:02,800 --> 01:40:06,240
Speaker 1: of a group, and there are many others, including traditional

1565
01:40:06,280 --> 01:40:12,080
Speaker 1: economists such as kenn Arrow Gerard Dubrow, who brought uncertainty economics.

1566
01:40:12,120 --> 01:40:17,160
Speaker 1: But both traditional academic finance and traditional academic economics were

1567
01:40:17,280 --> 01:40:20,040
Speaker 1: very different, and so in a sense it was a

1568
01:40:20,040 --> 01:40:23,200
Speaker 1: matter of finding a home and building this whole new

1569
01:40:23,280 --> 01:40:27,240
Speaker 1: idea of financial economics, the two together and in particular

1570
01:40:27,280 --> 01:40:31,240
Speaker 1: financial economic theory. Let's let's talk about books. This is

1571
01:40:31,240 --> 01:40:34,400
Speaker 1: the question that listeners ask more than any other. Tell

1572
01:40:34,479 --> 01:40:39,800
Speaker 1: us about some of your favorite books. Um, well, you

1573
01:40:39,800 --> 01:40:43,519
Speaker 1: know that's a hard question. You know, I don't really have.

1574
01:40:44,439 --> 01:40:46,160
Speaker 1: I'm not going to say any of my own books

1575
01:40:46,240 --> 01:40:50,759
Speaker 1: because I find it, if anything, painful to reread. I recently,

1576
01:40:51,800 --> 01:40:54,639
Speaker 1: some while ago, did a book of readings of my works,

1577
01:40:55,479 --> 01:40:58,800
Speaker 1: selected works, and and to do that I had to

1578
01:40:58,840 --> 01:41:02,880
Speaker 1: read through all my own work books and papers and such,

1579
01:41:02,920 --> 01:41:07,400
Speaker 1: which I found very painful. Um, I completely understand. But

1580
01:41:08,160 --> 01:41:10,960
Speaker 1: you know, there there there are no books. I have

1581
01:41:11,040 --> 01:41:14,000
Speaker 1: books on myself that I would never part with, but

1582
01:41:14,000 --> 01:41:16,439
Speaker 1: but I don't reread them or I really even look

1583
01:41:16,520 --> 01:41:18,519
Speaker 1: up things. What was the most recent thing you read?

1584
01:41:18,560 --> 01:41:22,360
Speaker 1: Tell us something? Uh, it could be fiction, nonfiction, something

1585
01:41:22,400 --> 01:41:26,559
Speaker 1: from yesterday. I read the book I'm I've been reading

1586
01:41:26,560 --> 01:41:29,600
Speaker 1: the last few days or so. It's called something like

1587
01:41:29,680 --> 01:41:32,839
Speaker 1: the decline of expertise or the death of expertise. Perhaps

1588
01:41:33,200 --> 01:41:37,680
Speaker 1: it's so it's polemic, but it's challenging and it's thought provoking.

1589
01:41:38,439 --> 01:41:44,479
Speaker 1: And um and you know, I like to read um books.

1590
01:41:45,960 --> 01:41:50,320
Speaker 1: I love computer programming. I think computer science although I'm

1591
01:41:50,360 --> 01:41:54,640
Speaker 1: not a computer scientist, but is fascinating. And I I

1592
01:41:54,760 --> 01:41:58,840
Speaker 1: like to think about and read about the potentially impact

1593
01:42:00,240 --> 01:42:06,240
Speaker 1: of technology, in particular computer and related technology on everything

1594
01:42:06,479 --> 01:42:12,639
Speaker 1: cars and professions and finance and what have you. Um

1595
01:42:12,720 --> 01:42:15,559
Speaker 1: and and so I like to do a little bit

1596
01:42:15,600 --> 01:42:21,040
Speaker 1: of modestly futuristic and to some extent history of the

1597
01:42:21,080 --> 01:42:25,559
Speaker 1: development of computer and technology. I I read, and I

1598
01:42:25,600 --> 01:42:28,080
Speaker 1: read a lot. I'm you know, I'm a classical music fan.

1599
01:42:28,160 --> 01:42:34,559
Speaker 1: And and I used to play jazz badly. Um what instrument? Bass?

1600
01:42:35,680 --> 01:42:38,479
Speaker 1: I play piano now from just for myself, I don't

1601
01:42:38,760 --> 01:42:42,320
Speaker 1: I don't play out as we say, but um and

1602
01:42:42,320 --> 01:42:45,040
Speaker 1: and I'm you know, I'm involved as I with a

1603
01:42:45,120 --> 01:42:49,000
Speaker 1: Carmel Bok Festival plug plug everybody should Carmel Buck Festival.

1604
01:42:49,120 --> 01:42:50,920
Speaker 1: When when does that take place? Two weeks in the

1605
01:42:50,960 --> 01:42:56,720
Speaker 1: summer h July August July. It's it's fantastic. So it's

1606
01:42:58,439 --> 01:43:02,040
Speaker 1: eight year, well, it's a number venues. We have chamber concerts,

1607
01:43:02,040 --> 01:43:05,760
Speaker 1: main concerts. Uh, it's it's it's a big deal, eighty

1608
01:43:05,840 --> 01:43:10,760
Speaker 1: years and counting. And uh, professional musicians, professional corral, it's

1609
01:43:10,800 --> 01:43:15,719
Speaker 1: it's it's a it's a remarkable occasion. I'm still stuck

1610
01:43:15,880 --> 01:43:19,639
Speaker 1: picturing you as a jazz bassist playing in some smokey club. Well,

1611
01:43:20,000 --> 01:43:23,559
Speaker 1: yeah it was. It was trad jazz, not not nothing,

1612
01:43:23,920 --> 01:43:27,760
Speaker 1: nothing after nineteen or maybe a little bit into the

1613
01:43:27,800 --> 01:43:32,799
Speaker 1: thirties styles. Okay, so we just celebrated Ella Fitzgerald's hundredth birthday.

1614
01:43:33,160 --> 01:43:37,280
Speaker 1: I'd forgotten and I didn't get or anything. Yeah, um,

1615
01:43:37,320 --> 01:43:42,680
Speaker 1: I saw Wynton Marsalis do a a the jazz ban

1616
01:43:42,760 --> 01:43:46,320
Speaker 1: of Lincoln Center. Did uh I read about that? Yeah,

1617
01:43:46,760 --> 01:43:49,200
Speaker 1: it was. It was lovely, it was absolutely it was

1618
01:43:49,240 --> 01:43:52,599
Speaker 1: a series of different vocalists. So I'm also, by the way,

1619
01:43:52,720 --> 01:43:56,360
Speaker 1: just an inveterate upper buff I go to every single

1620
01:43:56,400 --> 01:44:00,160
Speaker 1: operation in the movie theaters and uh and and I

1621
01:44:00,200 --> 01:44:03,080
Speaker 1: can live performances. But we don't have live opera in Carmel.

1622
01:44:03,400 --> 01:44:05,519
Speaker 1: What's what's in a while? We have one? But so

1623
01:44:05,560 --> 01:44:08,160
Speaker 1: you have to either go to San Francisco or Seattle.

1624
01:44:09,479 --> 01:44:14,559
Speaker 1: Uh so that's not too far. But but I'm not

1625
01:44:14,560 --> 01:44:17,160
Speaker 1: gonna stay on the air. But I really like the

1626
01:44:17,200 --> 01:44:20,840
Speaker 1: opera in the movie theater. Okay, that I find that

1627
01:44:21,000 --> 01:44:26,080
Speaker 1: very satisfying. Um, not so much jazz anymore. You know, no,

1628
01:44:26,920 --> 01:44:30,840
Speaker 1: modern jazz is to progress. It's it's it's you know.

1629
01:44:30,960 --> 01:44:33,240
Speaker 1: So what about so to me, it's so funny you

1630
01:44:33,240 --> 01:44:35,920
Speaker 1: said twenties and thirties. I appreciate it. I just doesn't

1631
01:44:36,000 --> 01:44:39,360
Speaker 1: turn me on. What about some of the classic jazz

1632
01:44:39,400 --> 01:44:42,719
Speaker 1: of the fifties. To me, classic jazz is fifties and sixties.

1633
01:44:42,720 --> 01:44:47,240
Speaker 1: So it's Ornette Coleman and Miles Davis and and Milonious Monk,

1634
01:44:47,320 --> 01:44:49,759
Speaker 1: and I sort of I sort of lost interest around

1635
01:44:49,760 --> 01:44:52,559
Speaker 1: the big band era. I grew up in the tail

1636
01:44:52,680 --> 01:44:55,840
Speaker 1: end of the big band era. Um. But and then

1637
01:44:55,880 --> 01:45:01,960
Speaker 1: I I listened, followed played, um traditional jazz. So big band,

1638
01:45:02,040 --> 01:45:05,719
Speaker 1: Duke Ellington, Tommy Dorsey, Leon Miller, you name it. Okay,

1639
01:45:06,080 --> 01:45:10,320
Speaker 1: so I love that stuff. But the next Jerry Mullikan

1640
01:45:10,479 --> 01:45:13,720
Speaker 1: and Coltrane, and that was not your bag. No. I

1641
01:45:13,720 --> 01:45:15,680
Speaker 1: mean I used to go to a little bit of

1642
01:45:16,000 --> 01:45:17,600
Speaker 1: you know, some of the clubs in l A in

1643
01:45:17,680 --> 01:45:20,719
Speaker 1: that era. But now I never got really got hooked

1644
01:45:20,760 --> 01:45:23,120
Speaker 1: on that. There used to be a great jazz scene

1645
01:45:23,120 --> 01:45:25,920
Speaker 1: in San Francisco before my time. Well, there was a

1646
01:45:26,080 --> 01:45:30,719
Speaker 1: good and when during the revival of trad jazz. Uh,

1647
01:45:30,800 --> 01:45:33,600
Speaker 1: there was a great trad jazz scene in San Francisco.

1648
01:45:33,760 --> 01:45:36,040
Speaker 1: And when I was at Cow my freshman year, we

1649
01:45:36,120 --> 01:45:37,880
Speaker 1: used to go to a place in Oakland, down by

1650
01:45:37,880 --> 01:45:43,719
Speaker 1: the in the industrial district. Um Turk Murphy, Um trying

1651
01:45:43,720 --> 01:45:47,960
Speaker 1: to remember his his clarinet player. But but there was

1652
01:45:48,000 --> 01:45:50,600
Speaker 1: some really good you know, there was a big revival

1653
01:45:50,720 --> 01:45:55,040
Speaker 1: period of trad jazz, just as later there was folk music.

1654
01:45:55,080 --> 01:45:57,479
Speaker 1: I love the folk music era. Well, the next time

1655
01:45:57,520 --> 01:45:58,840
Speaker 1: you get to New York, we'll have to get you

1656
01:45:58,840 --> 01:46:03,000
Speaker 1: over to Lincoln Center or the their jazz their big

1657
01:46:03,000 --> 01:46:07,559
Speaker 1: band jail. Yeah no, that's a fact. Yeah, absolutely. Um.

1658
01:46:07,600 --> 01:46:09,840
Speaker 1: So we went over some of the changes, We went

1659
01:46:09,880 --> 01:46:13,639
Speaker 1: over some of the shifts, and you told us about

1660
01:46:13,640 --> 01:46:16,000
Speaker 1: a time you failed. So I don't have to ask

1661
01:46:16,080 --> 01:46:18,400
Speaker 1: that that question. Well, there are more, but let's leave

1662
01:46:18,479 --> 01:46:22,599
Speaker 1: it at that. Any other antecdotes tell um. So let

1663
01:46:22,600 --> 01:46:25,200
Speaker 1: me let me get to my two favorite questions. I

1664
01:46:25,240 --> 01:46:28,600
Speaker 1: asked all of my guests if a student or a

1665
01:46:28,600 --> 01:46:31,080
Speaker 1: millennial would come up to you and said, I'm thinking

1666
01:46:31,120 --> 01:46:36,840
Speaker 1: about a career in either um financial economics or investing

1667
01:46:37,120 --> 01:46:39,200
Speaker 1: what sort of advice would you give them? Well, I

1668
01:46:39,200 --> 01:46:43,920
Speaker 1: can tell you what I've told our millennial grandchildren. I

1669
01:46:43,960 --> 01:46:49,400
Speaker 1: haven't the foggiest nor presumably does anybody else. But you'd

1670
01:46:49,400 --> 01:46:53,200
Speaker 1: better get a really broad education. And I mean really

1671
01:46:53,280 --> 01:46:58,160
Speaker 1: broad because technology, you know, I don't think we've begun

1672
01:46:58,200 --> 01:47:02,320
Speaker 1: to see anything yet. Technology is going to intrude and

1673
01:47:02,400 --> 01:47:07,680
Speaker 1: take out any profession or trade that has any routine

1674
01:47:07,760 --> 01:47:13,000
Speaker 1: nature to it. We know, UH, is subject to mechanization, computerization,

1675
01:47:13,040 --> 01:47:17,719
Speaker 1: whatever you want to call it. So you need real breadth,

1676
01:47:18,680 --> 01:47:22,360
Speaker 1: and you need the ability to think and to be

1677
01:47:22,800 --> 01:47:28,160
Speaker 1: and to learn, and the willingness. I have this little sideline,

1678
01:47:28,439 --> 01:47:32,160
Speaker 1: um for this will be my fifth year. I teach

1679
01:47:32,280 --> 01:47:36,640
Speaker 1: kids in one of the towns near Carmel how to

1680
01:47:36,720 --> 01:47:41,240
Speaker 1: program code. How old? How old are the kids roughly

1681
01:47:41,280 --> 01:47:46,040
Speaker 1: twelve and uh. It's been a challenge, uh and I'm

1682
01:47:46,080 --> 01:47:49,920
Speaker 1: still experimenting with different different methods. But there's this wonderful

1683
01:47:50,560 --> 01:47:53,719
Speaker 1: language developed over decades at m I T called scratch

1684
01:47:54,520 --> 01:47:58,719
Speaker 1: for eight to sixteen year olds, which is a remarkable language.

1685
01:47:58,720 --> 01:48:01,080
Speaker 1: As a matter of fact, I have a blog in

1686
01:48:01,160 --> 01:48:04,880
Speaker 1: which I did a whole retirement income monitor Monte Carlo

1687
01:48:05,000 --> 01:48:08,080
Speaker 1: system and I wrote it entirely in scratch just to

1688
01:48:08,160 --> 01:48:12,519
Speaker 1: prove I could. It's not fast, but it's not bad.

1689
01:48:12,680 --> 01:48:15,080
Speaker 1: I had to write all my graphic routines. You can

1690
01:48:15,120 --> 01:48:19,240
Speaker 1: find it. It's something like retirement in some scenarios, blog

1691
01:48:19,280 --> 01:48:22,960
Speaker 1: spot or something. You find it, or it's there's a

1692
01:48:23,000 --> 01:48:26,799
Speaker 1: link on my website. But my view is that kids

1693
01:48:27,479 --> 01:48:33,040
Speaker 1: not necessarily become programmers, but to think logically and to

1694
01:48:33,240 --> 01:48:39,400
Speaker 1: begin to get an appreciation of how you can think algorithmically.

1695
01:48:40,240 --> 01:48:47,720
Speaker 1: You can do research or analysis or decision making analytically,

1696
01:48:48,560 --> 01:48:53,160
Speaker 1: and also to understand what's going on with the things

1697
01:48:53,200 --> 01:48:56,280
Speaker 1: that are probably going to displace you in whatever job

1698
01:48:56,360 --> 01:49:00,760
Speaker 1: you start at. I mean, I have no notion what

1699
01:49:00,880 --> 01:49:03,960
Speaker 1: you know? What is what? What is university education going

1700
01:49:04,000 --> 01:49:07,559
Speaker 1: to be like in twenty years? Um, it's hard to

1701
01:49:07,600 --> 01:49:10,360
Speaker 1: even fathom. I mean, I've read again books. You know,

1702
01:49:10,360 --> 01:49:13,160
Speaker 1: there are books about speculating on that which some of

1703
01:49:13,240 --> 01:49:17,920
Speaker 1: which I've read, and you know, the whole idea. I mean,

1704
01:49:18,000 --> 01:49:22,920
Speaker 1: one one author had this um argument that I think

1705
01:49:23,040 --> 01:49:26,200
Speaker 1: is is very valid. We're beginning to learn something about

1706
01:49:26,200 --> 01:49:31,040
Speaker 1: how people learn, how brains work, and his argument was,

1707
01:49:31,400 --> 01:49:33,800
Speaker 1: if you try to invent the absolute worst way to

1708
01:49:33,880 --> 01:49:39,120
Speaker 1: try to convey information and education to a student, it

1709
01:49:39,120 --> 01:49:41,120
Speaker 1: would be to have somebody stand at the head of

1710
01:49:41,160 --> 01:49:44,880
Speaker 1: the class and talk to him for fifty minutes. And

1711
01:49:45,000 --> 01:49:49,200
Speaker 1: so all the ideas of online learning with feedback and

1712
01:49:49,240 --> 01:49:53,479
Speaker 1: constant testing and branching and all that, I think they're

1713
01:49:53,479 --> 01:49:58,240
Speaker 1: fascinating and and there's there's there's there's something there. So

1714
01:49:58,640 --> 01:50:01,599
Speaker 1: the socratic method has something too it because it forces

1715
01:50:01,680 --> 01:50:05,519
Speaker 1: people to think, Yeah, my my son, who's an education

1716
01:50:06,200 --> 01:50:09,479
Speaker 1: told me when we were talking about different ways I've

1717
01:50:09,560 --> 01:50:13,599
Speaker 1: experimented with my kids in this summer program, he said,

1718
01:50:13,960 --> 01:50:16,680
Speaker 1: you've got to change from being these are these are

1719
01:50:16,760 --> 01:50:20,360
Speaker 1: this is jargon and ed apparently, from being the stage

1720
01:50:20,400 --> 01:50:24,479
Speaker 1: on the stage, being the guide by the side. So

1721
01:50:24,600 --> 01:50:27,479
Speaker 1: I went from in the first two years I taught

1722
01:50:27,520 --> 01:50:30,760
Speaker 1: the course, all right, here I'm doing this, and now

1723
01:50:30,800 --> 01:50:34,000
Speaker 1: you do that and you can experiment with some variations.

1724
01:50:34,080 --> 01:50:36,200
Speaker 1: Now pay attention, we're going to do this and you're

1725
01:50:36,200 --> 01:50:39,759
Speaker 1: going to do that, which is the way in which

1726
01:50:39,760 --> 01:50:44,120
Speaker 1: you pretty much teach scratch uh to something called code

1727
01:50:44,120 --> 01:50:49,720
Speaker 1: dot org, which is online free material bill Gate, Suckerberg.

1728
01:50:49,840 --> 01:50:54,200
Speaker 1: It's heavily supported and the student just logs on and

1729
01:50:54,240 --> 01:50:58,519
Speaker 1: starts solving puzzles and it's got feedback, it's it's it's

1730
01:50:58,640 --> 01:51:02,000
Speaker 1: very clever code dot org. And what I found with

1731
01:51:02,040 --> 01:51:05,320
Speaker 1: my kids. I did this last year. Then I did

1732
01:51:05,360 --> 01:51:08,640
Speaker 1: ten one hour sessions and it was great for the

1733
01:51:08,680 --> 01:51:10,479
Speaker 1: first and then I would sit and you want to

1734
01:51:10,479 --> 01:51:13,040
Speaker 1: help or what are you doing? And I only have

1735
01:51:13,160 --> 01:51:16,719
Speaker 1: twelve kids. But and then it you know, at about

1736
01:51:16,840 --> 01:51:19,840
Speaker 1: session five, I could have killed him. They were getting

1737
01:51:19,840 --> 01:51:23,200
Speaker 1: really antsy and itchy and bored and and so I

1738
01:51:24,160 --> 01:51:26,639
Speaker 1: slowly got them to come over to scratch and learn

1739
01:51:26,680 --> 01:51:31,120
Speaker 1: a bigger, broader language where you can be more creative

1740
01:51:31,240 --> 01:51:37,200
Speaker 1: and and uh. But the takeaway is the collaborative approach

1741
01:51:37,400 --> 01:51:40,840
Speaker 1: seems to be more effective than just lecturing. Today, I'm

1742
01:51:40,880 --> 01:51:44,280
Speaker 1: thinking of mix, thinking of mix, and and I know

1743
01:51:44,479 --> 01:51:47,280
Speaker 1: people who teach that grade level, no this, They've been

1744
01:51:47,320 --> 01:51:50,160
Speaker 1: doing it for decades. It's just that I'm I grew

1745
01:51:50,240 --> 01:51:52,799
Speaker 1: up in an environment where I was up in front

1746
01:51:52,840 --> 01:51:56,960
Speaker 1: talking and yeah we had interchange and I did small seminars.

1747
01:51:57,640 --> 01:52:02,439
Speaker 1: But but still and the idea of now having every

1748
01:52:02,439 --> 01:52:05,360
Speaker 1: five minutes, you know, here's something, test them. If they

1749
01:52:05,439 --> 01:52:07,200
Speaker 1: learn it, they get to go on. If not, they

1750
01:52:07,240 --> 01:52:10,120
Speaker 1: go back. And you presented a different way until they

1751
01:52:10,160 --> 01:52:13,840
Speaker 1: get it. I mean, the ability of computerized systems too

1752
01:52:14,560 --> 01:52:18,000
Speaker 1: engage in all of that. You just you cannot ignore that.

1753
01:52:18,760 --> 01:52:21,000
Speaker 1: And and how that's all going to shake out, I

1754
01:52:21,040 --> 01:52:24,439
Speaker 1: don't know, but I'm awfully glad I'm not entering, you know,

1755
01:52:24,520 --> 01:52:29,280
Speaker 1: the academy now. And and my final and favorite question,

1756
01:52:29,960 --> 01:52:33,600
Speaker 1: what is it that you know about economics or finance

1757
01:52:33,840 --> 01:52:37,120
Speaker 1: or investing today that you wish you knew back in

1758
01:52:37,240 --> 01:52:42,200
Speaker 1: the late fifties early sixties when you were first setting out? Well,

1759
01:52:42,200 --> 01:52:45,640
Speaker 1: would be perfectly frank, that's an interesting question. I've not

1760
01:52:45,680 --> 01:52:50,200
Speaker 1: thought about that. Um, it's easy to say, I, well,

1761
01:52:50,240 --> 01:52:52,559
Speaker 1: I should have taken more math than the first course

1762
01:52:52,560 --> 01:52:57,280
Speaker 1: in calculus. I don't know that. But um, but I

1763
01:52:57,360 --> 01:53:01,400
Speaker 1: faked my way through well enough. Um, I don't know.

1764
01:53:01,560 --> 01:53:05,559
Speaker 1: I mean, it's it's been a hell of a ride,

1765
01:53:05,600 --> 01:53:07,800
Speaker 1: I say, the least I would not want to have,

1766
01:53:07,880 --> 01:53:11,600
Speaker 1: sort of. I mean, there's nothing more fun than discovering

1767
01:53:11,640 --> 01:53:14,920
Speaker 1: something you had to anticipated. I mean, that is just

1768
01:53:15,720 --> 01:53:19,960
Speaker 1: the best trip ever. Um, and so so I I

1769
01:53:21,040 --> 01:53:23,480
Speaker 1: was lucky enough to have a lot of those experiences.

1770
01:53:24,120 --> 01:53:29,320
Speaker 1: And also, you know, teaching, Uh, teaching can be very rewarding,

1771
01:53:29,400 --> 01:53:32,920
Speaker 1: can be very frustrating and very boring, but when it's rewarding,

1772
01:53:32,920 --> 01:53:36,599
Speaker 1: it's really rewarding. So I don't think I I choose

1773
01:53:36,600 --> 01:53:40,439
Speaker 1: to do it differently. We have been speaking with William F. Sharp,

1774
01:53:40,760 --> 01:53:45,360
Speaker 1: Nobel Laureate, creator of the capital asset pricing model, the

1775
01:53:45,439 --> 01:53:49,360
Speaker 1: Sharp ratio, and other measures of risk. Thank you Bill

1776
01:53:49,439 --> 01:53:51,479
Speaker 1: for being so generous with your time. This has been

1777
01:53:51,600 --> 01:53:56,400
Speaker 1: absolutely a delightful a couple of hours. If you enjoy

1778
01:53:56,520 --> 01:53:59,000
Speaker 1: this conversation, then look up an Inch or down an

1779
01:53:59,000 --> 01:54:03,599
Speaker 1: Inch on either Apple iTunes, SoundCloud or Bloomberg dot com

1780
01:54:03,600 --> 01:54:06,240
Speaker 1: and you can see any of the other hundred and

1781
01:54:06,360 --> 01:54:10,720
Speaker 1: fifty or so such previous conversations. I would be remiss

1782
01:54:11,160 --> 01:54:14,200
Speaker 1: if I did not thank Michael bat Nick, my head

1783
01:54:14,200 --> 01:54:18,080
Speaker 1: of research, Taylor Riggs, my book or producer. And again

1784
01:54:18,160 --> 01:54:21,280
Speaker 1: I have to thank Andres and Horowitz for hosting us

1785
01:54:21,280 --> 01:54:27,760
Speaker 1: here in their absolutely delightful facilities. We love your comments,

1786
01:54:28,000 --> 01:54:33,240
Speaker 1: feedback and suggestions right to us at m IB podcast

1787
01:54:33,360 --> 01:54:37,560
Speaker 1: at Bloomberg dot net. I'm Barry Ritolts. You've been listening

1788
01:54:37,560 --> 01:54:50,560
Speaker 1: to Masters in Business on Bloomberg Radio. Our world is

1789
01:54:50,560 --> 01:54:52,920
Speaker 1: always moving, so with Mery Lynch you can get access

1790
01:54:52,960 --> 01:54:55,880
Speaker 1: to financial guidance online, in person, or through the Apple.

1791
01:54:56,000 --> 01:54:58,280
Speaker 1: Visit mL dot com and learn more about Mery Lynch,

1792
01:54:58,320 --> 01:55:00,960
Speaker 1: an affiliated Bank of America. Meryl Nch makes available pducts

1793
01:55:00,960 --> 01:55:03,280
Speaker 1: and services offered by Merrill Lynch, Pierce Federan Smith, Incorporated

1794
01:55:03,320 --> 01:55:04,880
Speaker 1: or Registered Broker Dealer Member s I PC