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

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Speaker 1: Radio this weekend on the podcast Wow, what a what

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Speaker 1: a delightful conversation I had with Roger Ibbotson. If you

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Speaker 1: don't know who he is, well you need to become

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Speaker 1: a little more familiar with financial histories. He is one

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Speaker 1: of the founding fathers about modern thoughts on on asset allocation,

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Speaker 1: portfolio management, valuation factor UM. Go down the list of

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Speaker 1: a million things. He really was instrumental in the development

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Speaker 1: and expansion of CRISP, which is the giant stock database

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Speaker 1: UM originally out of the University of Chicago, Ibbotson associates.

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Speaker 1: He's on the board of advisors at Dimensional Funds. His

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Speaker 1: curriculum vitae is his pages and pages long. He was

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Speaker 1: extremely generous with his time and shared all sorts of

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Speaker 1: oscinating things with us. If you are at all a

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Speaker 1: stock market wank, if you're at all interested in why

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Speaker 1: some stocks go up and others don't, then you're going

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Speaker 1: to find this conversation to be absolutely fascinating. So, with

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Speaker 1: no further ado, my conversation with Yale Universities Roger Ibbotson

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Speaker 1: this week I have an extra special guest. His name

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Speaker 1: is Roger Ibbotson. He is a professor at the Yale

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Speaker 1: School of Management, where he is Professor of Practice Emeritus

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Speaker 1: of Finance. He is also the founder and chairman of

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Speaker 1: Ibbertson Associates, which was sold not too long ago to

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Speaker 1: Morning Star. He is on the board of directors of

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Speaker 1: Dimensional Fund Advisors. He is the chairman and CEO of

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Speaker 1: Zebra Capital Management uh an equity investment and hedge fund manager.

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Speaker 1: He has also taught for many years and served as

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Speaker 1: executive director for the Center for Research and Security Prices

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Speaker 1: better known as CRISP. He's the author of numerous books,

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Speaker 1: including Investment Markets, Gaining the Performance Advantage and Global Investing,

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Speaker 1: The Professionals Guide to the World of Capital Markets. Roger Ibbotson,

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Speaker 1: Welcome to Bloomberg. Great to be here. I've been looking

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Speaker 1: forward to having this conversation with you for a long

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Speaker 1: long time. I've certainly been familiar with Ibbotson Associates since

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Speaker 1: you founded them back in nine But let's go back

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Speaker 1: to your days when you got your PhD from Chicago.

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Speaker 1: What was the state of the world in finance, like

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Speaker 1: when when you first entered the markets, Well, you know,

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Speaker 1: it was really things were happening at the University of Chicago,

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Speaker 1: and ultimately many of the people there got Nobel prizes.

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Speaker 1: But what went on So, but if you just take

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Speaker 1: take us back a few years before that, uh, finance

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Speaker 1: really hadn't developed as a as an academic step tacos

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Speaker 1: security analyst or how to pick a stock or something

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Speaker 1: like that. And then and then we started really developing

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Speaker 1: a whole theory of how finance works. And I gotta

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Speaker 1: say I had I had great people to work with. Their.

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Speaker 1: My chairman of my committee when I wrote my dissertation

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Speaker 1: was Eugene Fama. He want to know about prize. I

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Speaker 1: had him my committee. Uh, Martin Miller, he wanted to

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Speaker 1: know about prize. Myron Shows want to know about prize.

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Speaker 1: Fischer Black he you know, you have to be alive

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Speaker 1: to win a know about prize. And unfortunately he died

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Speaker 1: before that, but he certainly would have. So it was

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Speaker 1: really a tremendous group to people to work with. That's

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Speaker 1: a Yankees murderous row of Nobel laureates there. Yes, uh so, yeah,

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Speaker 1: it was. It was a wonderful time going on. So

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Speaker 1: actually all the so many different discoveries were taking place

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Speaker 1: you mentioned that the study of finance really hadn't developed,

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Speaker 1: um academically as as much as it has since. Tell

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Speaker 1: us about the Center for Research in Security Prices for CRISP.

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Speaker 1: How did that come about and what was your involvement?

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Speaker 1: So the Center for Research and Security Prices we called

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Speaker 1: CRISP c RSP. Well, CRISP was really set up by

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Speaker 1: James Lourie and Larry Fisher, and they were collecting data

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Speaker 1: on on the stock market returns. And their data started

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Speaker 1: in and now you see a lot of things started in.

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Speaker 1: So they were collecting this data and um, that's what

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Speaker 1: started the center. Uh. And I gotta say, um, they

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Speaker 1: once they published this data, uh, they never kept it

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Speaker 1: quite up to date. But once they published it was

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Speaker 1: people were so interested because they had no idea what

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Speaker 1: actually had happened in the stock market. They didn't actually

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Speaker 1: have any sense for how stock market returns were like.

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Speaker 1: In fact, they kind of remember the thirties and how

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Speaker 1: terrible it was and so forth, and they knew things

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Speaker 1: were better lately, but but they still didn't really aized

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Speaker 1: the high returns and actually had happened in the stock market.

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Speaker 1: That's quite fascinating. So when you say this was not

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Speaker 1: much of an academic study. Are you really referring to

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Speaker 1: the fact that previous to the creation of CRISP, there

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Speaker 1: wasn't a whole lot of data that could be analyzed,

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Speaker 1: at least not consistent data in a in anything approaching

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Speaker 1: a um well structured way. Yeah, Chris really put the

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Speaker 1: data on the map. And then and when I got

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Speaker 1: my phdre the PhD there. But I actually stayed on

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Speaker 1: as a professor, and and when I stayed on, I

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Speaker 1: became the executive director of CRISP. So I wasn't, I guess,

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Speaker 1: helping to put the data together. And uh, it was

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Speaker 1: really not only the University of Chicago that was using

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Speaker 1: that data, but all the other universities were really using

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Speaker 1: that data. So this suddenly, suddenly finance became an empirical

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Speaker 1: subject that people people could study. That's the word I

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Speaker 1: was hunting for. It had no empiricism previous. This was

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Speaker 1: what years did you begin with CRISP. Well, I I

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Speaker 1: got there as a student in nineteen sixty eight and

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Speaker 1: became a faculty member in nineteen seventy four, and I

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Speaker 1: guess I became executive director. I don't remember a few

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Speaker 1: years after that of the center. But I was always

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Speaker 1: involved with Chris from the start because I was always

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Speaker 1: interested in data. And in fact, um, well I got

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Speaker 1: a I got a job while I was getting my

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Speaker 1: PhD in the investment office of the university, and I

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Speaker 1: came out the endowment right right. I was a consultant

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Speaker 1: to the office and one of the things they asked

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Speaker 1: me as a consultant was what do we do with

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Speaker 1: his bond portfolio? And I said, well, I can manage that,

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Speaker 1: And so they actually has a PhD student. I was

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Speaker 1: actually managing the University of Chicago bond portfolio. And how

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Speaker 1: large was that at the time, Well, these numbers were

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Speaker 1: not large in today's time to take a couple of

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Speaker 1: a couple of hundred million, you know, but still a

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Speaker 1: PhD student is like, okay, congratulations, you're running a few

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Speaker 1: hundred million dollars. That's out nothing, especially in the late sixties. Yeah,

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Speaker 1: it was. It was a great thing to do. And

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Speaker 1: and and it gets back to what you were talking

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Speaker 1: about when people. Once I was running the bond portfolio,

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Speaker 1: people would ask me when are Fisher and Lorie are

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Speaker 1: gonna update their study? Because first the first day that

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Speaker 1: came out and went from to nineteen sixty, and then

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Speaker 1: it was the nineteen to nineteen sixty five and then

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Speaker 1: nineteen but this was seventy to seventy three, and people

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Speaker 1: were asking when we want to see the updated data,

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Speaker 1: and I would ask. I would ask Larry Fisher when

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Speaker 1: it's gonna get updated, and Larry would give me a date,

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Speaker 1: but the date would go by and it wouldn't get done.

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Speaker 1: And and so this is when I really took the

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Speaker 1: ball by our hands, and I worked with Rex Sinkfield,

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Speaker 1: who was a student of mine at the time, and

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Speaker 1: we decided to put this put data that literally all

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Speaker 1: the UM we used a certainly to the Center for

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Speaker 1: Research for but we put some quicker data together to

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Speaker 1: get a sense of what something up to date actually

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Speaker 1: and and we brought it up to date actually through

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Speaker 1: seventy six at the time. So that brings us to

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Speaker 1: which not coincidentally, is when you launch Ibbanson Associates, tell

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Speaker 1: us about what motivated you to go out and hang

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Speaker 1: your own Shingle well West stock spons bills in inflation.

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Speaker 1: I was an assistant professor and we're just we had

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Speaker 1: just published stock spons Bills and inflation um first as

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Speaker 1: a couple of journal articles and what one which was

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Speaker 1: on the past and want to actually predicted the future,

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Speaker 1: UM long term, but then also as a monograph from

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Speaker 1: the cf A Institute. And every everybody was so interested that,

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Speaker 1: UM I was. I was getting inundated with letters coming

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Speaker 1: in CEOs asking me for information and and response of

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Speaker 1: this this question and that question. And and I was

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Speaker 1: at A. I barely had a secretary. I had a

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Speaker 1: part of a secretary. And I didn't know what to

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Speaker 1: do with all these letters from the c I O S,

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Speaker 1: c E O S and c I os and and

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Speaker 1: so forth. So I started hiring a couple of people

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Speaker 1: to help me out with this. And that's what that's

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Speaker 1: what it caused me to actually started ivots and Associates.

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Speaker 1: I had plenty of business at the start because somebody

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Speaker 1: people were actually requesting things from me. You sold Davidson Associates,

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Speaker 1: UH and Advisers to morning Star back in two thousand

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Speaker 1: and six, pretty good timing before the tide went out

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Speaker 1: in O eight oh nine. Um, what was that process? Like,

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Speaker 1: they're a pretty big shop, morning Star. How did that

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Speaker 1: transaction go? Well? In two thousand and six. By that time,

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Speaker 1: I was already at Yale Yale School of Management as

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Speaker 1: a professor in practice there. But in two thousand and six, uh,

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Speaker 1: the UM we had a hundred and fifty people and

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Speaker 1: invots and associates with offices in Chicago, New York and Tokyo.

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Speaker 1: Actually um, but still we were very small compared to

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Speaker 1: morning Star also in Chicago, right, don't they They They

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Speaker 1: were very far. They were just we were like two

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Speaker 1: firms that were somewhat similar that were only a couple

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Speaker 1: of blocks away from each other. That that's a pretty

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Speaker 1: natural fit that the large morning Star will acquire the

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Speaker 1: smaller Ibbotson in the same hometown. Actually, I gotta say

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Speaker 1: that we we were around first because I remember when

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Speaker 1: Joe Mansueto and then I in eighties but came to

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Speaker 1: a few of our UM holiday parties and things like that,

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Speaker 1: and um. But he but I gotta say, you have

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Speaker 1: to give Joe Massuado credit. Morning Star really took off

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Speaker 1: and grew. We were growing fast too, but nothing like

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Speaker 1: they were. You focused on data about markets generally. They

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Speaker 1: focused specifically on mutual funds, and that became a giant

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Speaker 1: growth area for them, especially with the Ariskle laws in

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Speaker 1: four oh one K coming up in in the early seventies. Well,

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Speaker 1: everybody was in the arrested was more um yeah, four

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Speaker 1: one case really started developing in the really in nineteenes.

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Speaker 1: First it was those defined men define benefit pension plans,

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Speaker 1: the dB plans, and uh we worked with them to

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Speaker 1: something stanton. But Marty Sarra actually picked the retail end

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Speaker 1: of it with a four OK market and the mutual

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Speaker 1: funds yes and so um. But we were too very

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Speaker 1: fast growing firms that were alongside each other for a

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Speaker 1: couple of decades in Chicago before they actually brought us out.

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Speaker 1: Quite intriguing. Uh, let's talk a little bit about CRISP

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Speaker 1: and and we mentioned earlier you worked on the nineteen

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Speaker 1: twenty six to present database of of stock market returns,

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Speaker 1: but not too long ago a new historical database was

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Speaker 1: added for the New York Stock Exchange, going back to

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Speaker 1: eighteen fifteen, straight up to the original sixth date. What

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Speaker 1: did you learn from that database about equity returns and

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Speaker 1: about volatility? Well, you know, first of all, we uh

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Speaker 1: we had to collect hand collect all that data back

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Speaker 1: to UM. What do you mean hand collect? Well, it

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Speaker 1: was it was in the Yale Bayneke Library where you

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Speaker 1: had a look at micro fich and old newspapers of

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Speaker 1: the New York Shipping and Commercial Chronicle, and it was

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Speaker 1: mostly about which ships are coming in to the harbor,

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Speaker 1: but they also listed to New York stock market prices. So,

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Speaker 1: so how do you error check that to make sure

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Speaker 1: that nobody makes a transcription error? That sounds like, you know,

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Speaker 1: a century of data. It's really easy to make a

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Speaker 1: mistake with that. Well it it is, uh, And I'm

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Speaker 1: not saying there's no possible mistakes in there because it

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Speaker 1: had to be hand collected. But there aren't that many

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Speaker 1: companies in the early days either. So if we're not

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Speaker 1: talking about the the three thousand stock that we might

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Speaker 1: be talking about today, we're talking about, you know, less

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Speaker 1: less than a hundred stocks over most of this period.

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Speaker 1: Quite quite fascinating. Um, so what did you learn about

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Speaker 1: equity returns and volatility? Well, you know that data in

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Speaker 1: the nineteen eighteen nineties eight hundreds was kind of unusual

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Speaker 1: because stocks tending to be issued around a hundred. It

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Speaker 1: almost looked like you were looking at bond data because

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Speaker 1: everything is trading at par or a little above. Well, yeah,

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Speaker 1: they were trading. They may trade in the range of

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Speaker 1: fifty to two hundreds or something, but they kind of

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Speaker 1: looked like bonds, and we had to keep on investigating

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Speaker 1: further and further to find out where are these these

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Speaker 1: These are stocks, aren't they? You know? And they were,

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Speaker 1: of course stocks, but they but they looked like bonds.

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Speaker 1: They weren't that volatile. They weren't. Of course, the trading

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Speaker 1: wasn't anything like the trading not a lot of volume

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Speaker 1: and trade by appointment only or did they actually that

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Speaker 1: was on the there was the the course of button

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Speaker 1: uh button would agreement, you know, they were traded on

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Speaker 1: on the curb and then uh then eventually inside. But

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Speaker 1: so they were continuously traded but not uh and and

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Speaker 1: and even the volume wasn't even recorded at that time,

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Speaker 1: you know, but we did get the last prices. That's

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Speaker 1: quite fascinating. So, you know, CRISP is very often associated

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Speaker 1: with factor based investing and equity risk premium UM. What

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Speaker 1: can you tell us about that? What What do we

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Speaker 1: know today about the equity ris premium that we didn't

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Speaker 1: know in the pre CRISP days back in UH The

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Speaker 1: theory was developing in the nineteen sixties, especially with the

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Speaker 1: UH and the nineteen fifties and sixties we had the

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Speaker 1: Harry Marco Wits came out with his portfolio theory in

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Speaker 1: in NFT two. It was at the University of Chicago. Actually,

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Speaker 1: and I gotta say it's kind of interesting because at

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Speaker 1: the first I didn't even Milton Freedman was reluctant to

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Speaker 1: give Harry Marco it's a PhD for this because he

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Speaker 1: wasn't sure this was economics. Okay, so you you've just

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Speaker 1: lowered my estimation to Milton Freeman a notch. How do

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Speaker 1: you not, Well, my hindsight biases Harry Marco. It's of

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Speaker 1: course you give him a PhD. But I guess that's

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Speaker 1: just hindsight bias, isn't it. Yeah, well, of course, now,

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Speaker 1: of course Harry has a Nobel Prize too, and so so.

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Speaker 1: And then we had the Capital Esta pricing models coming

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Speaker 1: along and in the ve with Bill Sharp and and

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Speaker 1: John Lettner and by the way, Harry Mark WIT's until

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Speaker 1: Sharp there's still around today. We can talk to them,

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Speaker 1: and so sure Sharp is out in um northern California.

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Speaker 1: And where's Mark Wits these days? He's in southern southern California.

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Speaker 1: So at the University of Chicago, when you get an

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Speaker 1: office as a faculty member. Do they give you like

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Speaker 1: a key to the office and a Nobel prize? How

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Speaker 1: does that work? Everybody in that faculty has that. That's

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Speaker 1: a lot of jewelry in that in that uh faculty department. Well, actually,

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Speaker 1: Bill Sharp wasn't ever at the university see Chicago, So um,

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Speaker 1: not every Nobel Prize went to the University of Chicago. People.

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Speaker 1: But you've you've named seven or eight of not counting bills,

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Speaker 1: if you've gone through a whole bunch of them. Um,

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Speaker 1: what was it like working with that crew? That is

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Speaker 1: some amazing list of advisors. Well, we did know, we

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Speaker 1: did recognize that things were going on, that this was

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Speaker 1: this was a special place. We we could see that.

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Speaker 1: I mean, it wasn't like we were surprised afterwards or something.

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Speaker 1: I guess we're surprised about the Nobel Prizes perhaps, but

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Speaker 1: we weren't surprised that we were the center of thought

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Speaker 1: leadership at that time. And and uh, one of the

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Speaker 1: things we were theoretically understanding is risk premiums. So not

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Speaker 1: not only in the stock byer where there's an equity

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Speaker 1: ris primum, but also in the bottom market where there's

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Speaker 1: an interest rate or I and risk premium that long

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Speaker 1: bonds have higher yields and short bonds, and and then

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Speaker 1: there's a default premium, the fact that that if you

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Speaker 1: buy lower grade bonds, they tend to have higher certain

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Speaker 1: higher yields, but even higher returns and higher grade bonds.

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Speaker 1: So you had all these different, uh, different risk premiums.

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Speaker 1: And that's what caused me to put this stocks, bonds, bills,

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Speaker 1: and inflation together, this data because it was really the

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Speaker 1: purpose of seeing what are what were the historical payoffs

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Speaker 1: of stocks versus bonds, stocks versus treasury bills, bonds versus

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Speaker 1: treasury bills, Treasury bills versus inflation, uh, the bonds that

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Speaker 1: could default versus treasury bonds. All these things were risk premiums.

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Speaker 1: And the purpose originally was to measure these risk premiums

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Speaker 1: and see how they had done historically because we had

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Speaker 1: the theory, yes, and it did that. Of course, they

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Speaker 1: had great payoffs. And and that's part of the reason

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Speaker 1: why it became so well known so fast, because everybody

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Speaker 1: could now see the numbers of the kinds of things

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Speaker 1: that we kind of knew were there, but we hadn't

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Speaker 1: seen the numbers. You could quantify risk and apply it

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Speaker 1: to future expected returns. Well, that's the other thing. These

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Speaker 1: were historical, but we were other paper we had, uh,

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Speaker 1: this is with Ibbotson and sink Field. The other paper

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Speaker 1: we had back in nineteen was a projection of what

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Speaker 1: would happen and how how you could go out the

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Speaker 1: next twenty five years using the last fifty years to

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Speaker 1: extrapolate out into the next twenty five years. And we're

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Speaker 1: not literally just extrapolating just the pure numbers, but we're

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Speaker 1: extrapolating the wrist premiums. And those wrist premiums were extrapolated

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Speaker 1: out to come up with these forecasts, which actually turned

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Speaker 1: out to be almost on the money forecasting what would

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Speaker 1: happen and by the year two thousand, so, of those

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Speaker 1: four stocks, bonds, bills, and inflation, which do you find

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Speaker 1: is the easiest to forecast? And which is the hardest? Well,

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Speaker 1: the easiest, well, the hardest to forecast is always the

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Speaker 1: stock market because there's so much volatility, there's so much noise.

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Speaker 1: It's a scary place to be. That's why it has

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Speaker 1: that equity risky And and what which is the easiest

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Speaker 1: to forecast? Actually it would be the UM I would

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Speaker 1: say in a derivative form, it would be the real

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Speaker 1: kind of the real interest rate, the difference between inflation

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Speaker 1: and treasury bills. But of course treasury bills, treasure bills

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Speaker 1: are mostly moving. Uh, the yields are and and bond

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Speaker 1: yields in general are mostly higher or lower because of

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Speaker 1: the expected inflation. So when you're in high inflationary periods,

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Speaker 1: you have high bond yields, and when you're in low

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Speaker 1: inflationary periods, you have low bond yields. Like we have

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Speaker 1: very low bond yields today, but we have very low inflation.

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Speaker 1: So a very large part of a body yield is

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Speaker 1: the expected inflation. And so uh, if you we know

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Speaker 1: at every point in time what the inflation is, and

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Speaker 1: that's usually a pretty good indication of what the expected

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Speaker 1: inflation is going to be. As you go further out,

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Speaker 1: you can you can't forecast it quite as well, but

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Speaker 1: at least in the near term, have a pretty good

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Speaker 1: idea of what expected inflation will be, and and therefore

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Speaker 1: you have a pretty good idea of what what what

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Speaker 1: these yields and bonds are going to be. So you're

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Speaker 1: the perfect person to ask a question about that. We've

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Speaker 1: been debating internally in the office is and back and

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Speaker 1: forth as too. Should stocks have a higher valuation these

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Speaker 1: days for a variety of reasons. But I'm gonna ask

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Speaker 1: it um differently. It costs nothing to trade today, It's

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Speaker 1: practically free. You could buy mutual funds or ETFs for

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Speaker 1: practically nothing. When you look at the historical returns and

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Speaker 1: let's call equity with dividends reinvested um, you're paying a

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Speaker 1: lot more for a portfolio. You're paying more for transactions.

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Speaker 1: This was go back before discount brokerage. It was not

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Speaker 1: cheap to buy yourself something. How does that figure into

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Speaker 1: historical returns or on? In a large enough portfolio, even

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Speaker 1: those higher prices aren't all that relevant. Well, it may

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Speaker 1: mean that the valuations should be higher because because the

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Speaker 1: trading costs are not as high anything. That's and I've

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Speaker 1: certainly studied liquidity. Risk is one one big consideration, but

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Speaker 1: liquidity is probably the second most important. Yes, the more

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Speaker 1: liquid something is, the more valuable it is. The less liquid,

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Speaker 1: the less valuable it is. And but but the counter

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Speaker 1: this course is that that something that's less liquid might

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Speaker 1: have a lower valuation but a higher expected return. Very

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Speaker 1: very very interest them. So you are currently a professor

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Speaker 1: at Yale. You spent time at the University of Chicago.

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Speaker 1: Let's talk a little bit about academia and the real world.

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Speaker 1: And you've moved pretty comfortably back and forth between the two.

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Speaker 1: What is the difference between how ideas get applied in

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Speaker 1: the business world. I probably shouldn't call it the real world,

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Speaker 1: but the business world versus the scholarly application of ideas

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Speaker 1: UM at a place like Chicago or Yale. Well, you know,

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Speaker 1: there used to be a big gap of time. I

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Speaker 1: remember like studying, as an example, duration on bonds, where

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Speaker 1: the devoted duration was developed around and and I. And

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Speaker 1: I was managing a bond portfolio for the University of

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Speaker 1: Chicago at the time, and and um I was using duration,

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Speaker 1: and I could figure out with duration, you could figure

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Speaker 1: out easily in your head how much if yield changed,

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Speaker 1: how's the price of a bond would change? And but

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Speaker 1: nobody knew that in the nineteen seventies, Yes, and and

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Speaker 1: I gave a talk on that, I remember, and and

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Speaker 1: it drew a large crowd. But it wasn't new new material.

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Speaker 1: It was merely uh stating something that was known, I

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Speaker 1: guess academically, but not known in the business world of

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Speaker 1: the real world, and so giant arbitrage opportunities. We're talking

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Speaker 1: about thirty forty years of delay before something kind of

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Speaker 1: caught on. And even though it's a very simple concept.

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Speaker 1: So uh, I think that that time though, has dramatically shrunk.

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Speaker 1: Now there is a connection, UM, all my life, I

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Speaker 1: guess I've been trying to break that connection down. In fact,

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Speaker 1: the title I have you've read these long titles that

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Speaker 1: I have at at Yale University as a professor in

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Speaker 1: in the Practice of Finance. Uh, that is a that

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Speaker 1: is a title that actually is is a professor rank

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Speaker 1: but not not tenured. But it allows me to have

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Speaker 1: business activities on the side. And so ordinarily your constraint

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Speaker 1: as a professor, you can't do a lot of business

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Speaker 1: activities on the side, but with this title I can,

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Speaker 1: and and I guess I in the end, I'd not tenured,

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Speaker 1: so I don't go to the committee meetings, and as

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Speaker 1: many as many as the committee meetings anyway, I still

399
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Speaker 1: have to go. So you're you're identifying that effectively that

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Speaker 1: bonds are misspriced relative to changes in interest rates. Was

401
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Speaker 1: there an investment opportunity to to um arbitrage those prices

402
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Speaker 1: relative to where they're supposed to be. This was a

403
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Speaker 1: course in the nineteen seventies, so not not today. I

404
00:24:48,119 --> 00:24:51,320
Speaker 1: don't think you find these kind of possibilities, but you

405
00:24:51,320 --> 00:24:54,520
Speaker 1: did find distortions in in the nineteen seventies, and so

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Speaker 1: it was one of the things I did. Now, of course,

407
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Speaker 1: the bond market is dramatically changed, much more efficient than

408
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Speaker 1: it was back then, and all the markets have become

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00:25:03,960 --> 00:25:07,080
Speaker 1: much more efficient since they were back then, because when

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Speaker 1: something has discovered, it gets the financial literature is not

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Speaker 1: something that just academics rate, it's something that that the

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Speaker 1: business world reads. And so now there are things are

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Speaker 1: almost immediately implemented. There's I don't know what the leg

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00:25:23,640 --> 00:25:26,560
Speaker 1: would be between. It wouldn't even be five years, uh

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Speaker 1: between what's discovered and was actually goes into practice. Ed

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Speaker 1: Thorpe wrote about um the arbitrage opportunity between equities and warrants,

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Speaker 1: and nobody was tracking them. There was a joint gap,

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Speaker 1: and that also took a good couple of years before

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Speaker 1: everybody else got on. And for a while it was

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Speaker 1: easy money. And then that arbitrage went away, and it

421
00:25:49,040 --> 00:25:53,040
Speaker 1: particularly went away with the black shows options model which

422
00:25:53,080 --> 00:25:56,720
Speaker 1: could price everything very accurately, from warrants to options to

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Speaker 1: any derivative relative to the underlying Yes, and and I

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Speaker 1: you know, I wasn't in at the University of Chicago

425
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Speaker 1: with Fisher Black and Myron Shows there right after they

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Speaker 1: developed it, and and before they published it. I said

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Speaker 1: to them, why don't Yes, and I suggested that, And

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Speaker 1: actually I had, I had actually bought a seat on

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Speaker 1: the cbo E had a seat on cbo E. And I, um,

430
00:26:28,920 --> 00:26:30,840
Speaker 1: I didn't go down there myself to trade, I said,

431
00:26:30,840 --> 00:26:34,800
Speaker 1: a trader down there, But but I wanted I was

432
00:26:34,840 --> 00:26:38,119
Speaker 1: trading at it. So I was trading on that, uh

433
00:26:38,200 --> 00:26:41,560
Speaker 1: for a while, using their model, using their model after

434
00:26:41,560 --> 00:26:43,960
Speaker 1: it was published. It didn't matter that it was published,

435
00:26:44,000 --> 00:26:46,760
Speaker 1: because at first the Black Shows model, with all its

436
00:26:46,800 --> 00:26:50,840
Speaker 1: long normal distributions and continuous time, was complicated enough that

437
00:26:50,880 --> 00:26:52,240
Speaker 1: you could hand it out on a piece of paper

438
00:26:52,240 --> 00:26:56,880
Speaker 1: to people nobody would get it right. But what did happen, uh,

439
00:26:57,560 --> 00:27:01,600
Speaker 1: not long after I started trading these was people figured

440
00:27:01,600 --> 00:27:04,800
Speaker 1: it out well more than that Black and So's Fisher

441
00:27:04,880 --> 00:27:09,879
Speaker 1: Black Um put it up in the members lounge of

442
00:27:09,960 --> 00:27:14,080
Speaker 1: the a computer system in the cbo E. So my

443
00:27:14,200 --> 00:27:18,080
Speaker 1: game was over. Well, let's talk a little bit about

444
00:27:18,200 --> 00:27:20,879
Speaker 1: your game and in the real world as opposed to

445
00:27:21,200 --> 00:27:24,880
Speaker 1: or in the asset management world, as opposed to the

446
00:27:24,880 --> 00:27:28,640
Speaker 1: theoretical academic world. Maybe that's a better way to describe it.

447
00:27:29,480 --> 00:27:32,840
Speaker 1: Your chairman and chief investment officer of Zebra Capital Management,

448
00:27:33,200 --> 00:27:35,600
Speaker 1: what do you do with Zebra? What sort of strategies

449
00:27:35,600 --> 00:27:38,639
Speaker 1: do you invest in? And how do you take the

450
00:27:38,720 --> 00:27:42,040
Speaker 1: academic theories that you work on and apply it to

451
00:27:42,240 --> 00:27:48,000
Speaker 1: actual management of assets and capital. We have a new monograph.

452
00:27:48,080 --> 00:27:50,199
Speaker 1: I have a new monograph with the c f A

453
00:27:50,280 --> 00:27:57,560
Speaker 1: Institute at UM. It's called Popularity, a Bridge between Classical

454
00:27:57,640 --> 00:28:02,960
Speaker 1: and Behavioral Finance. And this this monograph is co authored

455
00:28:03,000 --> 00:28:08,439
Speaker 1: with Tom Azerich and Paul Kaplan and James Song. They

456
00:28:08,480 --> 00:28:11,679
Speaker 1: are all three authors from morning Star and myself. But

457
00:28:11,760 --> 00:28:18,960
Speaker 1: it's on popularity and really popularity is the main concept,

458
00:28:19,040 --> 00:28:22,879
Speaker 1: the principle that we manage the money at Zebra Capital. Popularity.

459
00:28:23,400 --> 00:28:26,680
Speaker 1: It comes down to something you can kind of easily understand.

460
00:28:27,240 --> 00:28:29,440
Speaker 1: And of course you can get a copy of the monograph.

461
00:28:29,520 --> 00:28:31,960
Speaker 1: It's not expensive. You can buy it on Amazon for

462
00:28:32,280 --> 00:28:34,399
Speaker 1: twenty two dollars I think, and you can get it

463
00:28:34,680 --> 00:28:37,720
Speaker 1: downloaded for free. I think the I c f A institute.

464
00:28:38,680 --> 00:28:41,240
Speaker 1: So see if they institute dot org here it is

465
00:28:41,640 --> 00:28:44,080
Speaker 1: perhaps you have to be a member. I have to

466
00:28:44,120 --> 00:28:48,920
Speaker 1: be a um A see if a member in some

467
00:28:49,000 --> 00:28:52,440
Speaker 1: form to to get the access to it. But it's

468
00:28:52,440 --> 00:28:56,240
Speaker 1: free for the members membership anyway. So so when you

469
00:28:56,280 --> 00:28:59,960
Speaker 1: talk about popularity, what does that mean in actual tone

470
00:29:00,040 --> 00:29:04,440
Speaker 1: arms of of investment? How do you apply popularity to

471
00:29:05,320 --> 00:29:12,920
Speaker 1: deploying capital? So a anything that is popular tends to

472
00:29:12,960 --> 00:29:17,560
Speaker 1: have a higher price. Anything that is unpopular tends to

473
00:29:17,600 --> 00:29:19,880
Speaker 1: have a lower price. So I think of two two

474
00:29:19,920 --> 00:29:22,560
Speaker 1: assets that have the same cash flows, but one of

475
00:29:22,600 --> 00:29:25,160
Speaker 1: them is popular, that will be more expensive than one

476
00:29:25,160 --> 00:29:29,040
Speaker 1: that's unpopular. That's right. So are you long the unpopular,

477
00:29:29,080 --> 00:29:31,560
Speaker 1: short the popular or how do you how do you

478
00:29:31,760 --> 00:29:35,640
Speaker 1: do that trade? You're ready, You're ready to go here? Okay,

479
00:29:35,920 --> 00:29:38,200
Speaker 1: it makes sense. So it's a hedge position, and you're

480
00:29:38,600 --> 00:29:40,440
Speaker 1: even if they both go up, the theory is the

481
00:29:40,520 --> 00:29:42,640
Speaker 1: cheaper one will go up more than the expensive one

482
00:29:43,240 --> 00:29:45,280
Speaker 1: UM or vice versa. If they both go down, the

483
00:29:45,320 --> 00:29:47,360
Speaker 1: cheaper one will go down less than the expensive one.

484
00:29:47,960 --> 00:29:52,840
Speaker 1: UM And you do this across how many different asset classes?

485
00:29:52,920 --> 00:29:56,880
Speaker 1: Is just equities not no bonds, no bills. At Zaber Capital,

486
00:29:56,920 --> 00:29:59,320
Speaker 1: we just do it with equities. But the concept applies

487
00:29:59,720 --> 00:30:02,880
Speaker 1: to anything, and and and and it's really getting to

488
00:30:02,880 --> 00:30:08,240
Speaker 1: people to think differently about capital markets because, for example,

489
00:30:08,280 --> 00:30:12,520
Speaker 1: in the capitalistic pricing model, it's systematic risk or beta

490
00:30:12,640 --> 00:30:19,040
Speaker 1: risk that's that's unpopular. And therefore any any stock that

491
00:30:19,120 --> 00:30:23,000
Speaker 1: has high systematic risk high beta risk is supposed to

492
00:30:23,080 --> 00:30:27,000
Speaker 1: have a higher expected return according to the capitalized pricing model.

493
00:30:27,080 --> 00:30:29,600
Speaker 1: But I sense you disagree with that. I don't necessarily

494
00:30:29,640 --> 00:30:32,880
Speaker 1: disagree with that. But that's only one aspect of popularity.

495
00:30:33,440 --> 00:30:37,200
Speaker 1: Risk is unpopular. But there are a lot of other

496
00:30:37,240 --> 00:30:40,120
Speaker 1: things that are popular or unpopular, and one of them

497
00:30:40,120 --> 00:30:43,120
Speaker 1: we've already talked about um I talked about in an

498
00:30:43,200 --> 00:30:47,440
Speaker 1: earlier episode. Here we I talked about liquidity. Yes, so

499
00:30:48,960 --> 00:30:53,240
Speaker 1: liquidity is popular. Pay do you pay a premium for

500
00:30:53,440 --> 00:30:56,640
Speaker 1: more liquid stocks than in liquid stocks, or said differently,

501
00:30:56,640 --> 00:31:00,360
Speaker 1: do you get a discount for in liquid stocks? Yes?

502
00:31:01,080 --> 00:31:04,960
Speaker 1: And and of course it's also obviously true in other markets,

503
00:31:05,160 --> 00:31:07,840
Speaker 1: and you can see it more directly in the bond market,

504
00:31:07,920 --> 00:31:11,200
Speaker 1: for example, or at the most extreme case, think of

505
00:31:11,240 --> 00:31:15,160
Speaker 1: the treasury yield. There's an on the run treasury yield

506
00:31:16,040 --> 00:31:20,080
Speaker 1: where you buy the most liquid liquid security with a

507
00:31:20,120 --> 00:31:23,320
Speaker 1: certain maturity, and then there's an off the run and

508
00:31:23,360 --> 00:31:26,080
Speaker 1: there's a there's a spread out between those of ten

509
00:31:26,160 --> 00:31:28,800
Speaker 1: or twenty basis points between the on the run and

510
00:31:28,840 --> 00:31:31,000
Speaker 1: the off the run yields. So it's just a matter

511
00:31:31,040 --> 00:31:33,880
Speaker 1: of one being more liquid than the other. And we're

512
00:31:33,920 --> 00:31:37,200
Speaker 1: not talking about much difference here. The on the run

513
00:31:37,320 --> 00:31:41,120
Speaker 1: things maybe trading every minute, every few seconds, even whereas

514
00:31:41,120 --> 00:31:44,880
Speaker 1: the off the run might be trading every few minutes.

515
00:31:45,000 --> 00:31:49,560
Speaker 1: You know, but even small amounts of liquidity might might

516
00:31:49,720 --> 00:31:53,280
Speaker 1: make differences in how you get into differences of expected return.

517
00:31:53,520 --> 00:31:56,160
Speaker 1: So is that one element of the thinking that goes

518
00:31:56,200 --> 00:32:00,960
Speaker 1: into the portfolio or do you similarly create paired trades

519
00:32:01,040 --> 00:32:03,960
Speaker 1: with the liquid versus in the liquid stock you're buying

520
00:32:04,000 --> 00:32:06,720
Speaker 1: something at a discount and pit and shorting something at

521
00:32:06,720 --> 00:32:08,600
Speaker 1: a premium. And I don't want to ask you to

522
00:32:08,600 --> 00:32:11,240
Speaker 1: give away all your secrets, but I'm trying to get

523
00:32:11,240 --> 00:32:13,840
Speaker 1: a handle on on how you use these different aspects

524
00:32:13,840 --> 00:32:16,840
Speaker 1: of popularity. Well, they're not paired because we're just a

525
00:32:16,840 --> 00:32:20,680
Speaker 1: whole bundle of UH stocks on alongside that are that

526
00:32:20,800 --> 00:32:24,800
Speaker 1: are less popular and UH how bundle of another stocks

527
00:32:25,440 --> 00:32:28,400
Speaker 1: of stocks that are more popular. But there are many

528
00:32:28,400 --> 00:32:33,000
Speaker 1: different dimensions to be popular. We've already mentioned too, and

529
00:32:33,680 --> 00:32:36,400
Speaker 1: that's the capitalist surprise. Amodel really focused on that first one,

530
00:32:36,520 --> 00:32:40,760
Speaker 1: risk and and the whole financial literature has so much

531
00:32:40,840 --> 00:32:46,920
Speaker 1: focused on risk UM too much focus well, perhaps because

532
00:32:46,920 --> 00:32:50,080
Speaker 1: there are other aspects of things that are popular and unpopular.

533
00:32:50,200 --> 00:32:54,640
Speaker 1: So we mentioned valuation, we mentioned capem, we mentioned liquidly.

534
00:32:54,960 --> 00:32:58,560
Speaker 1: What other elements UM do you consider when when looking

535
00:32:58,640 --> 00:33:03,080
Speaker 1: at your popular this is unpopular. Let's not hold pairs

536
00:33:03,160 --> 00:33:08,040
Speaker 1: but pools of lungs and shorts. So the monograph that

537
00:33:08,080 --> 00:33:13,560
Speaker 1: we're talking about says popularity a bridge between classical and

538
00:33:13,680 --> 00:33:18,600
Speaker 1: behavioral finance. So some of the behavioral factors come up also,

539
00:33:18,680 --> 00:33:21,080
Speaker 1: how do they apply? So let me give you one

540
00:33:21,080 --> 00:33:23,840
Speaker 1: more though classical. I guess if I had a name

541
00:33:23,960 --> 00:33:28,600
Speaker 1: three major the three big classical UH considerations. One of

542
00:33:28,600 --> 00:33:31,680
Speaker 1: them misrisk and it might be systematic, but it can

543
00:33:31,680 --> 00:33:35,000
Speaker 1: have all different dimensions of different types of risk. Another

544
00:33:35,000 --> 00:33:39,880
Speaker 1: one is liquidity. Another one is taxation. Some securities are

545
00:33:39,960 --> 00:33:44,360
Speaker 1: tax differently than others, so different tax treatments between different

546
00:33:44,360 --> 00:33:49,480
Speaker 1: types of UM passed through holdings. What is the third one,

547
00:33:49,520 --> 00:33:52,520
Speaker 1: tax treatment. At the most extreme you can see it

548
00:33:53,080 --> 00:33:56,239
Speaker 1: with if you want to see it directly, would be

549
00:33:56,440 --> 00:33:59,600
Speaker 1: compare a municipal bond to say a corporate bond of

550
00:33:59,680 --> 00:34:04,000
Speaker 1: the same default and maturity characteristics. The municipal bond is

551
00:34:04,000 --> 00:34:08,600
Speaker 1: going to have a lower yield, but it's going to

552
00:34:08,719 --> 00:34:12,200
Speaker 1: have that's because it's popular. It has better tax treatment.

553
00:34:12,480 --> 00:34:17,200
Speaker 1: MLPs are treated differently than regular equities according to the taxman,

554
00:34:17,680 --> 00:34:21,040
Speaker 1: and that changes how you perceive them in the overall portfolio.

555
00:34:21,480 --> 00:34:24,840
Speaker 1: Similar Tommuni bonds, which were also treated differently. A lower

556
00:34:24,920 --> 00:34:29,960
Speaker 1: yield effectively net of taxes, turns out to be comparable

557
00:34:30,080 --> 00:34:33,480
Speaker 1: to a non municipal bond. So things that are unpopular

558
00:34:33,640 --> 00:34:40,839
Speaker 1: like MLPs are gonna be priced more attractively quite quite fascinating.

559
00:34:41,080 --> 00:34:44,279
Speaker 1: So that's just on the on the classical side. But

560
00:34:44,440 --> 00:34:47,600
Speaker 1: then you have the behavioral side. And on the behavioral

561
00:34:47,640 --> 00:34:50,080
Speaker 1: side you have things like one of the things we

562
00:34:50,200 --> 00:34:55,719
Speaker 1: tested in this monograph were brands uh brand value the companies,

563
00:34:55,960 --> 00:34:59,480
Speaker 1: and there are there's different ranking systems of how valuable

564
00:34:59,520 --> 00:35:02,320
Speaker 1: different bands are, but the companies that have the highest

565
00:35:02,600 --> 00:35:06,040
Speaker 1: brand value, you might imagine those are the companies you

566
00:35:06,080 --> 00:35:08,560
Speaker 1: would like in your portfolio, right, No, it would be

567
00:35:08,600 --> 00:35:11,359
Speaker 1: the opposite. I know where you're going. I'm cheating because

568
00:35:11,400 --> 00:35:14,719
Speaker 1: I know where you're going. But perfect example in in

569
00:35:14,760 --> 00:35:16,560
Speaker 1: the beginning of the year, Apple was one of the

570
00:35:16,640 --> 00:35:19,040
Speaker 1: time it was either one or two in the brand

571
00:35:19,080 --> 00:35:23,319
Speaker 1: ranking down since then. And I don't know how far

572
00:35:23,400 --> 00:35:26,759
Speaker 1: down the list of unpopular brands you go. Um, but

573
00:35:26,800 --> 00:35:29,400
Speaker 1: I would imagine there's a line that in the sand

574
00:35:29,440 --> 00:35:32,320
Speaker 1: where you say, on a list of five hundred, below

575
00:35:32,360 --> 00:35:35,960
Speaker 1: this number, these unpopular brands start to become attractive. So

576
00:35:36,160 --> 00:35:39,520
Speaker 1: first question on that is where is that line? How

577
00:35:39,560 --> 00:35:42,160
Speaker 1: how unpopular is something you have to get before it

578
00:35:42,200 --> 00:35:45,440
Speaker 1: becomes attractive. It's always relative. I mean you wouldn't you

579
00:35:45,520 --> 00:35:49,520
Speaker 1: want to short the most popular names, uh, the most

580
00:35:49,520 --> 00:35:52,879
Speaker 1: popular brands and and really the unpopular brands. You don't

581
00:35:52,920 --> 00:35:56,440
Speaker 1: have to go after the brands that are in some

582
00:35:56,480 --> 00:36:00,160
Speaker 1: sort of scandal or anything. But I meanly, most these

583
00:36:00,160 --> 00:36:02,160
Speaker 1: brands you've never heard of, you know, they're not they're

584
00:36:02,160 --> 00:36:05,040
Speaker 1: not well known brands. So not ge that's run into

585
00:36:05,080 --> 00:36:08,200
Speaker 1: hard times, but some entity that just seems to have

586
00:36:08,200 --> 00:36:11,440
Speaker 1: fallen out of favor, yes, or just or just overlooked.

587
00:36:11,840 --> 00:36:15,160
Speaker 1: Things that are overlooked the stocks that are overlooked around

588
00:36:15,160 --> 00:36:18,040
Speaker 1: popular stocks that are in the news all the time

589
00:36:18,239 --> 00:36:22,040
Speaker 1: are the popular stocks, sure now, um and and the

590
00:36:22,120 --> 00:36:25,960
Speaker 1: value effect is related to this because, uh stocks, you know,

591
00:36:26,040 --> 00:36:31,120
Speaker 1: value over the long term outperforms growth. But the value companies,

592
00:36:31,120 --> 00:36:33,960
Speaker 1: when you look at those things, those are not great companies,

593
00:36:34,120 --> 00:36:36,960
Speaker 1: whereas the growth companies are the companies that are really

594
00:36:37,440 --> 00:36:41,240
Speaker 1: much more exciting, much better companies. But there's a difference

595
00:36:41,320 --> 00:36:47,360
Speaker 1: between buying a great company and buying a great stock.

596
00:36:48,120 --> 00:36:51,400
Speaker 1: The great stocks are not the great companies the great stocks.

597
00:36:52,400 --> 00:36:55,719
Speaker 1: In fact, it's easier to fix a company that has

598
00:36:55,800 --> 00:36:59,960
Speaker 1: something wrong with that than to improve a company that's

599
00:37:00,239 --> 00:37:04,279
Speaker 1: everything is going great but just isn't popular. Well, well,

600
00:37:04,280 --> 00:37:06,200
Speaker 1: the ones that are great, they're they're too popular. That's

601
00:37:06,200 --> 00:37:10,400
Speaker 1: their problems. So give us one more behavioral measure to

602
00:37:10,440 --> 00:37:16,960
Speaker 1: consider with popularity. Well, another one we measured was reputation value. Well,

603
00:37:17,000 --> 00:37:18,640
Speaker 1: I'll give you another one that's a little more different.

604
00:37:18,719 --> 00:37:23,760
Speaker 1: Is tail risk. Any any stock that has actually gone

605
00:37:23,800 --> 00:37:29,040
Speaker 1: through a h a tail event, a negative event that's

606
00:37:29,040 --> 00:37:34,120
Speaker 1: called in its history recent history, last five ten years. Uh.

607
00:37:34,320 --> 00:37:36,960
Speaker 1: So let's talk about BP and the Gulf of Mexico.

608
00:37:37,080 --> 00:37:41,400
Speaker 1: We're Boeing and the seven thirty seven max. Uh, those

609
00:37:41,440 --> 00:37:44,279
Speaker 1: are kind of tail events. What does that tell you

610
00:37:44,320 --> 00:37:48,080
Speaker 1: about how you would expect the stock to do going forward?

611
00:37:48,600 --> 00:37:52,880
Speaker 1: Are they is a stock like Boeing post seven seven

612
00:37:52,880 --> 00:37:56,800
Speaker 1: issue become attractive because of that tail event? It can

613
00:37:56,880 --> 00:38:00,439
Speaker 1: and not necessarily the next month or so, But people

614
00:38:00,480 --> 00:38:02,439
Speaker 1: are going to remember that tale for a long time.

615
00:38:03,360 --> 00:38:06,280
Speaker 1: They're gonna remember that bad event, and and it's gonna

616
00:38:06,320 --> 00:38:12,279
Speaker 1: be the reputation is somewhat permanently damaged. Uh. But what

617
00:38:12,520 --> 00:38:15,200
Speaker 1: we have actually studied this uh with one of my

618
00:38:15,239 --> 00:38:17,440
Speaker 1: co authors that I named James Schong here or we

619
00:38:17,480 --> 00:38:21,920
Speaker 1: looked at tail risk and it turns out that future

620
00:38:21,960 --> 00:38:26,600
Speaker 1: tail risk is not so related to past tail events. Huh.

621
00:38:26,840 --> 00:38:30,840
Speaker 1: That's interesting. So it's something really went wrong, say Boeing.

622
00:38:31,320 --> 00:38:35,400
Speaker 1: They eventually fix it and get back on track with

623
00:38:35,440 --> 00:38:39,160
Speaker 1: a lower reputation, but they're not necessarily prone to have

624
00:38:39,640 --> 00:38:45,160
Speaker 1: another tail event. Quite fascinating. We were talking earlier about

625
00:38:45,920 --> 00:38:51,560
Speaker 1: the difference between academia and real world application of ideas

626
00:38:51,760 --> 00:38:56,440
Speaker 1: into actual investments and trades. How often you come across

627
00:38:56,480 --> 00:38:59,560
Speaker 1: an idea that looks great on paper but just doesn't work.

628
00:39:00,120 --> 00:39:04,400
Speaker 1: Can't be implemented UM in a real portfolio. The problem

629
00:39:04,480 --> 00:39:08,320
Speaker 1: with real portfolios is, as they have a lot of trading,

630
00:39:08,360 --> 00:39:09,960
Speaker 1: they may have trading costs. A lot of the very

631
00:39:09,960 --> 00:39:14,520
Speaker 1: best ideas have high trading costs, and and most of

632
00:39:14,600 --> 00:39:17,919
Speaker 1: us are not in a position to actually implement these

633
00:39:19,160 --> 00:39:21,719
Speaker 1: and get rid of rid of those trading costs. There's

634
00:39:21,760 --> 00:39:24,840
Speaker 1: another another problem, though, and that is that the kinds

635
00:39:24,880 --> 00:39:28,040
Speaker 1: of things that have worked in the past, you look

636
00:39:28,080 --> 00:39:30,400
Speaker 1: at it, you do some back testing, and you discover

637
00:39:30,520 --> 00:39:34,520
Speaker 1: things in the past. Uh, those sorts of things, once

638
00:39:34,520 --> 00:39:39,080
Speaker 1: they're discovered and known, tend to be heavily invested in

639
00:39:39,600 --> 00:39:42,960
Speaker 1: get popular actually, and once they get popular, they get

640
00:39:42,960 --> 00:39:46,360
Speaker 1: priced out out of the out of contention, and and

641
00:39:46,440 --> 00:39:48,640
Speaker 1: they don't have the same sort of payoffs. So so

642
00:39:48,920 --> 00:39:52,920
Speaker 1: it's uh no longer as easy to translate all these

643
00:39:53,080 --> 00:39:56,000
Speaker 1: ideas that come out of academics and actually make a

644
00:39:56,000 --> 00:40:00,080
Speaker 1: lot of money from them. Quite quite interesting. Um. One

645
00:40:00,120 --> 00:40:03,120
Speaker 1: of the big topics these days is index funds and

646
00:40:03,120 --> 00:40:07,319
Speaker 1: the move away from active towards passive. Where do you

647
00:40:07,360 --> 00:40:10,840
Speaker 1: find yourself in terms of Some people seem to think

648
00:40:11,200 --> 00:40:14,160
Speaker 1: the flow into index funds is distorting the market, or

649
00:40:14,200 --> 00:40:19,600
Speaker 1: at least hurting price discovery. What are we index in

650
00:40:19,640 --> 00:40:23,799
Speaker 1: the US? What's the impact of that? The I don't

651
00:40:23,840 --> 00:40:27,080
Speaker 1: actually think it's hurting markets. Actually, the index has come

652
00:40:27,080 --> 00:40:29,160
Speaker 1: in so many different forms. You can buy a whole

653
00:40:29,160 --> 00:40:32,200
Speaker 1: index on say the Russell three thousand or the SP five.

654
00:40:32,880 --> 00:40:36,160
Speaker 1: But you can also buy indexes in different forms and

655
00:40:36,239 --> 00:40:41,040
Speaker 1: e t fs and and even mutual funds on by

656
00:40:41,040 --> 00:40:45,680
Speaker 1: a specific industry, or by value stocks, or by small

657
00:40:45,719 --> 00:40:49,520
Speaker 1: cap stocks, or buy a particular type of stock. So

658
00:40:50,120 --> 00:40:54,240
Speaker 1: it's if it's really taking you're actually taking a bat

659
00:40:54,239 --> 00:40:57,600
Speaker 1: when you actually buy that type of stock. If you

660
00:40:57,760 --> 00:41:01,520
Speaker 1: if you thought, for example, that that oil or energy

661
00:41:01,600 --> 00:41:04,160
Speaker 1: was going to do poorly in the future. In the

662
00:41:04,160 --> 00:41:07,439
Speaker 1: old days, you'd have to how do I translate under

663
00:41:07,480 --> 00:41:10,960
Speaker 1: which stocks to avoid and so forth. It's much easier

664
00:41:10,960 --> 00:41:13,680
Speaker 1: to express the trade today, Yes, and that makes it

665
00:41:13,880 --> 00:41:17,480
Speaker 1: markets more efficient because if we can target our opinions

666
00:41:17,800 --> 00:41:22,680
Speaker 1: into a trade directly, rather than trying to mix it

667
00:41:22,719 --> 00:41:25,920
Speaker 1: in with some stocks that that are really noisy and

668
00:41:25,920 --> 00:41:28,960
Speaker 1: not so related to that trade. Uh, it makes it

669
00:41:29,040 --> 00:41:32,440
Speaker 1: very hard to accomplish. So one of the things I

670
00:41:32,520 --> 00:41:36,520
Speaker 1: keep hearing from you is it was much easier back then.

671
00:41:36,640 --> 00:41:39,640
Speaker 1: It's more efficient today. Is this part of the reason

672
00:41:39,719 --> 00:41:43,000
Speaker 1: why so many active managers seem to be unable to

673
00:41:43,080 --> 00:41:46,840
Speaker 1: keep up with their benchmark? Have the markets become that efficient?

674
00:41:48,640 --> 00:41:52,839
Speaker 1: By definition? Though, the market is a zero sum game

675
00:41:52,920 --> 00:41:56,719
Speaker 1: before costs, in the sense that if if you're going

676
00:41:56,719 --> 00:41:58,839
Speaker 1: to beat the market, I have to do worse than

677
00:41:58,840 --> 00:42:01,440
Speaker 1: the market, or we have to sum up to the

678
00:42:01,480 --> 00:42:05,759
Speaker 1: market collectively, and that's before costs, and after costs, Uh,

679
00:42:06,080 --> 00:42:08,600
Speaker 1: we're gonna on average do a little bit worse than

680
00:42:08,600 --> 00:42:12,839
Speaker 1: the market. So this isn't really efficient capital markets. This

681
00:42:12,960 --> 00:42:17,879
Speaker 1: is just a mathematical identity. It's called a zero sum game,

682
00:42:18,520 --> 00:42:21,799
Speaker 1: and in this case the zero relative sum game because

683
00:42:21,840 --> 00:42:25,520
Speaker 1: it's relative to the market, so we could we never

684
00:42:25,600 --> 00:42:29,399
Speaker 1: could collectively on average beat the market. We always had

685
00:42:29,440 --> 00:42:33,080
Speaker 1: to have most of us, um actually do a little

686
00:42:33,080 --> 00:42:35,160
Speaker 1: bit worse than the market about average. So it's not

687
00:42:35,280 --> 00:42:39,000
Speaker 1: like Lake Wobegone, where um, all the children are above average.

688
00:42:39,000 --> 00:42:42,680
Speaker 1: It doesn't work that way. Well, that's that's a good analogy,

689
00:42:42,760 --> 00:42:46,120
Speaker 1: because that's what keeps the market going. We all think

690
00:42:46,160 --> 00:42:49,520
Speaker 1: we're above average, and if we often this is behavioral course.

691
00:42:49,920 --> 00:42:52,279
Speaker 1: But but if we all think we're above average, then

692
00:42:53,120 --> 00:42:56,080
Speaker 1: then of course we can We're all willing to play

693
00:42:56,120 --> 00:42:58,840
Speaker 1: the game, even though some of us are clearly not

694
00:42:58,960 --> 00:43:02,239
Speaker 1: above average, and by definition all of us can be

695
00:43:02,280 --> 00:43:06,200
Speaker 1: above average. My my favorite question, anytime I'm at a

696
00:43:06,280 --> 00:43:09,160
Speaker 1: conference and presenting somewhere, I always ask a room full

697
00:43:09,200 --> 00:43:12,640
Speaker 1: of people, how many of you are above average? Drivers?

698
00:43:12,680 --> 00:43:14,719
Speaker 1: Just about every hand in the room goes up, and

699
00:43:14,800 --> 00:43:18,200
Speaker 1: my response is always, I've been on the road with you, folks,

700
00:43:18,239 --> 00:43:21,239
Speaker 1: some of you are wrong, because clearly we can all

701
00:43:21,239 --> 00:43:25,240
Speaker 1: be above average. So let's talk a little bit about um.

702
00:43:25,239 --> 00:43:29,920
Speaker 1: What What was discussed in Forward Thinker, which was something

703
00:43:29,960 --> 00:43:33,840
Speaker 1: you wrote for Wealth Manager back in December eight You

704
00:43:34,000 --> 00:43:39,000
Speaker 1: talked about the financial system being restructured UH following the

705
00:43:39,040 --> 00:43:44,440
Speaker 1: financial crisis. Have we sufficiently restructured the system to avoid

706
00:43:44,480 --> 00:43:48,160
Speaker 1: the next crisis? Or did we simply restructured enough to

707
00:43:48,200 --> 00:43:51,960
Speaker 1: avoid a repeat of the last crisis. It would be

708
00:43:52,000 --> 00:43:58,440
Speaker 1: mostly a repeat of the last crisis. Restructuring primarily involves

709
00:43:59,120 --> 00:44:06,000
Speaker 1: having UH less leverage and and and actually we're also

710
00:44:06,080 --> 00:44:09,680
Speaker 1: worried about counterparty risk. And of course some of that

711
00:44:09,719 --> 00:44:14,840
Speaker 1: counterparty risk has been been fixed. We're getting more leverage

712
00:44:14,880 --> 00:44:17,799
Speaker 1: back into the system probably now. Truly it's creeping back

713
00:44:17,840 --> 00:44:20,480
Speaker 1: in Yeah, so I don't. I don't see these were

714
00:44:20,520 --> 00:44:25,160
Speaker 1: as any permanent fixes. And of course anytime you regulate markets,

715
00:44:25,400 --> 00:44:29,360
Speaker 1: you also add a lot of incumbrances to the way

716
00:44:29,440 --> 00:44:32,239
Speaker 1: to the way the markets work. So it's both good

717
00:44:32,280 --> 00:44:36,520
Speaker 1: and bad here that it's um where I don't see

718
00:44:36,560 --> 00:44:42,640
Speaker 1: a uh, financial crisis coming soon. Most recessions, by the way, um,

719
00:44:42,880 --> 00:44:45,120
Speaker 1: most drops in the market are not really a financial

720
00:44:45,120 --> 00:44:48,160
Speaker 1: crisis crisis. But the one in two thousand and eight

721
00:44:48,320 --> 00:44:52,319
Speaker 1: was like like the tech public crash in in two

722
00:44:52,320 --> 00:44:55,520
Speaker 1: thousands or two thousand and two. Financial crisis wasn't to

723
00:44:55,600 --> 00:44:59,040
Speaker 1: do with finance. It's just technology, right right, Speaking of behavior,

724
00:44:59,239 --> 00:45:04,919
Speaker 1: excess sentiment had gotten so enthusiastic, people decided evaluation doesn't matter.

725
00:45:04,960 --> 00:45:08,080
Speaker 1: You could pay any price for these things nifty fifty

726
00:45:08,120 --> 00:45:11,720
Speaker 1: like it will all work out, except when eventually doesn't.

727
00:45:11,800 --> 00:45:18,400
Speaker 1: So clearly not a financial crisis. Seven. How do you

728
00:45:18,440 --> 00:45:21,279
Speaker 1: describe that not a financial crisis, really a plumbing and

729
00:45:21,320 --> 00:45:24,319
Speaker 1: structural issue. Well, it was a financial crisis, it's just

730
00:45:24,440 --> 00:45:28,040
Speaker 1: that it only lasted about a week, so not much.

731
00:45:28,360 --> 00:45:30,480
Speaker 1: That's not much of a crisis. And I think back

732
00:45:30,520 --> 00:45:34,120
Speaker 1: to seventy four, so just a deep recession not a

733
00:45:34,160 --> 00:45:38,759
Speaker 1: financial crisis. The fifties predate me, but when you read

734
00:45:38,800 --> 00:45:42,920
Speaker 1: the history books those late fifties early sixties recessions, they

735
00:45:42,960 --> 00:45:44,680
Speaker 1: don't seem like you have to go all the way

736
00:45:44,680 --> 00:45:47,640
Speaker 1: back to what ninety nine for a previous financial crisis.

737
00:45:48,640 --> 00:45:51,600
Speaker 1: Nineties were financial crisis. So we had the big one,

738
00:45:51,640 --> 00:45:55,040
Speaker 1: then we had the smaller one, and are the limited

739
00:45:55,040 --> 00:45:59,960
Speaker 1: one in seven and we had a potentially severe financial crisis,

740
00:46:00,040 --> 00:46:06,600
Speaker 1: said oh eight, and we got through it, which was um,

741
00:46:06,719 --> 00:46:10,800
Speaker 1: very fortunate, but it was the potential financial real breakdown

742
00:46:10,800 --> 00:46:13,200
Speaker 1: of financial markets. So that leads to the next question.

743
00:46:13,239 --> 00:46:17,200
Speaker 1: People have been some people, especially people on the bond

744
00:46:17,239 --> 00:46:21,759
Speaker 1: side of the universe, have been very critical about how

745
00:46:21,800 --> 00:46:24,960
Speaker 1: the FED intervened in the markets, and they've continued to

746
00:46:25,000 --> 00:46:28,719
Speaker 1: be critical about low rates being as low as they've been,

747
00:46:29,480 --> 00:46:32,719
Speaker 1: causing risk assets to rise. What are your thoughts on

748
00:46:32,760 --> 00:46:36,880
Speaker 1: the job the FED did during the financial crisis, and um,

749
00:46:36,920 --> 00:46:38,960
Speaker 1: how do you think the FED has done since then?

750
00:46:39,800 --> 00:46:43,719
Speaker 1: I think during the financial crisis, the FED really had

751
00:46:43,760 --> 00:46:46,000
Speaker 1: to do some special things that they hadn't done. Had

752
00:46:46,040 --> 00:46:48,200
Speaker 1: to buil up their balance sheet, which they had really

753
00:46:48,239 --> 00:46:50,279
Speaker 1: never done before. Used to be the FED was just

754
00:46:50,520 --> 00:46:55,480
Speaker 1: raising and lowering the uh, the interest rate, and um

755
00:46:55,640 --> 00:46:59,040
Speaker 1: that was not that didn't do anything after you got

756
00:46:59,040 --> 00:47:02,319
Speaker 1: down to zero interest or so they then had to

757
00:47:02,320 --> 00:47:04,520
Speaker 1: build up their balance sheets. So I think they Fed

758
00:47:05,280 --> 00:47:08,920
Speaker 1: UH did a great job, in my opinion, in helping

759
00:47:08,920 --> 00:47:14,200
Speaker 1: to avoid that another great suppression, a real catastrophe. Definitely,

760
00:47:14,520 --> 00:47:16,920
Speaker 1: you think it could have been. I think people have forgotten.

761
00:47:16,960 --> 00:47:20,359
Speaker 1: It's been a decade and they've already forgotten. Were we

762
00:47:20,520 --> 00:47:25,279
Speaker 1: on the precipice of another depression similar to the nine

763
00:47:25,320 --> 00:47:29,200
Speaker 1: and the nineties. We were on the precipice of a

764
00:47:29,239 --> 00:47:33,920
Speaker 1: financial breakdown. I don't know that it would cause a depression,

765
00:47:34,040 --> 00:47:38,759
Speaker 1: but it would cause a lot of chaos and certainly, um,

766
00:47:39,440 --> 00:47:41,239
Speaker 1: I'm not clear what it would cause, you know, but

767
00:47:41,560 --> 00:47:45,440
Speaker 1: we were definitely on the precipice of that financial breakdown,

768
00:47:45,440 --> 00:47:48,879
Speaker 1: which could have been very severe. I As far as

769
00:47:49,000 --> 00:47:51,919
Speaker 1: what the FED has done since then, well I think

770
00:47:51,960 --> 00:47:56,360
Speaker 1: they could have. We were now ten years later and

771
00:47:56,400 --> 00:48:00,400
Speaker 1: we haven't. Were now interest rates less than the window

772
00:48:00,719 --> 00:48:03,879
Speaker 1: the FED window. They're really only charging less than three

773
00:48:04,719 --> 00:48:07,839
Speaker 1: at this point. UH. One of the one of the

774
00:48:07,920 --> 00:48:11,040
Speaker 1: levels the FED has to protect against recessions is the

775
00:48:11,080 --> 00:48:13,560
Speaker 1: lower the rate we can't lower the rate if we

776
00:48:13,640 --> 00:48:16,800
Speaker 1: never royals it, and so I think they could have

777
00:48:16,840 --> 00:48:19,359
Speaker 1: done much more, but they were very reluctant to raise

778
00:48:19,480 --> 00:48:23,399
Speaker 1: rates over this long recovery, which has been now very

779
00:48:23,960 --> 00:48:28,279
Speaker 1: long tenure recovery. And so we still don't have normalized

780
00:48:28,360 --> 00:48:31,040
Speaker 1: interest rates, and the FED is are they behind the

781
00:48:31,080 --> 00:48:33,840
Speaker 1: curve or have they just not gotten us to a

782
00:48:33,880 --> 00:48:38,839
Speaker 1: place that resembles neutral? Yet the rates are still low,

783
00:48:39,000 --> 00:48:44,160
Speaker 1: and and although they are finally reducing the balance sheet

784
00:48:44,160 --> 00:48:46,920
Speaker 1: of the FAT, it's still a big balance sheet. So

785
00:48:47,560 --> 00:48:51,960
Speaker 1: they don't have the same firepower going into any new

786
00:48:52,520 --> 00:48:55,520
Speaker 1: activity that happens that we had back in oh eight.

787
00:48:56,120 --> 00:48:59,040
Speaker 1: Quite interesting, So let's talk a little bit about annuities

788
00:48:59,680 --> 00:49:03,680
Speaker 1: um which are somewhat controversial to some people. I don't

789
00:49:03,680 --> 00:49:05,960
Speaker 1: know if that's really the right descriptor of it. You

790
00:49:06,040 --> 00:49:10,839
Speaker 1: wrote not too long ago, investors should consider indexed anuities

791
00:49:11,000 --> 00:49:14,800
Speaker 1: as bond substitutes. Explain what those are and how would

792
00:49:14,800 --> 00:49:18,520
Speaker 1: that operate in a in a portfolio. Well, let me

793
00:49:18,520 --> 00:49:23,399
Speaker 1: first explain why we might need a bond substitute. Uh,

794
00:49:23,440 --> 00:49:27,080
Speaker 1: if you bonds today at least treasure yields are below

795
00:49:27,160 --> 00:49:31,120
Speaker 1: three percent the tenure I looked lass. I looked recently,

796
00:49:31,160 --> 00:49:36,200
Speaker 1: the tenure was definitely below three percent. And and uh,

797
00:49:36,680 --> 00:49:40,319
Speaker 1: a return on a bond is the yield plus or

798
00:49:40,400 --> 00:49:45,400
Speaker 1: minus a capital gainer loss. If yields rise, you have

799
00:49:45,640 --> 00:49:49,080
Speaker 1: a capital loss on bonds. And I mentioned duration at

800
00:49:49,080 --> 00:49:52,600
Speaker 1: an earlier time. The essentially, if he yields rise one

801
00:49:52,600 --> 00:49:56,000
Speaker 1: percent and the duration is ten on a bond, you're

802
00:49:56,120 --> 00:50:01,719
Speaker 1: you lose ten on that bond. So there was a

803
00:50:01,760 --> 00:50:06,120
Speaker 1: potential for actually bonds that have negative returns in the future. Uh,

804
00:50:06,280 --> 00:50:08,680
Speaker 1: if you look at the history of all these yields,

805
00:50:09,360 --> 00:50:13,080
Speaker 1: we from the fourties on we had very low yields

806
00:50:13,120 --> 00:50:17,799
Speaker 1: that rose and one they were double digits, and but

807
00:50:17,960 --> 00:50:21,000
Speaker 1: since then they've been straight down thirty three years of

808
00:50:21,040 --> 00:50:25,000
Speaker 1: falling interest rates. And that meant really good returns because

809
00:50:25,040 --> 00:50:27,000
Speaker 1: not only did you get those high yields that you

810
00:50:27,040 --> 00:50:30,879
Speaker 1: started with, but then you've got these capital gains. That's

811
00:50:30,960 --> 00:50:33,319
Speaker 1: probably not going to happen going forward. Here we're gonna

812
00:50:33,400 --> 00:50:37,080
Speaker 1: have a low yield and a potential capital loss. So

813
00:50:37,680 --> 00:50:40,200
Speaker 1: I wanted to see what else so you could you

814
00:50:40,239 --> 00:50:43,920
Speaker 1: could we could have that would be an alternative to that. Sure,

815
00:50:44,080 --> 00:50:48,080
Speaker 1: so short of going back too and building a bond portfolio.

816
00:50:48,680 --> 00:50:52,160
Speaker 1: How how so how would an index the nuity work?

817
00:50:52,320 --> 00:50:54,680
Speaker 1: What is what's in? What's in? Because in the nuity

818
00:50:54,760 --> 00:50:57,160
Speaker 1: is just a rapper, right and for the most part

819
00:50:57,239 --> 00:51:00,640
Speaker 1: tax deferred. UM, how do index in what he's worked?

820
00:51:00,920 --> 00:51:05,799
Speaker 1: And index I their insurance products? And UH, and I

821
00:51:05,880 --> 00:51:11,440
Speaker 1: have to say that UM Zebra Capital has indexes, actually

822
00:51:11,480 --> 00:51:13,760
Speaker 1: a co branded index with the New York Sack Exchange

823
00:51:14,400 --> 00:51:18,239
Speaker 1: and UH that index is actually used by an insurance

824
00:51:18,239 --> 00:51:25,120
Speaker 1: company to UH in creating a accumulation annuity. I'll mention

825
00:51:25,200 --> 00:51:28,160
Speaker 1: here so so I I have some in set up

826
00:51:28,160 --> 00:51:30,680
Speaker 1: here to Actually so you're talking your book, but you're

827
00:51:30,719 --> 00:51:33,560
Speaker 1: the expert in this space. So we've we know that

828
00:51:33,640 --> 00:51:36,840
Speaker 1: you take your academic theory and apply it in practical

829
00:51:36,920 --> 00:51:39,160
Speaker 1: and then here's a perfect example of it. So what

830
00:51:39,360 --> 00:51:43,200
Speaker 1: is that annuity holds? And aren't we kind of doing

831
00:51:43,239 --> 00:51:47,759
Speaker 1: a little bit of UM magic taking something and creating

832
00:51:47,880 --> 00:51:53,719
Speaker 1: a UM a yield that doesn't have the same parameters

833
00:51:53,719 --> 00:51:58,160
Speaker 1: of a bond. What is that metamorphosis there? So the

834
00:51:58,920 --> 00:52:03,000
Speaker 1: what what's happening as we take the index? Actually the

835
00:52:03,000 --> 00:52:08,480
Speaker 1: insurance company takes this index and it it ensures the

836
00:52:08,880 --> 00:52:13,560
Speaker 1: downside essentially, so it doesn't actually by buying, buying options

837
00:52:13,880 --> 00:52:16,680
Speaker 1: and so forth. So what what it means is that

838
00:52:16,800 --> 00:52:21,360
Speaker 1: a an actual person then can buy a annuity and

839
00:52:21,440 --> 00:52:26,080
Speaker 1: index annuity that they get a participation in. Our index

840
00:52:26,160 --> 00:52:27,960
Speaker 1: is an equity index that has the bonds in it,

841
00:52:28,200 --> 00:52:30,719
Speaker 1: and it's risk control to have a five percent volatility,

842
00:52:31,640 --> 00:52:35,040
Speaker 1: but they can buy that index with that with that

843
00:52:36,400 --> 00:52:40,400
Speaker 1: five percent volatility, the insurance company ensures it so that

844
00:52:40,440 --> 00:52:43,560
Speaker 1: you get equity participation, but you don't lose any money.

845
00:52:43,600 --> 00:52:45,960
Speaker 1: So this isn't We're not taking lead and turning it

846
00:52:45,960 --> 00:52:50,400
Speaker 1: into gold. This is essentially a hedged um equity product

847
00:52:50,480 --> 00:52:52,359
Speaker 1: that behaves like a bond. Is that a fair way

848
00:52:52,400 --> 00:52:55,600
Speaker 1: to describe it. It has roughly the same returns as

849
00:52:55,600 --> 00:52:59,280
Speaker 1: a bond on average, but it doesn't have any losses

850
00:52:59,320 --> 00:53:05,480
Speaker 1: that the losses are insured and uh and essentially you

851
00:53:05,880 --> 00:53:09,320
Speaker 1: have equity participation on the upside, not full equity participation.

852
00:53:09,560 --> 00:53:12,360
Speaker 1: It's not a substitute for stocks. It's a substitute for

853
00:53:12,440 --> 00:53:15,880
Speaker 1: bonds that doesn't ever lose money. So it's a dividend

854
00:53:15,920 --> 00:53:20,000
Speaker 1: yield plus whatever capital appreciation you get minus the cost

855
00:53:20,080 --> 00:53:23,400
Speaker 1: of of hedging against the downside. You get the capital

856
00:53:23,480 --> 00:53:30,280
Speaker 1: appreciation and um and a participation in the equity market. Uh,

857
00:53:30,360 --> 00:53:33,640
Speaker 1: and of course UH, no losses on the downside of

858
00:53:34,239 --> 00:53:38,480
Speaker 1: That's what by having less than participation in the equity

859
00:53:38,520 --> 00:53:42,440
Speaker 1: market and by getting the capital appreciation, uh, you are

860
00:53:42,719 --> 00:53:44,480
Speaker 1: be able to the insurance company to be able to

861
00:53:44,560 --> 00:53:47,440
Speaker 1: use that to have ensured the downside so that there

862
00:53:47,440 --> 00:53:50,400
Speaker 1: are no losses. So you end up with a distribution

863
00:53:50,480 --> 00:53:55,160
Speaker 1: of returns that is is maybe roughly comptable in terms

864
00:53:55,160 --> 00:53:58,920
Speaker 1: of returns to a bond market, but has upside but

865
00:53:59,040 --> 00:54:03,720
Speaker 1: not downside. Quite fascinating. You're a Chicago guy. You worked

866
00:54:03,719 --> 00:54:07,279
Speaker 1: with Gene Fama. There are a number of factors that

867
00:54:07,760 --> 00:54:11,360
Speaker 1: just keep getting discovered. Last count it was excess of

868
00:54:11,440 --> 00:54:14,319
Speaker 1: four hundred, maybe even more than that. What do you

869
00:54:14,320 --> 00:54:18,000
Speaker 1: think about all these different factors in the textbooks and

870
00:54:18,000 --> 00:54:20,960
Speaker 1: and how does that work in the real world. Well,

871
00:54:21,040 --> 00:54:23,840
Speaker 1: these say four hundred factors, there are a lot of

872
00:54:23,880 --> 00:54:26,040
Speaker 1: them are correlated with each other, so there's not really

873
00:54:26,200 --> 00:54:29,319
Speaker 1: four hundred, but but there's still many of them, you know.

874
00:54:29,680 --> 00:54:32,960
Speaker 1: And the way to evaluate it has to do with

875
00:54:33,280 --> 00:54:38,960
Speaker 1: our monograph again, popularity, Because in order for a a

876
00:54:39,080 --> 00:54:42,200
Speaker 1: factor to really pay off and forward from over the

877
00:54:42,239 --> 00:54:44,880
Speaker 1: long term, in anyway, it has to be unpopular in

878
00:54:44,920 --> 00:54:50,200
Speaker 1: some sense unpopular. So the popular ones are valuation, quality,

879
00:54:51,320 --> 00:54:56,600
Speaker 1: momentum um. Uh, those are probably the four most popular

880
00:54:56,640 --> 00:54:59,000
Speaker 1: ones I can think of. You want to go further

881
00:54:59,120 --> 00:55:02,520
Speaker 1: on the list and find ones that are either overlooked

882
00:55:02,640 --> 00:55:10,120
Speaker 1: or or unpopular. Well, for example, liquidity is something that's

883
00:55:10,160 --> 00:55:15,520
Speaker 1: inherently popular, and less liquarity is inherently less popular. So

884
00:55:15,640 --> 00:55:18,879
Speaker 1: if you have a factor based on liquarity, that's likely

885
00:55:18,920 --> 00:55:21,719
Speaker 1: to pay off. If you have a factor based on risk,

886
00:55:22,000 --> 00:55:24,920
Speaker 1: they're often likely to pay off because risk is always

887
00:55:24,920 --> 00:55:28,399
Speaker 1: going to be unpopular and less risk is always will

888
00:55:28,440 --> 00:55:32,920
Speaker 1: be more popular. If you have factors based on um,

889
00:55:32,960 --> 00:55:34,560
Speaker 1: it could be quality, it could be any of these

890
00:55:34,560 --> 00:55:38,520
Speaker 1: sort of things. It could be value, value based. Essentially,

891
00:55:38,560 --> 00:55:42,799
Speaker 1: if value companies are distressed companies that we don't like,

892
00:55:43,440 --> 00:55:48,879
Speaker 1: they value premium might be unpopular, I guess. So that's

893
00:55:48,960 --> 00:55:51,400
Speaker 1: that's the rationale why you might have a value premium

894
00:55:51,440 --> 00:55:54,040
Speaker 1: because if people if they're the type of companies that

895
00:55:54,080 --> 00:55:58,680
Speaker 1: we don't like, then then they're going to be relatively

896
00:55:58,760 --> 00:56:01,200
Speaker 1: less demand for them. They're going to be unpopular. But

897
00:56:01,239 --> 00:56:03,640
Speaker 1: that means that less demand means that they're gonna have

898
00:56:03,800 --> 00:56:08,560
Speaker 1: higher expected returns. So so here we are in value

899
00:56:08,560 --> 00:56:12,799
Speaker 1: has gotten its butt kicked over the past decade by growth. Um,

900
00:56:13,160 --> 00:56:16,800
Speaker 1: I'm gonna take that. I'm gonna interpret what you're saying

901
00:56:16,960 --> 00:56:21,640
Speaker 1: as implying valuate value as a factor is less popular,

902
00:56:22,040 --> 00:56:25,960
Speaker 1: but value should expect better returns going forward, or value

903
00:56:25,960 --> 00:56:29,360
Speaker 1: should have a higher expected return going forward versus the

904
00:56:29,400 --> 00:56:32,839
Speaker 1: more popular growth. Yes, I think it does, especially now

905
00:56:32,880 --> 00:56:37,520
Speaker 1: that we've had this bad period, because once, once, after

906
00:56:37,680 --> 00:56:41,600
Speaker 1: value does so well, everybody gets excited and stays saying, well,

907
00:56:41,640 --> 00:56:44,040
Speaker 1: I should be buying value stocks instead of growth stocks.

908
00:56:44,800 --> 00:56:47,440
Speaker 1: So the question you always have to ask yourself this question,

909
00:56:47,520 --> 00:56:50,839
Speaker 1: that's what this monograph is all about. I can help

910
00:56:50,840 --> 00:56:55,440
Speaker 1: but point out that a number of famous and infamous

911
00:56:55,440 --> 00:56:59,520
Speaker 1: hedge fund managers over the past decade, all of whom

912
00:56:59,640 --> 00:57:04,080
Speaker 1: kind of came to the public's attention eight, ten, twelve

913
00:57:04,160 --> 00:57:07,440
Speaker 1: years ago. The more popular they got and the more

914
00:57:07,640 --> 00:57:10,920
Speaker 1: capital they got, the worst their performance seems to be.

915
00:57:11,680 --> 00:57:16,800
Speaker 1: Is this the same sort of issue where suddenly popularity

916
00:57:17,120 --> 00:57:20,240
Speaker 1: just exceeds their ability to manage that capital, or is

917
00:57:20,280 --> 00:57:23,440
Speaker 1: there something about, hey, that's way late in the cycle

918
00:57:23,480 --> 00:57:26,240
Speaker 1: by the time it gets popular. It's just too late. Well,

919
00:57:26,240 --> 00:57:29,919
Speaker 1: it's probably both of that that. Certainly, when they get

920
00:57:30,480 --> 00:57:33,720
Speaker 1: a lot of money that their particular idea, they put

921
00:57:33,760 --> 00:57:35,480
Speaker 1: a lot of money behind it, they're putting too much

922
00:57:35,520 --> 00:57:38,840
Speaker 1: money behind it, and then and then they're moving the

923
00:57:38,880 --> 00:57:43,280
Speaker 1: price themselves, and then that makes it overvalued and then

924
00:57:43,680 --> 00:57:46,560
Speaker 1: too popular in our words, So too popular could be overvalued,

925
00:57:46,600 --> 00:57:50,520
Speaker 1: too popular could be just classical uh types of things

926
00:57:50,560 --> 00:57:55,240
Speaker 1: like risk, opic quality, um, uh popular. But popularity is

927
00:57:55,280 --> 00:57:57,640
Speaker 1: the thing that ties everything together. That's why I guess

928
00:57:57,640 --> 00:58:02,560
Speaker 1: I'm so excited about uh our new uh, our new

929
00:58:02,680 --> 00:58:07,920
Speaker 1: monograph here, uh, because it's not only it ties all

930
00:58:07,960 --> 00:58:10,640
Speaker 1: these premiums together that people are talking about, say the

931
00:58:10,680 --> 00:58:14,320
Speaker 1: four premiums and which ones might work. And it also

932
00:58:14,400 --> 00:58:19,320
Speaker 1: ties in the link because we were evolving into behavioral

933
00:58:19,360 --> 00:58:24,000
Speaker 1: finance and classical finances being different fields to some extent,

934
00:58:24,600 --> 00:58:27,800
Speaker 1: this ties them back together again. So I'm really I'm

935
00:58:27,800 --> 00:58:32,320
Speaker 1: really excited about the whole approach here, and I'll I'll

936
00:58:32,360 --> 00:58:35,200
Speaker 1: include a link to that white paper on the right

937
00:58:35,240 --> 00:58:37,480
Speaker 1: of about this so people can find it. We have

938
00:58:37,640 --> 00:58:42,120
Speaker 1: been speaking with Roger Ibbotson, Professor of Finance at the

939
00:58:42,240 --> 00:58:46,560
Speaker 1: Yale School of Management and Chairman of Ibbotson associates, as

940
00:58:46,560 --> 00:58:50,320
Speaker 1: well as chairman and ce IO of Zebrook Capital Management.

941
00:58:50,720 --> 00:58:53,520
Speaker 1: If you enjoy this conversation, we'll be sure and check

942
00:58:53,560 --> 00:58:56,280
Speaker 1: out the podcast That Straus, where we keep the tape

943
00:58:56,360 --> 00:59:02,800
Speaker 1: rolling and continue discussing all things factor and popularity related.

944
00:59:03,400 --> 00:59:08,920
Speaker 1: You can find that at iTunes, Overcast, Stitcher, Bloomberg dot com,

945
00:59:08,920 --> 00:59:12,960
Speaker 1: wherever final podcasts are found. We love your comments, feedback

946
00:59:13,000 --> 00:59:17,000
Speaker 1: and suggestions right to us at m IB podcast at

947
00:59:17,000 --> 00:59:20,920
Speaker 1: Bloomberg dot net. Check out my daily column on Bloomberg

948
00:59:21,000 --> 00:59:24,920
Speaker 1: dot com slash Opinion. Follow me on Twitter at Hults.

949
00:59:25,400 --> 00:59:29,080
Speaker 1: I'm Barry Rihults. You're listening to Master's in Business on

950
00:59:29,240 --> 00:59:47,320
Speaker 1: Bloomberg Radio. Welcome to the podcast, Roger, Thank you so much.

951
00:59:47,320 --> 00:59:50,040
Speaker 1: I don't know what's called you, Professor Edwardson, Roger, what's

952
00:59:50,040 --> 00:59:54,400
Speaker 1: your preferred uh, well with a person like you, Berry

953
00:59:54,520 --> 00:59:56,880
Speaker 1: is I'll call you Berry. You should call me Roger.

954
00:59:57,680 --> 01:00:01,880
Speaker 1: I feel like I'm I'm definitely uh punching above my

955
01:00:01,920 --> 01:00:06,320
Speaker 1: weight in this conversation. Um, but this has been just

956
01:00:06,400 --> 01:00:10,560
Speaker 1: really fantastic stuff. I know I've been following your career

957
01:00:10,760 --> 01:00:14,440
Speaker 1: and your research and writings for um my whole career

958
01:00:14,440 --> 01:00:17,160
Speaker 1: in finance, and I've been very much looking forward UM

959
01:00:17,240 --> 01:00:21,200
Speaker 1: to this conversation. So let's jump to our favorite questions.

960
01:00:21,240 --> 01:00:24,000
Speaker 1: I asked these of all our guests, and some of

961
01:00:24,240 --> 01:00:28,400
Speaker 1: some of these UM are especially resonant for for different listeners.

962
01:00:28,760 --> 01:00:32,400
Speaker 1: Let's start with UM. I used to ask this question

963
01:00:32,480 --> 01:00:36,480
Speaker 1: just as a sound check for the UM Audio Engineer,

964
01:00:36,640 --> 01:00:40,000
Speaker 1: and the answers were so fascinating that I've started asking

965
01:00:40,000 --> 01:00:42,960
Speaker 1: all the guests this question. So, what was the first

966
01:00:42,960 --> 01:00:47,040
Speaker 1: car you ever owned? Year, make and model. Well, it

967
01:00:47,080 --> 01:00:52,240
Speaker 1: was the nine six one Plymouth Valiant light blue, and

968
01:00:52,360 --> 01:00:55,880
Speaker 1: I was a junior, I guess, just a starting senior

969
01:00:56,160 --> 01:01:00,600
Speaker 1: in college at Purdue. And I really love that car.

970
01:01:00,680 --> 01:01:03,040
Speaker 1: I gotta say it look kind of a sporty looking car,

971
01:01:03,120 --> 01:01:05,800
Speaker 1: and so it was a really do a four door

972
01:01:06,200 --> 01:01:07,960
Speaker 1: it was. It was a four door car, but it

973
01:01:08,040 --> 01:01:12,600
Speaker 1: was still a very very sporty looking car and it

974
01:01:12,640 --> 01:01:16,439
Speaker 1: was great fun, although unfortunately I didn't get to keep

975
01:01:16,480 --> 01:01:20,000
Speaker 1: it that long. I kind of remember the Valiant. I'm

976
01:01:20,040 --> 01:01:22,120
Speaker 1: trying to remember if that was like a Dodge Dart,

977
01:01:22,920 --> 01:01:26,120
Speaker 1: similar shape, sort of a big long hood and a

978
01:01:26,160 --> 01:01:28,880
Speaker 1: big long No, it's actually a little bit smaller than that.

979
01:01:29,600 --> 01:01:32,880
Speaker 1: Very smart sporty looking, all right, wasn't like that back

980
01:01:32,880 --> 01:01:36,120
Speaker 1: in the day when Plymouth made some pretty substantial sports cars. Yeah,

981
01:01:36,120 --> 01:01:39,480
Speaker 1: they didn't make the sports cars. But unfortunately that car,

982
01:01:40,040 --> 01:01:42,320
Speaker 1: uh my first job after I got my m b A.

983
01:01:42,400 --> 01:01:44,480
Speaker 1: So I had that car for three years, a senior

984
01:01:44,560 --> 01:01:47,720
Speaker 1: year and then two years as an NBA student. Um,

985
01:01:47,760 --> 01:01:50,640
Speaker 1: but I worked on a cattle ranch, actually my first

986
01:01:50,720 --> 01:01:55,720
Speaker 1: job in Nevada. Huh. So I was gonna ask my

987
01:01:55,800 --> 01:01:57,680
Speaker 1: next question is what is the most important thing we

988
01:01:57,720 --> 01:02:00,760
Speaker 1: don't know about Roder Ribertson? But before I get to that,

989
01:02:01,080 --> 01:02:03,360
Speaker 1: how do you go from the University of Chicago, an

990
01:02:03,440 --> 01:02:08,280
Speaker 1: NBA in finance to a cattle ranch. Well, I thought

991
01:02:08,520 --> 01:02:11,360
Speaker 1: I had an offer, so I took it that I

992
01:02:11,440 --> 01:02:14,840
Speaker 1: thought it looked like a good opportunity to do something

993
01:02:14,840 --> 01:02:18,000
Speaker 1: really great and but but I was out of my element.

994
01:02:18,040 --> 01:02:20,400
Speaker 1: I was fired at the end of the summer. So

995
01:02:20,520 --> 01:02:23,120
Speaker 1: you weren't trading cattle futures. You were actually on a

996
01:02:23,160 --> 01:02:26,240
Speaker 1: horse within a ranch. Well, it was more like trucks

997
01:02:26,280 --> 01:02:30,800
Speaker 1: and planes. But you're let me tell you, a car

998
01:02:31,120 --> 01:02:34,880
Speaker 1: does not last on a cattle ranch very long because

999
01:02:35,160 --> 01:02:38,840
Speaker 1: they're all dirt roads and the car was just covered

1000
01:02:38,880 --> 01:02:42,440
Speaker 1: with thick with dust and every and every bump and

1001
01:02:42,480 --> 01:02:45,840
Speaker 1: so forth, the transmission went out and all the things.

1002
01:02:46,120 --> 01:02:49,760
Speaker 1: So that was the my card. Uh had that kind

1003
01:02:49,760 --> 01:02:52,320
Speaker 1: of ending life there. And and I didn't work out

1004
01:02:52,320 --> 01:02:56,440
Speaker 1: either at the cattle ranch, and so I actually uh

1005
01:02:56,840 --> 01:02:58,800
Speaker 1: ended that job and put it on my resume as

1006
01:02:58,800 --> 01:03:02,800
Speaker 1: a summer job. That's pretty that's pretty hilarious. So I

1007
01:03:03,520 --> 01:03:06,280
Speaker 1: given that I'm reluctant to ask the next question, but

1008
01:03:06,760 --> 01:03:09,920
Speaker 1: what's the most important thing people don't know about? Roger Robertson,

1009
01:03:11,200 --> 01:03:12,840
Speaker 1: I would actually say, though I'll take it all the

1010
01:03:12,880 --> 01:03:15,760
Speaker 1: way to the present, sure that I'm not you know,

1011
01:03:15,800 --> 01:03:18,280
Speaker 1: I've always been working in finance all these years, but

1012
01:03:18,320 --> 01:03:23,120
Speaker 1: I'm actually very interested in um long term data and

1013
01:03:23,160 --> 01:03:25,720
Speaker 1: time over time and so forth. And I'm getting very

1014
01:03:25,800 --> 01:03:29,920
Speaker 1: interested and I might be writing at least researching whether

1015
01:03:30,120 --> 01:03:34,720
Speaker 1: or right on the subject of of long history going

1016
01:03:34,840 --> 01:03:40,320
Speaker 1: far forward and forward and back. Essentially. Uh, perhaps I've

1017
01:03:40,320 --> 01:03:43,640
Speaker 1: been starting with the Big Bang and and and then

1018
01:03:44,280 --> 01:03:47,440
Speaker 1: really for more predictive purposes though, and and long term

1019
01:03:47,520 --> 01:03:49,880
Speaker 1: kind of predictions and what's going to happen to to

1020
01:03:50,360 --> 01:03:53,120
Speaker 1: humanity and so forth. So that's that's one of the

1021
01:03:53,120 --> 01:03:55,919
Speaker 1: things you'll have to interview me a couple of years later.

1022
01:03:56,640 --> 01:03:58,840
Speaker 1: Times out. You're absolutely welcome back. We'd love to talk

1023
01:03:58,880 --> 01:04:02,000
Speaker 1: about that. So tell us about And I'm afraid to

1024
01:04:02,040 --> 01:04:04,960
Speaker 1: even ask this question, but i have to because I

1025
01:04:05,000 --> 01:04:07,680
Speaker 1: know who's coming up in the answer. Who were your

1026
01:04:07,720 --> 01:04:11,560
Speaker 1: early mentors? Oh? I it is the It is the

1027
01:04:11,680 --> 01:04:17,360
Speaker 1: University of Chicago, people with uh, the Farmer, Merton, Marco

1028
01:04:17,400 --> 01:04:20,600
Speaker 1: Wits who else is on that run? Well as Byron

1029
01:04:20,680 --> 01:04:25,320
Speaker 1: Shows and Fisher Black and yeah, and Merton Miller and

1030
01:04:25,520 --> 01:04:29,400
Speaker 1: Jane Farmer. There are all my early mentors. Yes, that

1031
01:04:29,400 --> 01:04:33,840
Speaker 1: that's that's an incredible lineup. What about investors? What investors

1032
01:04:34,080 --> 01:04:38,080
Speaker 1: influence the way you think about taking theory and applying

1033
01:04:38,120 --> 01:04:42,760
Speaker 1: it in the real world. You know, I they weren't

1034
01:04:42,840 --> 01:04:48,120
Speaker 1: literally investors, because I have to say that, uh, getting

1035
01:04:48,160 --> 01:04:55,200
Speaker 1: grounded in efficient capital market theory UH gives you a

1036
01:04:55,240 --> 01:05:00,640
Speaker 1: whole approach to investing. And and it's stead of starting

1037
01:05:00,680 --> 01:05:04,880
Speaker 1: out with imagining that every stock you imagine is is

1038
01:05:05,000 --> 01:05:08,000
Speaker 1: unrelated to the price in some way. If you start

1039
01:05:08,040 --> 01:05:11,320
Speaker 1: out with the fact that if you know nothing about

1040
01:05:11,320 --> 01:05:16,720
Speaker 1: a company or or a security, it's the prices the

1041
01:05:16,800 --> 01:05:18,919
Speaker 1: kind of the best guess of what its value is.

1042
01:05:19,160 --> 01:05:23,960
Speaker 1: And so that's such a dramatic approach, even if you're

1043
01:05:23,960 --> 01:05:27,560
Speaker 1: looking for inefficience for the market, it's such an uh

1044
01:05:27,720 --> 01:05:30,880
Speaker 1: changes your whole way of approaching everything and thinking about

1045
01:05:30,880 --> 01:05:35,840
Speaker 1: everything because you instead of well, for example, as a

1046
01:05:35,880 --> 01:05:41,960
Speaker 1: portfolio manager, you could do nothing and you might do fine.

1047
01:05:42,000 --> 01:05:45,880
Speaker 1: You know, it's you don't have to literally trade every

1048
01:05:45,920 --> 01:05:48,840
Speaker 1: day to make things work. It's a matter of of

1049
01:05:49,480 --> 01:05:51,280
Speaker 1: you only want to trade when you really do have

1050
01:05:51,960 --> 01:05:55,240
Speaker 1: the edge that you're actually going to add value. So

1051
01:05:55,920 --> 01:05:57,320
Speaker 1: one of the things I always think about and I've

1052
01:05:57,320 --> 01:06:03,120
Speaker 1: managed companies and I've managed stocks, and I think um

1053
01:06:03,400 --> 01:06:08,160
Speaker 1: stocks in some way manage themselves. People. Well, if you

1054
01:06:08,280 --> 01:06:11,439
Speaker 1: start out from the belief that the prices of fair

1055
01:06:11,560 --> 01:06:15,360
Speaker 1: estimation of the value that should really take care of itself.

1056
01:06:15,840 --> 01:06:18,320
Speaker 1: That's right. That's that's the key. I mean, once you

1057
01:06:18,400 --> 01:06:21,200
Speaker 1: sort of know that your stocks are managing themselves, but

1058
01:06:21,240 --> 01:06:24,520
Speaker 1: then you try to improve on that. But man your

1059
01:06:24,560 --> 01:06:28,480
Speaker 1: people don't manage themselves, that's right. So it's much harder

1060
01:06:28,480 --> 01:06:32,240
Speaker 1: to manage people than it is to manage stocks once

1061
01:06:32,280 --> 01:06:35,160
Speaker 1: you understand this principle. If you don't understand that principle, though,

1062
01:06:36,280 --> 01:06:39,560
Speaker 1: managing stock markets. You've got hundreds of thousands of stocks.

1063
01:06:39,560 --> 01:06:42,280
Speaker 1: You've got hundreds, hundreds or thousands or whatever. You've got

1064
01:06:42,280 --> 01:06:44,280
Speaker 1: all these stocks, and you're trying to think out it's

1065
01:06:44,320 --> 01:06:47,560
Speaker 1: just one over valued or undervalued. If you have to

1066
01:06:47,760 --> 01:06:50,800
Speaker 1: attack it that way, you can quickly get overwhelmed into

1067
01:06:50,800 --> 01:06:55,360
Speaker 1: the situation. So it's just a principle that so much

1068
01:06:55,400 --> 01:06:59,560
Speaker 1: simplifies the process. Starting with the assumption that, hey, most

1069
01:06:59,560 --> 01:07:03,000
Speaker 1: stocks gonna be more or less priced relative to their value.

1070
01:07:03,320 --> 01:07:06,000
Speaker 1: It's a good starting place. Um, all right, let's get

1071
01:07:06,040 --> 01:07:09,160
Speaker 1: to everybody's favorite question. UM, I get emails about this

1072
01:07:09,200 --> 01:07:13,400
Speaker 1: all the time. Tell us some of your favorite books. Well,

1073
01:07:13,440 --> 01:07:15,800
Speaker 1: but by the way, I will include your books when

1074
01:07:15,800 --> 01:07:17,720
Speaker 1: we when this goes live, I will include a list

1075
01:07:17,760 --> 01:07:20,080
Speaker 1: of all your published books. So tell us some of

1076
01:07:20,160 --> 01:07:23,600
Speaker 1: the favorite books that you've read of other authors. Yeah,

1077
01:07:23,600 --> 01:07:25,360
Speaker 1: I want to say, of course, I'm not going to

1078
01:07:25,400 --> 01:07:27,240
Speaker 1: promote my own books. I always love my own books.

1079
01:07:27,240 --> 01:07:32,840
Speaker 1: I guess who does it? What does But because I've

1080
01:07:32,840 --> 01:07:36,000
Speaker 1: been looking at long term things, I've been you know,

1081
01:07:36,040 --> 01:07:40,400
Speaker 1: are you're gonna go go ahead? Let's see for for example, UM,

1082
01:07:40,440 --> 01:07:44,880
Speaker 1: there's this, Uh, you've o Harari who's been writing and

1083
01:07:45,360 --> 01:07:50,000
Speaker 1: sapiens and then the predicting the future on that and

1084
01:07:50,080 --> 01:07:53,920
Speaker 1: reading that like I've been reading like Steven Pinker's Huh.

1085
01:07:54,120 --> 01:07:56,160
Speaker 1: I think it's called Our Better Angels, that our angels

1086
01:07:56,160 --> 01:08:00,360
Speaker 1: of our nature. Yes, about how how how we're changing

1087
01:08:00,400 --> 01:08:02,840
Speaker 1: over time and so forth, and how we're less violent

1088
01:08:02,880 --> 01:08:08,080
Speaker 1: and so forth. Uh. David Christian is an author and

1089
01:08:08,720 --> 01:08:11,600
Speaker 1: he's Australian. Well it's not really Australia, and he's been

1090
01:08:11,600 --> 01:08:13,120
Speaker 1: all over the world, but he's I think he's teaching

1091
01:08:13,160 --> 01:08:16,400
Speaker 1: in Australia at this point, but he he's he's written

1092
01:08:16,439 --> 01:08:21,080
Speaker 1: a book and done a lot of uh of course,

1093
01:08:21,360 --> 01:08:27,160
Speaker 1: uh electronic courses on on big History, which is looking

1094
01:08:27,200 --> 01:08:30,080
Speaker 1: at history from the start all the way out into

1095
01:08:30,120 --> 01:08:32,960
Speaker 1: the future. Is that the name of the book Big History? Well,

1096
01:08:32,960 --> 01:08:35,240
Speaker 1: he has a book that's his most storole book is

1097
01:08:35,640 --> 01:08:39,679
Speaker 1: Maps in Time from about ten ten, fifteen years ago.

1098
01:08:40,360 --> 01:08:41,880
Speaker 1: Do you have any other books you want to mention

1099
01:08:41,920 --> 01:08:45,320
Speaker 1: because I'm gonna circle back to something you said earlier. Well,

1100
01:08:45,360 --> 01:08:49,639
Speaker 1: I could name many kids like this subject when I'm

1101
01:08:49,680 --> 01:08:55,920
Speaker 1: just just just finished was Yeah. Johan uh Norberg out

1102
01:08:55,920 --> 01:08:59,599
Speaker 1: of Sweden wrote a book on progress and how how

1103
01:09:01,120 --> 01:09:05,120
Speaker 1: we're getting better off and lots of different dimensions and

1104
01:09:05,120 --> 01:09:07,479
Speaker 1: so forth. So these are all subjects that are of

1105
01:09:07,520 --> 01:09:09,960
Speaker 1: interest to me. I don't think it's all good news though,

1106
01:09:10,000 --> 01:09:13,799
Speaker 1: of course, uh because at Yale, for example, the recent

1107
01:09:13,840 --> 01:09:22,280
Speaker 1: Nobel Prize winner um Um Bill Northouse uh really worked

1108
01:09:22,280 --> 01:09:25,200
Speaker 1: on pollution and so forth, and how global warming and

1109
01:09:25,200 --> 01:09:27,120
Speaker 1: so forth. So lots of other things are going on.

1110
01:09:27,200 --> 01:09:29,920
Speaker 1: I'm not saying it's all good news. I'm I'm interested

1111
01:09:29,920 --> 01:09:32,200
Speaker 1: in the overall effect though, and trying to understand this.

1112
01:09:32,479 --> 01:09:35,800
Speaker 1: So I'm I am interested in long term history, and

1113
01:09:35,840 --> 01:09:41,000
Speaker 1: I'm always interested in long term forecasting. I'm fascinated by

1114
01:09:41,000 --> 01:09:45,200
Speaker 1: the two Harari books because Sapiens is so interesting and

1115
01:09:45,240 --> 01:09:48,519
Speaker 1: while some of it's got a little bit of negative cast,

1116
01:09:49,160 --> 01:09:53,120
Speaker 1: generally speaking, it's the history of progress, even though along

1117
01:09:53,120 --> 01:09:56,679
Speaker 1: the way he kind of annotates it with all these, yeah,

1118
01:09:56,720 --> 01:10:00,200
Speaker 1: well farms and cities or where diseases began, and but

1119
01:10:00,280 --> 01:10:03,920
Speaker 1: it's not a completely bleak picture. The Homo Dais book

1120
01:10:04,040 --> 01:10:08,479
Speaker 1: is a little more negative looking forward than Sapiens, sort

1121
01:10:08,479 --> 01:10:11,240
Speaker 1: of had this wide eyed wonder to it, and which

1122
01:10:11,320 --> 01:10:13,439
Speaker 1: did not come across at Home of Dais. I'm curious

1123
01:10:13,439 --> 01:10:16,639
Speaker 1: as to your your take on the two books. Well,

1124
01:10:16,720 --> 01:10:21,679
Speaker 1: I sense what you're saying, and and I must say,

1125
01:10:21,880 --> 01:10:26,040
Speaker 1: humans I've been a remarkable species. And I'm always just

1126
01:10:26,560 --> 01:10:32,000
Speaker 1: amazed at the fact that that we we actually can

1127
01:10:32,200 --> 01:10:35,479
Speaker 1: understand the start of the universe, and we can understand

1128
01:10:35,680 --> 01:10:38,240
Speaker 1: we maybe we do, We're not sure we do yet. Well,

1129
01:10:38,280 --> 01:10:40,280
Speaker 1: we know a lot about it. It's some amazing a

1130
01:10:40,320 --> 01:10:43,160
Speaker 1: lot about it. And we also know I mean, that's

1131
01:10:43,160 --> 01:10:45,240
Speaker 1: how they got the Higgs, Boston fixtion and all that

1132
01:10:45,280 --> 01:10:47,000
Speaker 1: kind of thing. They can predict these sort of things.

1133
01:10:47,240 --> 01:10:50,080
Speaker 1: And we also know something about the scale of the universe,

1134
01:10:50,240 --> 01:10:53,600
Speaker 1: which is astonishing. And this is all happened in the

1135
01:10:53,680 --> 01:10:55,960
Speaker 1: last three or four hundred years, you know, since since

1136
01:10:56,080 --> 01:11:02,800
Speaker 1: uh Copernicus and Kepler and Galileo and so forth, these things, Uh,

1137
01:11:02,840 --> 01:11:04,960
Speaker 1: we went from nowhere thinking the Earth was the center

1138
01:11:04,960 --> 01:11:09,439
Speaker 1: of everything to actually understanding everything that's at the macro level,

1139
01:11:09,560 --> 01:11:13,240
Speaker 1: but the micro level. We also understand how of course

1140
01:11:13,680 --> 01:11:18,400
Speaker 1: Adams and electrons and and courts and d n A

1141
01:11:18,760 --> 01:11:25,800
Speaker 1: and and so forth. So our um, our species has

1142
01:11:25,880 --> 01:11:32,960
Speaker 1: just uh may has amazing accomplishments and and and but

1143
01:11:33,120 --> 01:11:35,519
Speaker 1: I don't know where. So you have to wonder, then,

1144
01:11:35,880 --> 01:11:40,479
Speaker 1: given all this huge acceleration of knowledge and and actually

1145
01:11:41,439 --> 01:11:44,320
Speaker 1: collection of knowledge that just keeps building on itself. Where

1146
01:11:44,360 --> 01:11:48,720
Speaker 1: does that go, especially especially the past fifty years. We're

1147
01:11:48,800 --> 01:11:51,479
Speaker 1: in a golden age of physics, I mean the past

1148
01:11:52,120 --> 01:11:56,840
Speaker 1: fifty years and in the past decade, quite quite astonishing

1149
01:11:56,920 --> 01:12:01,240
Speaker 1: the sort of progress we've made. All that said, the

1150
01:12:01,240 --> 01:12:04,880
Speaker 1: Big Bang theory is still a very preliminary theory. We

1151
01:12:04,920 --> 01:12:08,640
Speaker 1: don't understand the whole inflationary expansion that took place. We

1152
01:12:08,680 --> 01:12:12,280
Speaker 1: still don't understand if we're measuring the universe right, how

1153
01:12:12,360 --> 01:12:14,360
Speaker 1: much dark matter is out there? What are we missing

1154
01:12:14,400 --> 01:12:18,160
Speaker 1: when we not say why are galaxies accelerating away from

1155
01:12:18,200 --> 01:12:21,800
Speaker 1: each other no matter what direction you look. I'm fascinated

1156
01:12:21,880 --> 01:12:25,760
Speaker 1: by that. I like you are. I share um, just

1157
01:12:25,840 --> 01:12:29,920
Speaker 1: a genuine astonishment about that. I'm also there was a

1158
01:12:29,960 --> 01:12:33,000
Speaker 1: book that came out about a decade ago um that

1159
01:12:33,040 --> 01:12:36,400
Speaker 1: you might appreciate if you're if you're working away forward

1160
01:12:36,439 --> 01:12:40,559
Speaker 1: from the Big Bang, called the rare Earth thesis, which

1161
01:12:40,600 --> 01:12:44,880
Speaker 1: basically says, well, life is probably common in the galaxy

1162
01:12:45,040 --> 01:12:48,479
Speaker 1: because in the universe, because every galaxy has all the

1163
01:12:48,520 --> 01:12:53,200
Speaker 1: build fundamental building blocks hydrogen, carbon, oxygen, nitrogen, etcetera. But

1164
01:12:53,320 --> 01:12:56,679
Speaker 1: intelligent life is really, really rare, and it's a whole

1165
01:12:56,720 --> 01:13:02,080
Speaker 1: series of incredibly unused usual events that lead to a

1166
01:13:02,200 --> 01:13:06,880
Speaker 1: planet like this that's stable enough for for billions of years,

1167
01:13:06,880 --> 01:13:13,320
Speaker 1: for long enough for intelligent, technologically advanced life to um develop.

1168
01:13:13,400 --> 01:13:16,920
Speaker 1: And I'm I'm just intrigued by that sort of circling

1169
01:13:17,000 --> 01:13:20,360
Speaker 1: back to the original pre enlightenment. Oh no, no, it's

1170
01:13:20,439 --> 01:13:23,920
Speaker 1: just humans. The rest of the universe doesn't doesn't matter.

1171
01:13:24,080 --> 01:13:26,519
Speaker 1: The whole universe was just created by God for us.

1172
01:13:27,320 --> 01:13:33,080
Speaker 1: And maybe there's actually some for completely different reasons, theoretical

1173
01:13:33,120 --> 01:13:38,760
Speaker 1: basis for that um thesis from a physics approach, But

1174
01:13:38,760 --> 01:13:42,479
Speaker 1: but I digress far off. It's not the best written book,

1175
01:13:42,479 --> 01:13:45,639
Speaker 1: but it's a fascinating concept if you if you're enjoying

1176
01:13:46,080 --> 01:13:49,479
Speaker 1: all the modern astrophysics work, and I'm sure you've you've

1177
01:13:49,520 --> 01:13:52,640
Speaker 1: come across Brian Green and his string theory work and

1178
01:13:52,680 --> 01:13:56,120
Speaker 1: other stuff. I'm endlessly fascinated by that. I'm just intrigued

1179
01:13:56,160 --> 01:13:58,040
Speaker 1: by that stuff. So one of the great things about

1180
01:13:58,080 --> 01:14:02,320
Speaker 1: being a professor, uh is is uh you get to

1181
01:14:02,360 --> 01:14:05,479
Speaker 1: study whatever you want and and you you share this

1182
01:14:05,800 --> 01:14:09,720
Speaker 1: fascination with me with this, And now I really can

1183
01:14:09,800 --> 01:14:12,400
Speaker 1: kind of work on this now you see. So do

1184
01:14:12,439 --> 01:14:14,360
Speaker 1: you ever pick up the phone at Yale and say, oh,

1185
01:14:14,439 --> 01:14:17,400
Speaker 1: so and so is in the astrophysics department, let me

1186
01:14:17,439 --> 01:14:19,360
Speaker 1: get him on the phone. And you have access to

1187
01:14:19,360 --> 01:14:22,360
Speaker 1: all these folks who can who can steer you in

1188
01:14:22,360 --> 01:14:26,840
Speaker 1: certain directions. I'm starting to do that. I hadn't been

1189
01:14:26,840 --> 01:14:29,639
Speaker 1: doing that, you know, because I've more had been pretty

1190
01:14:29,680 --> 01:14:32,599
Speaker 1: focused over the as you said, I've have a broad

1191
01:14:32,840 --> 01:14:36,759
Speaker 1: breath breath of activities here, but but they hadn't gotten

1192
01:14:36,840 --> 01:14:39,120
Speaker 1: into this sort of thing. Now I'm getting into all

1193
01:14:39,160 --> 01:14:42,479
Speaker 1: the other arms of the university, and uh, I find

1194
01:14:42,479 --> 01:14:48,160
Speaker 1: it really exciting to uh to see, really I'm applying

1195
01:14:48,240 --> 01:14:50,120
Speaker 1: the kind of things I've always done because I always

1196
01:14:50,200 --> 01:14:53,840
Speaker 1: was interested in history and data and I've always taking

1197
01:14:53,840 --> 01:14:56,080
Speaker 1: it to the next level and always interested in making

1198
01:14:56,080 --> 01:14:59,759
Speaker 1: projections and and so forth. And now I'm just trying

1199
01:14:59,800 --> 01:15:03,080
Speaker 1: to broaden this out. So so this is, uh, this

1200
01:15:03,760 --> 01:15:05,479
Speaker 1: a great I guess at this point I would say

1201
01:15:05,479 --> 01:15:08,600
Speaker 1: it's a hobby, but but it but it trying to

1202
01:15:08,600 --> 01:15:10,720
Speaker 1: make it into I don't think I'll make money from

1203
01:15:10,760 --> 01:15:14,240
Speaker 1: this car. Well, it's tough to do that. So so

1204
01:15:14,320 --> 01:15:16,439
Speaker 1: it makes me want to ask you the question universal

1205
01:15:16,560 --> 01:15:19,240
Speaker 1: entropy and we eventually just going to dissipate or the

1206
01:15:19,280 --> 01:15:21,720
Speaker 1: big crunch and all starts over again. You really do

1207
01:15:21,840 --> 01:15:26,559
Speaker 1: want the big question, you know, I I come prepared

1208
01:15:28,040 --> 01:15:33,280
Speaker 1: while the uh, of course entropy eventually dissipates everything. But

1209
01:15:33,280 --> 01:15:35,880
Speaker 1: but one of the things that the universe is built

1210
01:15:35,920 --> 01:15:39,879
Speaker 1: on is complexity. And even though you have this overall

1211
01:15:40,360 --> 01:15:43,280
Speaker 1: entropy going on, which is spreading everything out and dissipating,

1212
01:15:43,680 --> 01:15:46,400
Speaker 1: you have gravity has been one of the big things,

1213
01:15:46,479 --> 01:15:50,040
Speaker 1: pulling things, pulling parts of things together. So at the

1214
01:15:50,160 --> 01:15:55,800
Speaker 1: same time everything is dissipating. Obviously we are we are

1215
01:15:55,800 --> 01:15:58,599
Speaker 1: getting more and more complex, and that's certainly humans are

1216
01:15:58,600 --> 01:16:01,120
Speaker 1: an example of that. So the word own is getting

1217
01:16:01,400 --> 01:16:05,880
Speaker 1: more complex, not less complex, despite this, despite the second

1218
01:16:05,920 --> 01:16:09,519
Speaker 1: law thermodynamics. So I'm fascinated by the concept and I

1219
01:16:09,560 --> 01:16:12,479
Speaker 1: wish I can remember where I read it that and

1220
01:16:12,560 --> 01:16:15,680
Speaker 1: this will be the last astrophysics part of our conversation.

1221
01:16:16,280 --> 01:16:20,840
Speaker 1: The idea that nothingness is inherently unstable, and the Big

1222
01:16:20,880 --> 01:16:26,280
Speaker 1: Bang comes around that you can't have nothingness forever. Nothingness

1223
01:16:26,280 --> 01:16:31,280
Speaker 1: eventually just explodes into something else because if it's inherent instability,

1224
01:16:31,680 --> 01:16:35,679
Speaker 1: and that's a little mind blowing to me us humans

1225
01:16:35,760 --> 01:16:40,280
Speaker 1: can barely conceptualize it. But it's a fascinating concept. Where

1226
01:16:40,479 --> 01:16:44,080
Speaker 1: how does the universe come out of nothingness? And the

1227
01:16:44,160 --> 01:16:47,439
Speaker 1: answer is, well, maybe nothingness just is so unstable that

1228
01:16:47,479 --> 01:16:51,120
Speaker 1: it has no choice but to create a universe. I'm

1229
01:16:51,200 --> 01:16:54,920
Speaker 1: I'm fascinated by that. So in my case, I'm going

1230
01:16:54,920 --> 01:16:59,680
Speaker 1: to limit this a little more. You're gonna keep it

1231
01:16:59,720 --> 01:17:03,200
Speaker 1: limited to just the universe. Well, I mean I might

1232
01:17:03,320 --> 01:17:06,040
Speaker 1: look at the next hundred years or the next thousand

1233
01:17:06,120 --> 01:17:10,040
Speaker 1: days or something like that, so uh, the instead of

1234
01:17:10,160 --> 01:17:13,880
Speaker 1: uh or maybe mainly our planet, you know, instead of

1235
01:17:13,880 --> 01:17:16,400
Speaker 1: the whole universe and so forth. So, but but they're

1236
01:17:16,439 --> 01:17:20,200
Speaker 1: all related. I mean, it's certainly having some understanding of

1237
01:17:20,439 --> 01:17:25,640
Speaker 1: the big picture helps you, helps you understand still the

1238
01:17:25,840 --> 01:17:30,320
Speaker 1: very big picture quite interesting. So let me move forward

1239
01:17:30,800 --> 01:17:34,000
Speaker 1: away from the universe and ask you this, tell us

1240
01:17:34,040 --> 01:17:36,439
Speaker 1: about a time you failed and what you learned from

1241
01:17:36,479 --> 01:17:39,519
Speaker 1: the experience. You know, I failed quite a bit in

1242
01:17:39,560 --> 01:17:43,840
Speaker 1: my early days, and uh my my father was in

1243
01:17:43,880 --> 01:17:47,280
Speaker 1: the heating and air conditioning business, and had I been

1244
01:17:47,800 --> 01:17:51,599
Speaker 1: more mechanically inclined, you would have gone into that field. Yes,

1245
01:17:52,040 --> 01:17:54,960
Speaker 1: that's certainly goodness, you weren't because we we need you

1246
01:17:55,000 --> 01:17:58,000
Speaker 1: here in finance doing what you're doing. And and then

1247
01:17:58,120 --> 01:18:02,639
Speaker 1: even even when I, UM, so, when I went to college,

1248
01:18:02,720 --> 01:18:05,680
Speaker 1: my father to take engineering because the corporate presidents are

1249
01:18:05,720 --> 01:18:08,360
Speaker 1: mostly engineers. That was true after World War Two, but

1250
01:18:08,400 --> 01:18:12,240
Speaker 1: it's not true today. Um. And so I took engineering,

1251
01:18:12,240 --> 01:18:15,759
Speaker 1: but I really couldn't handle all the hands on stuff

1252
01:18:15,760 --> 01:18:19,200
Speaker 1: of even engineering, and and so I got into math

1253
01:18:19,240 --> 01:18:23,479
Speaker 1: and physics as my major. But then I but even so,

1254
01:18:23,560 --> 01:18:26,000
Speaker 1: I needed to be something more abstract. But when I

1255
01:18:26,040 --> 01:18:29,160
Speaker 1: got to math, it got too abstract from me and

1256
01:18:29,200 --> 01:18:33,280
Speaker 1: I couldn't even handle that. And and so I had

1257
01:18:33,320 --> 01:18:37,000
Speaker 1: to find a niche and finance. Finance is actually the

1258
01:18:37,000 --> 01:18:39,439
Speaker 1: perfect niche. It's it's kind of abstract, but it's not

1259
01:18:39,479 --> 01:18:43,439
Speaker 1: as abstract as as abstract. Um it's I I actually

1260
01:18:43,479 --> 01:18:48,439
Speaker 1: try to make something like abstract things practical. That's been

1261
01:18:48,520 --> 01:18:51,960
Speaker 1: my key. I guess that that that piece in between

1262
01:18:52,000 --> 01:18:55,240
Speaker 1: the fully abstract and the practical and blend them together.

1263
01:18:55,320 --> 01:18:57,240
Speaker 1: That's your sweet spot, right in the middle between the

1264
01:18:57,400 --> 01:18:59,519
Speaker 1: between the two of them. So what do you do

1265
01:18:59,600 --> 01:19:03,639
Speaker 1: for fun and when you're not contemplating, uh, the universe? What? What?

1266
01:19:03,640 --> 01:19:08,160
Speaker 1: What does Roger Ribintson do to keep himself entertained, Well,

1267
01:19:08,439 --> 01:19:11,880
Speaker 1: I mean I have a family, and so I have

1268
01:19:11,880 --> 01:19:15,960
Speaker 1: two sons here in Brooklyn, and um, it's great to

1269
01:19:15,960 --> 01:19:18,240
Speaker 1: get together with them. Of course I exercise and all that,

1270
01:19:18,320 --> 01:19:22,600
Speaker 1: and and um like hiking and all that. But but

1271
01:19:22,680 --> 01:19:26,439
Speaker 1: to me, the most fun and even connects my family

1272
01:19:26,439 --> 01:19:30,240
Speaker 1: and everything is the world of ideas. I mean, and

1273
01:19:30,479 --> 01:19:33,640
Speaker 1: that's what's so great about being a professor. Uh, you

1274
01:19:33,760 --> 01:19:37,200
Speaker 1: essentially are told do what you want. Um, you have

1275
01:19:37,240 --> 01:19:38,600
Speaker 1: to come out with something once in a while, but

1276
01:19:38,680 --> 01:19:41,719
Speaker 1: do whatever you want every once in a while. Educate

1277
01:19:41,760 --> 01:19:44,320
Speaker 1: the kids if you can. But also I actually do

1278
01:19:44,439 --> 01:19:48,320
Speaker 1: love teaching too, So the teaching is really exciting to

1279
01:19:48,360 --> 01:19:50,600
Speaker 1: me and stimulating. So it's a it's a it's a

1280
01:19:50,640 --> 01:19:53,760
Speaker 1: great combination. So so I thought that I'm a workaholic,

1281
01:19:54,600 --> 01:20:00,160
Speaker 1: because I'm really not a workaholic. But it's about the

1282
01:20:00,200 --> 01:20:02,400
Speaker 1: most fun you could have. And even when I talked

1283
01:20:02,400 --> 01:20:07,519
Speaker 1: to my sons, for example, and uh, it's all we're

1284
01:20:07,560 --> 01:20:10,599
Speaker 1: talking about economics all the time, and it's an exciting

1285
01:20:11,560 --> 01:20:15,439
Speaker 1: area and not only just economics about but about um,

1286
01:20:15,479 --> 01:20:18,400
Speaker 1: the universe or whatever, you know, talking about all these ideas.

1287
01:20:18,400 --> 01:20:23,040
Speaker 1: So the world of ideas is really fun. Quite quite

1288
01:20:23,439 --> 01:20:27,400
Speaker 1: um quite charming. Um. So you mentioned teaching. If a

1289
01:20:27,520 --> 01:20:30,320
Speaker 1: millennial or a recent college grad came to you and

1290
01:20:30,800 --> 01:20:34,439
Speaker 1: asked for career advice about the world of finance, what

1291
01:20:34,560 --> 01:20:37,599
Speaker 1: sort of advice would you give them, Well, it starts

1292
01:20:37,640 --> 01:20:40,880
Speaker 1: with follow your passion and follow your interests. Of course,

1293
01:20:41,040 --> 01:20:46,040
Speaker 1: don't just try to maximize money here the um and

1294
01:20:46,120 --> 01:20:49,960
Speaker 1: if if they they're gonna be likely successful if they

1295
01:20:49,960 --> 01:20:52,920
Speaker 1: are following their passion, so so that that's definitely what

1296
01:20:52,960 --> 01:20:56,920
Speaker 1: I would tell them to do. Uh. Fortunately, finance is

1297
01:20:56,960 --> 01:21:01,720
Speaker 1: a pretty well paid profession, so most people end up

1298
01:21:01,760 --> 01:21:05,680
Speaker 1: doing reasonably well. And uh that maybe that's not I'm

1299
01:21:05,680 --> 01:21:08,360
Speaker 1: glad that happens, because I guess at all worked out

1300
01:21:08,360 --> 01:21:14,160
Speaker 1: great for me. So and and and our final question,

1301
01:21:14,560 --> 01:21:16,479
Speaker 1: what is it that you know about the world of

1302
01:21:16,520 --> 01:21:20,960
Speaker 1: investing today that you wish you knew forty years ago

1303
01:21:21,040 --> 01:21:23,519
Speaker 1: when you were coming out of your NBA program and

1304
01:21:23,560 --> 01:21:27,040
Speaker 1: just getting started. And that question I'm not going to

1305
01:21:27,160 --> 01:21:30,320
Speaker 1: really answer the way you asked it, because in fact

1306
01:21:30,680 --> 01:21:36,920
Speaker 1: I did know, uh, And the whole idea of coming

1307
01:21:36,960 --> 01:21:41,360
Speaker 1: out of understanding what equilibrium is and how prices are

1308
01:21:41,439 --> 01:21:47,520
Speaker 1: formed and our version and how starting with market efficiency

1309
01:21:47,560 --> 01:21:52,160
Speaker 1: and so forth. That really changed my life and how

1310
01:21:52,240 --> 01:21:56,760
Speaker 1: I approach everything and made everything easier. So you were

1311
01:21:56,920 --> 01:22:00,360
Speaker 1: fortunate to figure out at the beginning of your career

1312
01:22:00,800 --> 01:22:03,920
Speaker 1: what a lot of people don't figure out until towards

1313
01:22:03,960 --> 01:22:06,160
Speaker 1: the end of their career. Yes, so it's not like

1314
01:22:06,240 --> 01:22:07,880
Speaker 1: I don't I don't know what to tell you about

1315
01:22:07,920 --> 01:22:10,400
Speaker 1: what I didn't know, because I actually did know these

1316
01:22:10,439 --> 01:22:14,840
Speaker 1: important concepts and they actually had wonderful I watched a

1317
01:22:14,920 --> 01:22:18,479
Speaker 1: wonderful impact on me. Well, that's quite fascinating. Roger Ibbotson,

1318
01:22:18,560 --> 01:22:20,840
Speaker 1: thank you so much for being so generous with your time.

1319
01:22:21,280 --> 01:22:23,960
Speaker 1: We have been speaking with Roger Ibbotson. He is a

1320
01:22:24,000 --> 01:22:27,519
Speaker 1: Professor uh in the Practice Emeritus of Finance at the

1321
01:22:27,640 --> 01:22:31,280
Speaker 1: Yale School of Management, as well as founder and chairman

1322
01:22:31,320 --> 01:22:35,160
Speaker 1: of Ibbotson Associates and chairman and chief investment officer of

1323
01:22:35,360 --> 01:22:40,160
Speaker 1: Zebra Capital Management. We love your comments, feedback and suggestions.

1324
01:22:40,160 --> 01:22:43,560
Speaker 1: Be sure and write to us at m IB podcast

1325
01:22:43,840 --> 01:22:46,799
Speaker 1: at Bloomberg dot net. Check out some of the other

1326
01:22:46,920 --> 01:22:51,000
Speaker 1: two hundred and forty eight such previous conversations that we've had.

1327
01:22:51,479 --> 01:22:53,000
Speaker 1: If you look up an inch or down an Inch

1328
01:22:53,080 --> 01:22:57,120
Speaker 1: on Apple, iTunes, Stitcher, Overcast, Bloomberg dot com, wherever final

1329
01:22:57,160 --> 01:22:58,960
Speaker 1: podcasts are sold, you can see the rest of all

1330
01:22:59,000 --> 01:23:02,439
Speaker 1: of our previous com stations. I would be remiss if

1331
01:23:02,479 --> 01:23:05,240
Speaker 1: I did not thank the crack staff that helps put

1332
01:23:05,280 --> 01:23:09,439
Speaker 1: this podcast together each week. Um Medina Parwana is my

1333
01:23:09,560 --> 01:23:14,639
Speaker 1: producer slash audio engineer. Taylor Riggs is our booker. Attica

1334
01:23:14,760 --> 01:23:18,680
Speaker 1: val Bron is our project manager. Michael bat Nick is

1335
01:23:18,680 --> 01:23:21,719
Speaker 1: our head of research. I have to thank Charlie Palett

1336
01:23:21,800 --> 01:23:24,960
Speaker 1: this week for giving us his desk and his UH

1337
01:23:25,280 --> 01:23:29,439
Speaker 1: recording studio because of my snaff who with daylight, savings

1338
01:23:29,479 --> 01:23:33,839
Speaker 1: time and the UK. I'm Barry Retults. You've been listening

1339
01:23:33,880 --> 01:23:36,400
Speaker 1: to Masters in Business on Bloomberg Radio