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Speaker 1: Welcome to Trillions. I'm Joel Weber and I'm Eric bel Tunis.

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Speaker 1: Eric Our colleague Sam Potter recently published a story that

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Speaker 1: the moment I published, we started talking about it and

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Speaker 1: we wanted to bring it on to this episode. UM.

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Speaker 1: What was the story and why did it cutch your attention?

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Speaker 1: It was really a story about the fiftieth anniversary of

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Speaker 1: the Index Fund. And a lot of people really when

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Speaker 1: they think of the Index Fund, they think of Vanguard

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Speaker 1: and Bogel, which launched their's I believe in seventies six.

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Speaker 1: But the real birth of the fund was four or

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Speaker 1: five years before that. And so the guy behind it

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Speaker 1: was interviewed in Sam's story, and this is a there

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Speaker 1: was a lot that was going on pre Vanguard, and

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Speaker 1: I think getting at that and looking at that is

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Speaker 1: really interesting. But largely, look, it's the fiftieth anniversary the

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Speaker 1: Index Fund. This is an e t F show et

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Speaker 1: y s would not exist if it wasn't for the

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Speaker 1: Index Fund. So the Index Fund is sort of the

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Speaker 1: the route which has sprung all of this, UM, all

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Speaker 1: these other things, and it's been obviously taking people have

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Speaker 1: taken it and run with it. So to talk to

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Speaker 1: the person who is even pre vogel uh, you know,

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Speaker 1: is really exciting, and we have them today. His name

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Speaker 1: is mc McCown and he'll be joining us from California

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Speaker 1: for this episode as well as Sam Potter from London.

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Speaker 1: This time on Trillions, Meet the man who started eleven

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Speaker 1: trillion dollar index Revolution, Sam mac Welcome to Trillions, Thank you,

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Speaker 1: good morning, and hello from Sonoma Wine Country. Okay, Sam,

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Speaker 1: can you set the stage? How did you decide that

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Speaker 1: this was going to be the story that that you

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Speaker 1: were going to pursue? How did I How did I

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Speaker 1: find mac? Well? Um? About six months ago, so I

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Speaker 1: was actually on a cool with Salim Ramsey, who's the

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Speaker 1: glogle head of ice Shares at Black Crop and um.

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Speaker 1: It was just an introductory called. You know, we haven't

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Speaker 1: spoken before, so we were just chatting, and I was

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Speaker 1: telling him that I was getting really interested in the

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Speaker 1: sort of science of indexing, you know, what was what

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Speaker 1: was kind of underpinning the E t F tidal wave

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Speaker 1: if you like, that's coming through markets and and he

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Speaker 1: and I said, I'm kind of getting into the history

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Speaker 1: of it of how the how Wall three got Bill?

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Speaker 1: And and I said, I'm reading Capital Ideas by by

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Speaker 1: Peter Bernstein. You know the history of Wall Street. And

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Speaker 1: he said, ah, well, you you must know about Matt mcclown. Then, Um,

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Speaker 1: I said, I don't actually know that name. He said,

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Speaker 1: he said, you must know this guy. This is this

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Speaker 1: is the guy. This is where it started. And I

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Speaker 1: did the obvious. I thought it was Vogel or whatever.

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Speaker 1: He said, No, it's Matt. He's still around. He's in

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Speaker 1: Wine Country having you know this great life. I said,

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Speaker 1: I haven't seen his name in the book though, and

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Speaker 1: he said keep reading. And then sure enough I was

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Speaker 1: reading couple ideas like one or two weeks later, and

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Speaker 1: I read this. The first wedge into this system was

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Speaker 1: driven by a man with the fine Scottish name of

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Speaker 1: John Andrew mcquowen. Mcquowan has a boyis mop of hair that,

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Speaker 1: though now gray as white, still tumbles down over his forehead.

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Speaker 1: So I thought, okay, I gotta I gotta track down Mac. Um. So, yeah,

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Speaker 1: I I sort of tracking down. He's also on the

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Speaker 1: board of Dimensional Fund Advisors, which of course is just

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Speaker 1: getting into the TF industry now, and they helped me

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Speaker 1: get ahold of Mac and he was gracious enough to

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Speaker 1: do an interview and it was. It was fascinating stuff. Mac.

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Speaker 1: Can you rewind the Clark fifty plus years now and

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Speaker 1: tell us how did you get the idea for the

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Speaker 1: index one? Well, it's the story is kind of convoluted,

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Speaker 1: as as all kinds of evolutionary transformative processes seemed to

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Speaker 1: be when I when I was doing graduate work at Harvard,

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Speaker 1: I was Harvard Business School. I was working with a

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Speaker 1: professor from from Sloan School at M I T. Who

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Speaker 1: had collected a bunch of data from Barrens on common

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Speaker 1: stock on Friday closes. And I was doing programming. I

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Speaker 1: had been involved in programming, and I was fascinated by

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Speaker 1: the parent discrepancy between the behavior of share prices over

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Speaker 1: time from a normal probability distribution. You know a nice

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Speaker 1: bell shaped curve. Well, it doesn't actually look like that.

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Speaker 1: And fast forward about four or five years and I'm

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Speaker 1: in New York and I ended up at a meeting

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Speaker 1: through more serendipity at Merrill Lynch when Jim, Professor Jim Lorrie,

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Speaker 1: and Professor Lawrence Fisher we're presenting there are initial findings

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Speaker 1: on return on common stocks that Mary Lynch had funded.

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Speaker 1: And I went up to p pressor Lori afterwards, and

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Speaker 1: I asked him a question about these fat tailed distributions

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Speaker 1: as they were coming to be known, and he said, WHOA, Well,

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Speaker 1: we have a graduate student who knows something about that subject.

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Speaker 1: That's his dissertation. His name is Gene Fama. And one

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Speaker 1: thing led to another, and I went out to visit

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Speaker 1: my family was to Chicago, and I ended up spending

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Speaker 1: them the Monday morning after Thanksgiving of I remember vividly

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Speaker 1: having a conversation with Jeane Farmer, and he introduced me

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Speaker 1: to Merton Miller, and uh, all of a sudden, I

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Speaker 1: realized that this is what I was suspicious of, was

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Speaker 1: maybe not entirely ill founded, but it was naive relative

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Speaker 1: to what Jean was actually working on. Well, one thing

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Speaker 1: led to do another, and I ended up being asked

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Speaker 1: by an executive at IBM to give a talk at

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Speaker 1: the IBM Executive Center at San Jose, south of San

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Speaker 1: Francisco in January of sixty four, and in the audience

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Speaker 1: was Ransom Cook, who was the chairman of Wells Fargo.

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Speaker 1: Well I went back to New York and in a

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Speaker 1: few days later, a week later, I got a phone

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Speaker 1: call from from Ransom Cook and he says, I would

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Speaker 1: like you to come back to San Francisco and meet

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Speaker 1: some of my colleagues. So of course, I took a

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Speaker 1: couple of days off and went back to San Francisco,

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Speaker 1: and I met a number of people, and I went

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Speaker 1: back into Ransom's office. He said to me, so, how

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Speaker 1: much how much money are you making in your in

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Speaker 1: your job in New York? And I said six thousand

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Speaker 1: dollars a year. And he said, how about I offered

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Speaker 1: you a job for eighteen thousand. Would you come to

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Speaker 1: work for me starting immediately? Well, you could have. You

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Speaker 1: could have knocked me over with a feather. So I

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Speaker 1: get back to New York and I told my wife

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Speaker 1: about this, and then she's not exactly thrilled. But the

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Speaker 1: long and the short of it is, I took the job,

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Speaker 1: and we moved to San Francisco and we began to

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Speaker 1: work on what Ransom called the investment management problem. Bear

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Speaker 1: in mind that computers had just come to the four

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Speaker 1: in in in major banks, and they were mostly being

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Speaker 1: used for accounting purposes. That was, which is exactly what

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Speaker 1: had been handled by those with green eye shades and

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Speaker 1: arm bands prior. But now the costs had gone up

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Speaker 1: by an order of magnitude, and Ransom said to me,

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Speaker 1: I think it's about time we started looking at what's

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Speaker 1: going on in the data. Well, you gotta get that

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Speaker 1: he was incredibly forsightful, and the fact that I was

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Speaker 1: hired by the chairman really meant that the primary operatives

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Speaker 1: in the Investment Department and in the Trust department. Um

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Speaker 1: really didn't have very much to say, especially since he

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Speaker 1: wanted me to set up an independent group to do

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Speaker 1: the work, which we did and he personally funded it.

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Speaker 1: We were removed from the Wealth Fargo budgeting process and

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Speaker 1: given a budget by the Chairman. Well, Um, I have

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Speaker 1: to tell you. The first thing I did was go

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Speaker 1: back to Chicago and have a conversation with Jane Farmer,

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Speaker 1: and we began. He introduced me to Myron Scholes and

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Speaker 1: Fisher Black, and I made a deal with those guys

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Speaker 1: to come work on this problem. Uh. And before all

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Speaker 1: of the death settled, which was several odd years later,

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Speaker 1: we had had twelve different academic consultants working in various

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Speaker 1: in various ways and the various aspects of this puzzle.

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Speaker 1: But what's really interesting about those twelve is that today,

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Speaker 1: if you look back at those twelve. Six of them

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Speaker 1: have won Nobel Prizes. So there was a heady group

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Speaker 1: and how that happened I don't know, but Ransom Cook

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Speaker 1: and his successor, Dick Cooley funded that research effort. And

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Speaker 1: the upshot was what we called market portfolios. That you

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Speaker 1: you were better off with a portfolio containing every name

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Speaker 1: in the market than any subset, including any index. In fact,

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Speaker 1: Larry Fisher made an astute observation and he simulated every

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Speaker 1: possible portfolio from the original CRISP data set, which started

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Speaker 1: in and ended in nine six. And what do you

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Speaker 1: noticed was the distributions were bimodal, and what differentiated the

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Speaker 1: two modes with whether the portfolio contained IBM or not.

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Speaker 1: So if you had missed IBM, you would have had

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Speaker 1: a distinctly different investment experience if you had owned everything

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Speaker 1: on the New York Stock Exchange. You mentioned the investment

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Speaker 1: management problem as you described, and can you tell us

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Speaker 1: a little bit more about what that issue was as

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Speaker 1: you guys saw it? Well, I don't. I'll tell you, Sam,

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Speaker 1: I thought about that question an awful lot of in

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Speaker 1: over the years, for various reasons. But I can I

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Speaker 1: can kind of give you my synoptic view of the

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Speaker 1: investment management problem. Ransom didn't like the way UH this

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Speaker 1: the investment management process was working at the bank. He

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Speaker 1: was suspicious of the way portfolios were being constructed, just

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Speaker 1: pure intuition. Let me give you a specific example. Wells

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Speaker 1: was just getting into the trust UH advisory business for

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Speaker 1: pension funds at the time, and the largest client they had,

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Speaker 1: whose name I remember vividly and will not identify, had

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Speaker 1: a pension fund of about a half a billion dollars

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Speaker 1: and it was invested in twenty five names. And his

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Speaker 1: intuition was, and he wouldn't have stated it quite this way,

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Speaker 1: but that that part was under diversified. Well that would

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Speaker 1: go down as the understatement of the month. And he

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Speaker 1: and he couldn't get an answer out of the investment

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Speaker 1: department that satisfied him. And that is exactly why he

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Speaker 1: went it was invited to and went to this conference

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Speaker 1: that IBM had put on about down in San Josey

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Speaker 1: about bringing analytical procedures to databases, and so it just

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Speaker 1: it just was an outcropping of his intuition. Well, of

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Speaker 1: course he was exactly right, as you work through all

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Speaker 1: of this stuff that we were doing in those days.

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Speaker 1: The one thing that struck me and a lot of

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Speaker 1: us as just how representative of the New York Stock

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Speaker 1: Exchange is or isn't. So we discovered that NICO had

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Speaker 1: collected a bunch of data on Japan East first section

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Speaker 1: of the Tokyo Exchange, and Wells Fargo used this relationship

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Speaker 1: to NICO, actually too the Industrial Bank of Japan who

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Speaker 1: had in turn and introduced me to NICO, and we

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Speaker 1: got an opportunity to run some tests on the NICO data.

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Speaker 1: And basically what we found was the same thing, which

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Speaker 1: is the best portfolio with the portfolio that contained all names,

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Speaker 1: same exact algorithms, same assumptions, same everything. So talk about

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Speaker 1: an out of sample experience that's about as distant from

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Speaker 1: from sample bias as you can get. So anyway, that

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Speaker 1: is kind of the base and we call it from

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Speaker 1: which what we called, as I said, we called the

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Speaker 1: market market funds. And we began to look at the

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Speaker 1: SPS just and remember the SNP didn't actually exist until

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Speaker 1: somewhere around nineteen fifty seven or fifty six, or fifties

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Speaker 1: seven or eight or nine somewhere there. Prior to that,

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Speaker 1: there were variations that SMP had but the five itself,

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Speaker 1: I think originated in the early in the mid fifties,

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Speaker 1: and then they went back and and and back assembled

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Speaker 1: the data to recalculate the SMP five hundred back I

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Speaker 1: forget how far, quite a few decades, several decades, four

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Speaker 1: or five decades. So while they were doing that, we

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Speaker 1: got to know the SMP people and what was the

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Speaker 1: basis upon which they chose the five stocks. And that

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Speaker 1: made a lot of us pretty suspicious because instead of

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Speaker 1: having a random sample or some kind of like the

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Speaker 1: doll thirty index, which is pretty subjective as well, the

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Speaker 1: investment committee at SMP was choosing the five hundred, So

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Speaker 1: we were kind of right back in the same puzzled.

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Speaker 1: But in any event, the S and P five hundred

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Speaker 1: was becoming the benchmark by which people were judging portfolios.

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Speaker 1: So it was perfectly obvious that the thing you should

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Speaker 1: do is just offer the SMP five hundred at a

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Speaker 1: price about a quarter of what they were selling investment

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Speaker 1: management for at the time, which was by way about

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Speaker 1: about a hundred basis points versus. And of course now

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Speaker 1: that is down to five or whatever it is. But

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Speaker 1: the point of the matter is what was clear to

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Speaker 1: us that I think pretty much at the beginning was

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Speaker 1: that the more names you had in the portfolio, the better.

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Speaker 1: And we were instrumental also in getting the beginning of

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Speaker 1: data collected for the American Exchange and also the OTC market,

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Speaker 1: which became NASDAC. You mentioned the s and you a

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Speaker 1: portfolio that s and pole of hundreds of way to go,

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Speaker 1: but that that wasn't the first fund, wasn't. That wasn't

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Speaker 1: what you did with the very first one. Can you

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Speaker 1: tell us a bit about the construction of that one? Well,

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Speaker 1: the first the first fund was actually the the All

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Speaker 1: New York Fund, which was about six hundred names. But

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Speaker 1: we made the mistake initially of equal weighting the portfolio

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Speaker 1: in our simulations, and it became apparent right away that

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Speaker 1: there was something wrong with that. And of course what

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Speaker 1: happened was we realized that it needed to be market

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Speaker 1: capitalization weighted, not equal weighted, because of course, what happened

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Speaker 1: when you equal way of portfolio like that as you

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Speaker 1: overwake the riskiest stocks. So the market cap weights were

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Speaker 1: became all of a sudden the unambiguous preference for those

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Speaker 1: kinds of portfolios. But it was not the SMP. It

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Speaker 1: was the All New York I have a question, so,

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Speaker 1: and I like asking this about people who were right

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Speaker 1: there and at the option moment, when you locked into

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Speaker 1: that moment where you're like, the more stocks the better,

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Speaker 1: the diversified portfolio is better, which is obviously the inception

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Speaker 1: moment of what would be the index fund. Did you

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Speaker 1: know is a big idea at the time or was

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Speaker 1: it just another you know, part of your day and

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Speaker 1: you had your mind on other things like how much

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Speaker 1: did you identify the legs this idea had? Well, I

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Speaker 1: can tell I can tell you right now I did

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Speaker 1: not have my mind on anything else. That I promise

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Speaker 1: you my work week and those in those days was

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Speaker 1: about eighty hours and you know, and and the Wealth

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Speaker 1: Fargo people, to their credit realize that. And you know,

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Speaker 1: within a very short time, my my wages went from

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Speaker 1: eighteen thousand to forty So I was by far the

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Speaker 1: highest paid, uh young vice president at well Fargo Bank.

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Speaker 1: And I was the reason I I got away with it.

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Speaker 1: It was because the chairman approved it. But if you

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Speaker 1: don't think I wasn't conscious of the fact that we

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Speaker 1: had uncovered something serious that I was, all of us were.

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Speaker 1: I don't mean to say that I was alone by

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Speaker 1: no means. I mean Myron and Fisher and and we

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Speaker 1: we had a lot of We had a lot of

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Speaker 1: influence from Jack Trayner, who was a very important thinker

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Speaker 1: about this topic in the beginning. Uh. And the more

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Speaker 1: we had conversations with various academics, including people from M I. T.

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Speaker 1: And and Berkeley and Stanford and so forth, the more

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Speaker 1: academic interest was surfaced. And I think that while the

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Speaker 1: Chicago guys were leading the crowd, and especially Jeane Parma

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Speaker 1: and Larry Fisher and Jim Laurie and and and the

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Speaker 1: godfather I think was really Merton Miller. You have to

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Speaker 1: give him probably the seminal original credit for the analytical

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Speaker 1: foundation that just the Chicago School was based on. But

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Speaker 1: when you got right down to it, uh, this was

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Speaker 1: still with the influence of computers and data and it's

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Speaker 1: very nascent years. Remember I'm very fond of pointing out

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Speaker 1: that the first stock traded was that was the India East.

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Speaker 1: It was the East India Company. It was traded on

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Speaker 1: the on the fledgling Amsterdam Exchange in sixteen o two.

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Speaker 1: And it wasn't until nineteen that we had an algorithm

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Speaker 1: for measuring risk adjusted performance. So for three d and

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Speaker 1: sixty six years people were constructing portfolios with no fee

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Speaker 1: back loops. Yeah, I think, you know, we take a

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Speaker 1: lot of that for granted, for sure. And I guess

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Speaker 1: I would say, Okay, so you have this big idea.

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Speaker 1: You know it's a good idea. Can you talk about

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Speaker 1: how you tried to implement it and the first funds

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Speaker 1: and and you know how it went to try to

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Speaker 1: get to actually put money into in indexing. Well again,

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Speaker 1: you know, all I would say is serendipity rears its

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Speaker 1: proverbial head, all right. Keith Schwaider it was a graduate

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Speaker 1: of the Graduate School of Business from Chicago and his

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Speaker 1: family owned Sampson I in Denver, and Jim Laurie and

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Speaker 1: introduced me to Keith, and he came to San Francisco

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Speaker 1: and we had company. We had discussions about this and immediately, uh,

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Speaker 1: he wanted to see some of the samson I pension

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Speaker 1: fund invested in and what we did call an index fund.

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Speaker 1: So we we we kind of invented an index fund

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Speaker 1: that would would work. And that was the first one. Well.

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Speaker 1: Immediately thereafter, or very close to immediately there after, the

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Speaker 1: the Wells Fargo Pension Fund. We had a big shot

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Speaker 1: of capital put into Remember this is a commingled trust

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Speaker 1: now it's not a registered mutual fund. But the long

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Speaker 1: and the short of it is, it became apparent very

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Speaker 1: soon that we needed broader, diversified portfolios. And because of

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Speaker 1: SMP five was such a popular a referent, it became

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Speaker 1: apparent that that was one of the places to go.

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Speaker 1: And of course it wasn't long thereafter until we had

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Speaker 1: an opportunity to meet Jack Bogel and and and he

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Speaker 1: was interested in and he was already interested for his

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Speaker 1: own reasons and the idea of what amounted to an

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Speaker 1: index fund. I'm not sure he called it that. In fact,

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Speaker 1: I'm not sure anybody did. But I got introduced to

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Speaker 1: Jack Bogel by a former SEC commissioner who I had

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Speaker 1: met at the University of Chicago's Center for Research and

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Speaker 1: Securities Prices that LORI engineered. Shortly after the the you know,

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Speaker 1: the security that Center for Security Research and Securities Prices,

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Speaker 1: the christ Data came into existence. He created this bi

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Speaker 1: annual seminar twice a year seminar. It had about thirty

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Speaker 1: different major financial institutions attending, and and and that that

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Speaker 1: Pow Wow two Day, Powell Spring and Fall. I went

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Speaker 1: to the very first one, which was in sixty four,

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Speaker 1: and I went to everyone until I left well Fargo,

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Speaker 1: and they invited me back on several occasions uh thereafter.

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Speaker 1: So I was not only through my my period that

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Speaker 1: through seventy four as well, but pretty close to n

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Speaker 1: I was still going to the CRISP Seminar. Well, that's

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Speaker 1: where all this stuff was being discussed. First National Bank

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Speaker 1: of Chicago, JP, Morgan, Chase, you know, all the major

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Speaker 1: names in banking. They were all members of the CRISP Seminar.

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Speaker 1: And we're listening to all this stuff going on. Well,

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Speaker 1: so this, this this influence that that index Funds was

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Speaker 1: beginning to have, wasn't was not limited to Wells Fargo

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Speaker 1: by any stretch of the imagination. And a quick quick question.

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Speaker 1: You mentioned meeting Bogel. Were you still in the early

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Speaker 1: early seventies here or is this more towards I was

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Speaker 1: around seventy four. I'm going to say, Okay, so did

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Speaker 1: he come to you. That's after he left Wellington, which

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Speaker 1: was an active fund he was it was it was

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Speaker 1: during okay, it was during the formation of Gotcha it's

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Speaker 1: interesting um that obviously was a huge situation UM where

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Speaker 1: Bogol was basically fired by the people he had acquired it. Uh.

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Speaker 1: When he was running Wellington's he was an advocate of

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Speaker 1: active funds to a degree. Then he starts Vanguard as

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Speaker 1: a back office company. The index fund just is a

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Speaker 1: is a way for him to not manage money while

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Speaker 1: managing money. It was kind of an interesting loophole. My

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Speaker 1: question to you is, as you saw a Vanguard come

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Speaker 1: out with this unique mutual ownership structure and then they

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Speaker 1: go and get the index fund, how did you see that? Like,

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Speaker 1: because the mutual ownership structure I think probably deserves a

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Speaker 1: lot of credit for constantly lowering the fees of the

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Speaker 1: index funds over the years, But how did you see

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Speaker 1: the potential of Vanguard or only on as well? Well, Eric,

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Speaker 1: you put your finger on one of my hot buttons.

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Speaker 1: Let me tell you what actually happened at Wills Fargo.

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Speaker 1: We got registered at the SEC what was called the

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Speaker 1: Stagecoach Fund. But in June, the Supreme Court came down

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Speaker 1: in a decision of the Investment Company Institute versus the

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Speaker 1: Controller of the Currency As giving permission to City Bank

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Speaker 1: to sell commingled mutual funds being a violation of Glass Stiegel.

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Speaker 1: Remember Glass Steagle separated investment banks from commercial banks. We

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Speaker 1: were ready to sell the Stagecoach fund in in Ransom

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Speaker 1: Cooks Branches, and it got shot down by that Supreme

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Speaker 1: Court decision. I see I v. Camp Descent June. Look

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Speaker 1: it up. The banks could sell co mingled trusts, but

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Speaker 1: they could not sell registered mutual funds. They were securities,

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Speaker 1: not loans, and not trusts. Remember the idiocy emerging from

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Speaker 1: Congress created those laws. They did not know what they

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Speaker 1: were doing, and I guess I would say as usual.

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Speaker 1: So you basically were unable to really capitalize and commercialize

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Speaker 1: on the idea because of that ruling in law and

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Speaker 1: that when when I met Jack Bogel and told Ransom

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Speaker 1: and Dick what Jack wanted to do, he said, give

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Speaker 1: him Allery's Our research were restricted. We can't do it,

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Speaker 1: but it needs to get done. You can read in

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Speaker 1: Jack's writings too, he he gave us credit for it.

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Speaker 1: Did you and Jack exchange Christmas cards from therefore? Yeah?

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Speaker 1: I mean I had a very personal relationship with Jack.

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Speaker 1: We weren't really very close, remember we were we were

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Speaker 1: oceans apart or not like what a continent apart. I

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00:27:21,680 --> 00:27:26,360
Speaker 1: mean I saw him fairly often. And before Jack died,

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Speaker 1: you know, we got the the CMME Award. I can't

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Speaker 1: even think when that was for innovation in corporate finance.

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Speaker 1: So mac h Wells Fargo was limited in in the

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Speaker 1: way it could deploy this, and obviously Jack benefit immensely

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Speaker 1: from that. But I think we should also note that

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Speaker 1: um Wells Fargo did, did capitalize on on it, and

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Speaker 1: had an incredible legacy because what you built, what you

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00:28:04,119 --> 00:28:09,399
Speaker 1: started at Wells eventually became Barkley's Global Investors, if I'm right,

399
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Speaker 1: and that Lawrence Stye Shares, which was then brought by

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Speaker 1: black Rock. So um, it was an incredible it was

401
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Speaker 1: the beginning of an incredible uh kind of achievement. Well

402
00:28:23,640 --> 00:28:26,280
Speaker 1: that's right. I mean, you know, we created well for

403
00:28:26,359 --> 00:28:30,920
Speaker 1: our investment advisors to be the advisor to the Stagecoach Fund.

404
00:28:31,880 --> 00:28:35,760
Speaker 1: That was the original plan before I see I V

405
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Speaker 1: Camp came down. Ah and as a matter of fact,

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Speaker 1: Coolly made me the chairman of of w f I

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Speaker 1: A and we hired an independent guy to become the CEO.

408
00:28:50,280 --> 00:28:53,719
Speaker 1: Came from outside the bank and uh, and we were

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Speaker 1: all we were all literally we were literally ready to

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Speaker 1: go to market when I see I V camp came

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Speaker 1: down and I remember June one, like I was yesterday,

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Speaker 1: we were on the on the cost of doing that. Mac.

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Speaker 1: I have a question related to that time. How what

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Speaker 1: was the computing speed and prowess like at that time,

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Speaker 1: because obviously I'm I'm guessing these machines were huge. How

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Speaker 1: what was it like to feed data into that? And

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Speaker 1: how long were you waiting to get the feedback? I mean,

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Speaker 1: your feedback loop was one thing, but like you know,

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Speaker 1: this was state at the state of the art at

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Speaker 1: the one at the time. Right. Well, you're that's something

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Speaker 1: very very good question, Joel, because well, you know, another

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Speaker 1: another innovation that was just coming to the four in

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Speaker 1: those days was what we call time sharing. Well, time

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Speaker 1: sharing was the idea that you could hook up a

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Speaker 1: bunch of different as it were, teletype machine to a

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Speaker 1: single computer and the computer could multiplex the input and

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Speaker 1: output from a teletype machine to a computer. And the

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Speaker 1: IBM three six seven was the first from commercial scale

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Speaker 1: computer of that kind. And well farther we were just

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Speaker 1: adopting three sixties at the time, and I convinced coolly

431
00:30:27,960 --> 00:30:32,360
Speaker 1: to let us buy a sixty seven and so that

432
00:30:32,440 --> 00:30:35,360
Speaker 1: we could have a terminal, actually two terminals in the

433
00:30:35,440 --> 00:30:41,360
Speaker 1: Management Sciences department. And the reason he went for it

434
00:30:41,600 --> 00:30:45,440
Speaker 1: was that he another group at the bank we're working

435
00:30:45,480 --> 00:30:49,720
Speaker 1: on the idea of an automated teller terminal, and it

436
00:30:49,840 --> 00:30:53,440
Speaker 1: became apparent that we could utilize we could have teller

437
00:30:53,560 --> 00:30:57,200
Speaker 1: terminals that were being multiplexed by the same kind of

438
00:30:57,240 --> 00:31:00,320
Speaker 1: a computer where when you went into the bank to

439
00:31:00,400 --> 00:31:02,760
Speaker 1: cash your check, you could actually in real time discover

440
00:31:02,880 --> 00:31:05,600
Speaker 1: whether or not there was a balance into your account.

441
00:31:07,560 --> 00:31:10,320
Speaker 1: So there was there was a movement in the direction

442
00:31:10,680 --> 00:31:17,480
Speaker 1: of what I call remote computing. And we had we

443
00:31:18,480 --> 00:31:21,719
Speaker 1: the Management Science department had access to that three sixty

444
00:31:21,800 --> 00:31:25,240
Speaker 1: six seven immediately upon them when it came out, and

445
00:31:25,360 --> 00:31:29,480
Speaker 1: that was about nine sixties six or six seven, something

446
00:31:29,600 --> 00:31:35,000
Speaker 1: like that. So we had gone from having to fuss

447
00:31:35,080 --> 00:31:40,080
Speaker 1: around at an IBM data center h to having our

448
00:31:40,160 --> 00:31:45,600
Speaker 1: own U teletype machine with access to the data that

449
00:31:45,720 --> 00:31:51,400
Speaker 1: we stored on the three seven. So that was another

450
00:31:52,080 --> 00:31:59,120
Speaker 1: very critical thing that that addict Cooley did was introduced

451
00:31:59,160 --> 00:32:04,959
Speaker 1: time sharing or multiplexing, right terminal multiplexing into the picture.

452
00:32:05,920 --> 00:32:08,560
Speaker 1: And that was about the same time so all of

453
00:32:08,640 --> 00:32:11,920
Speaker 1: a sudden, the productivity of of these guys doing analytical

454
00:32:12,040 --> 00:32:16,640
Speaker 1: work skyrocketed because you didn't have to go stand in

455
00:32:16,680 --> 00:32:20,040
Speaker 1: the front of a computer and feed punch cards into it.

456
00:32:21,360 --> 00:32:23,040
Speaker 1: The long and the short of it is, it was

457
00:32:23,120 --> 00:32:26,720
Speaker 1: concurrent that that occurred. And again I would characterize that

458
00:32:26,800 --> 00:32:38,000
Speaker 1: as yet more serendipity. You know, you said earlier you

459
00:32:38,080 --> 00:32:41,520
Speaker 1: worked with what was it six people who went on

460
00:32:41,600 --> 00:32:48,280
Speaker 1: in one Nobel Prizes um And I mean just you were,

461
00:32:49,200 --> 00:32:52,080
Speaker 1: you know, a pioneer effectively right and working with a

462
00:32:52,160 --> 00:32:55,120
Speaker 1: ton of smart people. And I'm just wondering, like, for

463
00:32:55,280 --> 00:32:58,480
Speaker 1: those of us who get to, you know, you know,

464
00:32:58,760 --> 00:33:01,360
Speaker 1: look back on your story, what do you feel like

465
00:33:01,520 --> 00:33:08,360
Speaker 1: the keys of that of good collaboration were. Well, that's

466
00:33:08,400 --> 00:33:11,520
Speaker 1: a that's a very that's a very interesting question, Joel.

467
00:33:11,520 --> 00:33:13,080
Speaker 1: I mean, we spend the rest of the day on

468
00:33:13,200 --> 00:33:18,440
Speaker 1: that question. A. H. Well, you need to have a

469
00:33:18,560 --> 00:33:22,360
Speaker 1: certain openness. At the same time, you have a very

470
00:33:22,440 --> 00:33:27,120
Speaker 1: critical mind, and you're you're and you're willing to challenge anything.

471
00:33:27,760 --> 00:33:31,440
Speaker 1: I would characterize it as the Chicago school at work.

472
00:33:33,080 --> 00:33:35,440
Speaker 1: I mean, I think that you know, Milton Freeman was

473
00:33:35,480 --> 00:33:40,000
Speaker 1: a champion of that point and several others before him,

474
00:33:40,080 --> 00:33:44,800
Speaker 1: including fa Hiak and so forth. But you know, really

475
00:33:44,880 --> 00:33:50,840
Speaker 1: critical thinking and and economics was generally missing. And tell

476
00:33:50,920 --> 00:33:55,560
Speaker 1: about that same time. Now. When I say in economics,

477
00:33:55,600 --> 00:34:00,400
Speaker 1: I'm referring to predominantly macro economics and not micro econ comics.

478
00:34:01,640 --> 00:34:05,800
Speaker 1: But critical thinking kind of came to both and with

479
00:34:05,960 --> 00:34:09,600
Speaker 1: the onset of the computer and the database, and prior

480
00:34:09,680 --> 00:34:13,520
Speaker 1: to that there was an awful lot of I regard Kenes,

481
00:34:13,600 --> 00:34:18,439
Speaker 1: for example, as gibberish. It just it doesn't make any

482
00:34:18,520 --> 00:34:21,720
Speaker 1: sense when you put the blade to it. It doesn't

483
00:34:21,760 --> 00:34:27,439
Speaker 1: stand the test. That just wasn't available until considerably later.

484
00:34:30,360 --> 00:34:34,600
Speaker 1: I guess it was like a perfect storm in that period,

485
00:34:36,040 --> 00:34:39,400
Speaker 1: and it was influencing all kinds of things, index funds

486
00:34:39,480 --> 00:34:45,320
Speaker 1: being only one. So can you apply that that critical

487
00:34:45,440 --> 00:34:53,239
Speaker 1: thinking to the current moment? Oh well, I'm not a

488
00:34:53,360 --> 00:34:59,759
Speaker 1: very big fan of um, I'm not a very big

489
00:35:00,000 --> 00:35:07,400
Speaker 1: out of Let me call it h on grounded meaning

490
00:35:07,800 --> 00:35:15,480
Speaker 1: without data analysis. I just I can't characterize it as

491
00:35:15,640 --> 00:35:23,800
Speaker 1: subjectivism versus data driven science, and the world is a

492
00:35:23,960 --> 00:35:30,040
Speaker 1: wash in subjectivism and data. Data driven science is only

493
00:35:30,120 --> 00:35:37,040
Speaker 1: beginning to invade the hallowed halls of subjectivism and and

494
00:35:37,120 --> 00:35:41,759
Speaker 1: of course politics is the is the epicenter of subjective

495
00:35:41,800 --> 00:35:45,360
Speaker 1: is along with religion. And I can't tell the difference

496
00:35:45,400 --> 00:35:49,880
Speaker 1: between politics and religion, but Matt can. Matt, what about

497
00:35:50,719 --> 00:35:55,000
Speaker 1: for the investment industry, for the state of indexing now

498
00:35:55,160 --> 00:35:58,799
Speaker 1: with you know, eleven trillion or whatever we are at now? Um,

499
00:35:59,680 --> 00:36:03,080
Speaker 1: when look at that, now, what's your sort of critical

500
00:36:03,200 --> 00:36:08,440
Speaker 1: thought crisis? Well, Sam, you know, eleven trillion is a

501
00:36:08,680 --> 00:36:14,880
Speaker 1: major understatement, right, because that's only the admitted indexing. Just

502
00:36:15,200 --> 00:36:19,600
Speaker 1: think about the closet indexing. There's a lot of incentive

503
00:36:19,640 --> 00:36:21,480
Speaker 1: to keep fees high, and the way to do that

504
00:36:21,680 --> 00:36:23,960
Speaker 1: is to do an index fund. But call it something else,

505
00:36:25,320 --> 00:36:26,880
Speaker 1: or let me put it another way to do a

506
00:36:27,000 --> 00:36:32,120
Speaker 1: market portfolio. Right, it's not necessarily an inda, but there's

507
00:36:32,160 --> 00:36:38,320
Speaker 1: a you know, closet passive is everywhere. I would my

508
00:36:38,480 --> 00:36:40,480
Speaker 1: guess is at least two thirds of the market is

509
00:36:40,560 --> 00:36:46,839
Speaker 1: passive and maybe maybe maybe more like But it would

510
00:36:46,880 --> 00:36:49,440
Speaker 1: be very difficult to put a number to that because

511
00:36:49,480 --> 00:36:52,880
Speaker 1: you have to collect an off lot of data. But

512
00:36:53,440 --> 00:36:56,359
Speaker 1: it's eleven trillion is an understatement by a lot. Um,

513
00:36:56,880 --> 00:37:00,600
Speaker 1: let me jump in here with that whole premise, because um,

514
00:37:01,400 --> 00:37:04,640
Speaker 1: eleven trillion is the public fund that's quote passive, but

515
00:37:04,800 --> 00:37:06,600
Speaker 1: some of that is in smart data where they took

516
00:37:06,640 --> 00:37:11,520
Speaker 1: the index fund and they put active metrics and overlays

517
00:37:12,040 --> 00:37:13,640
Speaker 1: and on the flip. You're right, there's a bunch of

518
00:37:13,680 --> 00:37:17,279
Speaker 1: active discretionary funds that basically hold the S and p UM.

519
00:37:18,360 --> 00:37:21,879
Speaker 1: So it's very blurry, which is my answer to people

520
00:37:21,960 --> 00:37:23,879
Speaker 1: when they say, oh my god, passive is taking over.

521
00:37:23,920 --> 00:37:25,480
Speaker 1: It's going to ruin the stock market. We're all going

522
00:37:25,520 --> 00:37:28,080
Speaker 1: to die, which is this sort of fear of But

523
00:37:28,160 --> 00:37:30,920
Speaker 1: I'm like, it's so nuanced and gray between active and

524
00:37:31,000 --> 00:37:34,000
Speaker 1: passive it just doesn't bother me. But I guess I

525
00:37:34,040 --> 00:37:36,400
Speaker 1: would like to get your take on some of the

526
00:37:37,000 --> 00:37:40,480
Speaker 1: people who fear that passive is growing too big and

527
00:37:40,800 --> 00:37:44,560
Speaker 1: is distorting fundamentals could result in some kind of a

528
00:37:44,680 --> 00:37:50,040
Speaker 1: market structure issue. They don't they just don't understand the situation.

529
00:37:50,239 --> 00:37:55,319
Speaker 1: Eric Com, I'm right back to my basic point. It's

530
00:37:55,360 --> 00:38:01,080
Speaker 1: subjectivism versus data driven science. When you when you allow

531
00:38:01,160 --> 00:38:07,120
Speaker 1: the data to speak to you, that clears up that fog. Remember,

532
00:38:08,040 --> 00:38:11,839
Speaker 1: share prices get formed because people are buying and selling shares. Right,

533
00:38:13,160 --> 00:38:16,680
Speaker 1: if buying and selling shares stopped, I don't know what

534
00:38:16,800 --> 00:38:19,520
Speaker 1: you would have for prices. You wouldn't have any prices.

535
00:38:21,239 --> 00:38:23,360
Speaker 1: But do you think buying and selling is going to stop?

536
00:38:24,400 --> 00:38:26,680
Speaker 1: I mean, it's and it's not because somebody thinks they

537
00:38:26,719 --> 00:38:29,719
Speaker 1: know more than the market does. There's a whole lot

538
00:38:29,800 --> 00:38:33,040
Speaker 1: of reasons why people buy and sell, and some of

539
00:38:33,120 --> 00:38:36,560
Speaker 1: it is valuation related, and some of it is liquidity related,

540
00:38:36,600 --> 00:38:39,719
Speaker 1: and some of it is a trust account related, and

541
00:38:39,840 --> 00:38:45,840
Speaker 1: I mean it's just there's an impossible assortment of motives

542
00:38:45,960 --> 00:38:50,200
Speaker 1: behind people buying and selling shares, and that's just that's

543
00:38:50,239 --> 00:38:54,240
Speaker 1: just never gonna stop. I mean, I'm never worried about

544
00:38:54,280 --> 00:38:58,040
Speaker 1: that one. And just generally the e t F Right,

545
00:38:58,120 --> 00:39:02,719
Speaker 1: so you've got index funds were launched. Bogel popularized them

546
00:39:02,760 --> 00:39:05,560
Speaker 1: with Vanguard. He was not a fan of e t F.

547
00:39:05,719 --> 00:39:07,640
Speaker 1: He he wanted people to stick in the long term.

548
00:39:07,719 --> 00:39:09,880
Speaker 1: E t F s come out, they trade, some of

549
00:39:09,920 --> 00:39:12,520
Speaker 1: them are used in place of futures. What's your take

550
00:39:12,560 --> 00:39:13,880
Speaker 1: on the e t F world? And did you ever

551
00:39:13,960 --> 00:39:16,200
Speaker 1: meet Nate Most and Steve Blueman am X when they

552
00:39:16,239 --> 00:39:20,040
Speaker 1: were coming up with their E t F for spy host? Yeah? Sure,

553
00:39:20,520 --> 00:39:22,160
Speaker 1: you've got to remember what on E t F his

554
00:39:23,760 --> 00:39:27,800
Speaker 1: Remember what preceded, You remember closed down the mutual funds,

555
00:39:30,200 --> 00:39:33,600
Speaker 1: and then there was opened end mutual funds, and you

556
00:39:33,760 --> 00:39:36,520
Speaker 1: t F is just an amalgam of those two things, right,

557
00:39:37,640 --> 00:39:40,920
Speaker 1: And we were talking about e t F way before

558
00:39:40,960 --> 00:39:46,799
Speaker 1: they existed. It Again, it's it's right back to foolishness

559
00:39:46,880 --> 00:39:52,080
Speaker 1: in Congress of trying to distinguish between closed end funds

560
00:39:52,160 --> 00:39:55,560
Speaker 1: and opened end funds. It was protocols that were put

561
00:39:55,600 --> 00:40:01,080
Speaker 1: in place by regulation, not not by economics. So you

562
00:40:01,400 --> 00:40:04,400
Speaker 1: you could have an e t F that was closed

563
00:40:04,520 --> 00:40:07,600
Speaker 1: and would be called a closed end fund, and you

564
00:40:07,680 --> 00:40:10,520
Speaker 1: could have a conventional mutual fund that was that is

565
00:40:10,600 --> 00:40:14,480
Speaker 1: open ended, whose shares were traded in a competitive market,

566
00:40:15,640 --> 00:40:19,120
Speaker 1: that would be an e t F. Right, there's just

567
00:40:19,480 --> 00:40:21,560
Speaker 1: there's nothing novel and new about an e t F.

568
00:40:21,640 --> 00:40:25,320
Speaker 1: It's just a it's an amalgam of opened and closed funds.

569
00:40:27,640 --> 00:40:29,560
Speaker 1: We should have had him a long long time ago.

570
00:40:30,440 --> 00:40:34,480
Speaker 1: We we talked about that idea going way back. We

571
00:40:34,640 --> 00:40:37,800
Speaker 1: talked about that idea to the commissioners at the SEC.

572
00:40:38,960 --> 00:40:41,360
Speaker 1: And again I'm the ex commissioner whose name I'm not

573
00:40:41,400 --> 00:40:44,000
Speaker 1: going to use because I haven't seen him in years,

574
00:40:45,080 --> 00:40:47,000
Speaker 1: and I don't even know whether he's alive, but he

575
00:40:47,160 --> 00:40:52,520
Speaker 1: was a champion of thinking through new things, even though

576
00:40:53,320 --> 00:40:56,920
Speaker 1: in his days as a commissioner of the SEC that

577
00:40:57,040 --> 00:41:00,440
Speaker 1: was the last thing that they were doing. They weren't thinking,

578
00:41:00,560 --> 00:41:07,000
Speaker 1: they were just regulating. There's a distinction, Mac, I want

579
00:41:07,000 --> 00:41:12,759
Speaker 1: to ask before we we finish, Presumably you're invested in

580
00:41:12,960 --> 00:41:16,799
Speaker 1: index funds. I imagine, Oh, yes, indeed we are. Are

581
00:41:16,880 --> 00:41:19,800
Speaker 1: you like, are you like a by the total market

582
00:41:19,840 --> 00:41:22,360
Speaker 1: fund kind of guy or do you sort of have

583
00:41:22,640 --> 00:41:28,680
Speaker 1: maybe more index funds that you specialize at all or customize. Well,

584
00:41:28,800 --> 00:41:30,839
Speaker 1: if you take all the different kinds of index funds

585
00:41:30,840 --> 00:41:32,919
Speaker 1: and put them in a portfoli you get a market port.

586
00:41:34,880 --> 00:41:38,440
Speaker 1: So your port you're a very simple portfolio, you know

587
00:41:38,760 --> 00:41:42,279
Speaker 1: kind of investor. Okay, yeah, well I'm an entrepreneur. Most

588
00:41:42,320 --> 00:41:45,680
Speaker 1: of my wealth is involved in startup companies. Right. Well

589
00:41:45,760 --> 00:41:48,839
Speaker 1: that actually I had a really question which was, um,

590
00:41:49,040 --> 00:41:52,000
Speaker 1: you know, Sam in the article reminded me that you

591
00:41:52,080 --> 00:41:56,800
Speaker 1: were a mechanical engineer by training and I'm by by schooling,

592
00:41:57,640 --> 00:41:59,960
Speaker 1: Just wondering how how do you think that informs your

593
00:42:00,120 --> 00:42:03,759
Speaker 1: your outlook? I mean, obviously you're you made a really

594
00:42:03,920 --> 00:42:08,439
Speaker 1: clear point on being you know, praising objectivity and data

595
00:42:08,440 --> 00:42:10,799
Speaker 1: above all else. But like talked about talk to us

596
00:42:10,840 --> 00:42:12,839
Speaker 1: about how you look at the world through the lens

597
00:42:12,920 --> 00:42:16,719
Speaker 1: of a being a mechanical engineer. I grew up on

598
00:42:16,800 --> 00:42:23,279
Speaker 1: a farm. I split my early life between my mother

599
00:42:23,360 --> 00:42:26,520
Speaker 1: and father. My father was a businessman. We lived in

600
00:42:26,600 --> 00:42:29,160
Speaker 1: the edge of this little town, and his elder brother

601
00:42:29,880 --> 00:42:33,000
Speaker 1: rand the family farming operation, which was seven miles away.

602
00:42:34,080 --> 00:42:38,560
Speaker 1: So I ended up growing up in two families uh

603
00:42:38,960 --> 00:42:42,319
Speaker 1: one uh in literally in the country where we were.

604
00:42:42,880 --> 00:42:45,440
Speaker 1: We were growing all kinds of grains and lagoons and

605
00:42:45,520 --> 00:42:49,239
Speaker 1: so forth, of feed, the whole assortment of livestock right.

606
00:42:50,480 --> 00:42:53,239
Speaker 1: And I've I've been often asked what because I'm very

607
00:42:53,320 --> 00:42:58,000
Speaker 1: fond of my early life on the farm, And I'm

608
00:42:58,080 --> 00:43:00,640
Speaker 1: often asked what did you learn and when you are

609
00:43:00,719 --> 00:43:04,920
Speaker 1: on the farm? And I have evolved a good answer

610
00:43:04,960 --> 00:43:08,160
Speaker 1: to that question. I learned that you can't bullshit the

611
00:43:08,280 --> 00:43:13,879
Speaker 1: bull It does not work. And if you get into

612
00:43:13,920 --> 00:43:19,040
Speaker 1: the world of data, it's the same point if the

613
00:43:19,200 --> 00:43:22,080
Speaker 1: data doesn't if it's not in the data, it's not

614
00:43:22,320 --> 00:43:28,959
Speaker 1: there now, mind you. Teasing conclusions out of big data

615
00:43:29,040 --> 00:43:33,360
Speaker 1: sets is a complicated topic, and of course I began

616
00:43:33,480 --> 00:43:35,480
Speaker 1: to do just exactly that when I was in an

617
00:43:35,560 --> 00:43:40,280
Speaker 1: engineering student. And remember that was when computers first began,

618
00:43:42,040 --> 00:43:44,600
Speaker 1: So I started programming computers when I was still an

619
00:43:44,640 --> 00:43:49,480
Speaker 1: engineering student. I mean, I mean, don't don't ask me

620
00:43:49,560 --> 00:43:51,560
Speaker 1: why because I have no idea. That's just the way

621
00:43:51,640 --> 00:43:55,640
Speaker 1: it was. Um. The last question we always ask gun trillions.

622
00:43:56,120 --> 00:43:59,239
Speaker 1: Really curious what you're gonna say? Um, what is your

623
00:43:59,280 --> 00:44:07,200
Speaker 1: favorite each you have ticker? Well, uh, I have a

624
00:44:07,239 --> 00:44:11,120
Speaker 1: good answer to that. None, I have to tell you,

625
00:44:11,239 --> 00:44:15,640
Speaker 1: I think um. Jack Bogel had a very similar response. Now,

626
00:44:16,160 --> 00:44:18,480
Speaker 1: Vogel's response was, because you know, he wasn't a fan

627
00:44:18,520 --> 00:44:23,360
Speaker 1: of ets, was c r z y, which isn't a ticker,

628
00:44:23,520 --> 00:44:26,719
Speaker 1: but it's what he thinks of them. You guys are

629
00:44:26,800 --> 00:44:30,279
Speaker 1: similar in that in that answer with not really giving one.

630
00:44:33,440 --> 00:44:35,240
Speaker 1: Mac Sam, thanks so much for joining us in trillion.

631
00:44:36,040 --> 00:44:44,000
Speaker 1: My pleasure, You guys, thanks for listening to Trillions until

632
00:44:44,080 --> 00:44:45,880
Speaker 1: next time. You can find us on the Bloomberg terminal,

633
00:44:46,040 --> 00:44:50,360
Speaker 1: Bloomberg dot com, Apple Podcasts, Spotify, and wherever else you

634
00:44:50,480 --> 00:44:52,839
Speaker 1: like to listen. We'd love to hear from you. We're

635
00:44:52,880 --> 00:44:56,880
Speaker 1: on Twitter. I'm at Joel Webber Show. He's at Eric Faltunus,

636
00:44:57,320 --> 00:45:00,840
Speaker 1: and you can find Sam at Sam J. Potter. This

637
00:45:00,960 --> 00:45:04,520
Speaker 1: episode of Trillions was produced by Magnus Hendrickson. Francesca Levy

638
00:45:04,680 --> 00:45:06,719
Speaker 1: is the head of the Homeberk podcast VI