Latest / The Capitalism and Freedom in the Twenty-First Century Podcast / Cliff Asness on Factor Investing and the History of Financial Economics | Hoover Institution

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Cliff Asness on Factor Investing and the History of Financial Economics | Hoover Institution · The Capitalism and Freedom in the Twenty-First Century Podcast

• 0:09
  - This is the Capitalism and Freedom in
• 0:10
  the 21st Century podcast
• 0:11
  and official podcast
• 0:12
  of the Hoover Institution Economic Policy Working Group,
• 0:15
  where we talk about economics, markets, and public policy.
• 0:18
  I'm Jon Hartley, your host Today.
• 0:20
  My guest is Cliff Asness, who is the co-founder
• 0:22
  and managing partner of a QR Capital Management,
• 0:24
  a quantitative investment manager.
• 0:26
  Cliff also is a PhD in finance from the University
• 0:29
  of Chicago Blue School of Business,
• 0:30
  where he studied under the Nobel Prize winner,
• 0:32
  Eugene Fama as well.
• 0:33
  And he's here to talk all about asset pricing
• 0:36
  and financial and economics. Welcome Cliff.
• 0:39
  - Thank you, Jon. Thank you for having me.
• 0:41
  - Well, it's a real honor to have you on
• 0:43
  and really wanna get into, start
• 0:47
  with getting into your earlier, early career.
• 0:50
  Where did you grow up
• 0:51
  and how did you first get interested in
• 0:53
  economics and finance?
• 0:56
  - I was born in Queens, New York.
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  At four, we moved out to suburbia on Long Island
• 1:02
  and I grew up in, you know,
• 1:03
  post World War II suburban housing out there.
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  I was not interested in finance for a while.
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  I was kind of a only half engaged high school student.
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  Mediocre grades, good boards.
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  Ended up, my father found this program at the University
• 1:25
  of Pennsylvania where you got a dual degree.
• 1:27
  You got a degree in from the engineering school
• 1:30
  and from the business school.
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  He said, you should do this.
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  You have no clue what you want to do.
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  So study two things that was about the depth of it.
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  I went there, it, it actually was not me.
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  I was not a young, you know,
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  read the Wall Street Journal trade stocks
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  at the age of seven.
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  It wasn't until I took one
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  or two finance classes as part of the Wharton curriculum
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  that I was like, this stuff's pretty cool.
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  So I, I got interested in a very boring way.
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  I took the classes and,
• 2:00
  and liked them though my dad,
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  again influenced my career a lot
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  because I had always assumed
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  that even though I was studying two fairly mathematical
• 2:10
  topics, that I would go to law school.
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  'cause my dad was a lawyer.
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  I had several uncles who were lawyers.
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  And at some point around junior year, I actually registered
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  for the LSATs and my dad said, what are you crazy?
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  None of us can add you, can you do math?
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  You should do something with math.
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  And I switched it to the GMATs
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  and ended up at the University of Chicago's PhD program.
• 2:34
  - Wow. Wow. That that's that's fantastic.
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  And, and I know, so, so you went to Wharton
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  as Penn Wharton signed engineering and,
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  and finances while Wharton
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  and I think, I don't know if they called it m and t program.
• 2:49
  - They did. It was the early days of the program. It,
• 2:51
  - It still exists and is a, a very, a well-known program
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  that that, that, you know, is a super selected program that,
• 2:59
  that, that students go through.
• 3:00
  And my understanding is that you also met your, you know,
• 3:04
  great longtime friend John Byner who would go
• 3:07
  to Goldman Sachs and you'd kind of meet up with, with later
• 3:11
  and he'd go on to run the fixed income group at Goldman
• 3:14
  Sachs Asset Management for, for many years.
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  You went on to U Chicago for your PhD
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  and you saw you fi finished economics under f
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  what was studying with f like, and,
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  and it's just around my guess the 1980s and,
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  and what, you know, what was U Chicago like at that time,
• 3:32
  what was in the air at that time?
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  And my guess is there were
• 3:35
  so many Nobel Prize winners at the time and,
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  and many of, so the, you know,
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  the so-called Chicago school were around at that time.
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  And my sense is in, in financial economics side
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  of things too, a good number of folks that
• 3:48
  that intersected there as well.
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  I'm, I'm curious what was, what
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  was in the year when you were there?
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  - It was amazing time to be there.
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  I I, I, I think historically there've been a lot
• 3:56
  of amazing times to be at the University
• 3:58
  of Chicago studying economics or, or finance.
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  But for me, I always point out
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  that my entire career was, was in some sense a lucky break
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  because I got there in the, in the late 1980s.
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  I think I showed up in fall of 88.
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  And that was basically just when f
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  and French were doing their first paper on the cross section
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  of expected stock returns.
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  It was probably a working paper at that point.
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  I don't remember exactly.
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  I could be off by a year or so in my stories.
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  I'm getting very old.
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  But I was essentially there when
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  what we'll today call factor investing,
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  what we might call systematic value momentum,
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  systematic momentum investing.
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  I was there at the infancy
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  and that's al there's always some luck to, to these things.
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  And even when you look back,
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  it's funny terms like value investing terms like,
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  like factor investing.
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  We didn't use those terms back then.
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  Factor investing came much later.
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  I'm pretty sure the first couple of f
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  and French papers didn't call like their price
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  to book factor value.
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  I think that came later.
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  But when you have some amazing teachers, fam
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  and French were co-chair of my co-chairs of my dissertation.
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  I think you're sitting in John Cochrane's office.
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  He was on the outside committee of my, my, my dissertation.
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  When you have these amazing teachers
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  and they happen to be breaking ground on what will be
• 5:27
  probably the major field of academic finance
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  for the next 20, 30 years,
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  you've gotta consider yourself pretty, pretty lucky.
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  So it was a pretty amazing place to be
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  - Now, you know, to be there at that time.
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  I'm sure it's incredibly, you know, farmer French factors
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  are, are sort of new that, you know,
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  those eventually published in the, in, in,
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  I think the JFE in 1993.
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  I'm curious who else was also crossing paths
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  with you at that time?
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  You know, I think, you know, when we talk about momentum
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  investing, you know, mark Carhartt, a fellow Fama student
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  was was was their PhD student, Ray Romanowski.
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  I mean these are all people that you hired later,
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  but I'm curious who else was there Maybe at the time,
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  obviously Fama was working with Dimensional Point advisors
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  and David Booth and, and others.
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  Yeah. But you know, Dan Iveson, man Roman went
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  through booths in, in around that same time as well.
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  I mean, did, did you know any of these other people
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  that sort of also like yourself would go on
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  to if they hadn't already started them at the time?
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  Go on to start these massive, massive, you know,
• 6:31
  multi-billion dollar asset managers?
• 6:34
  - Sure. Mark and Ray, again, my memory's fuzzy.
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  I'm pretty sure I knew Ray
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  and I think I knew Mark in, in the program,
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  or at least I, I knew him through mutual friends.
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  You know, I mean you're a PhD student, you know,
• 6:47
  a few years into the program,
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  you're not quite in all the same classes together.
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  And I was a few years ahead of them.
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  Mark and I have an intimate connection, not just hiring him,
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  but the momentum factor that I wrote my dissertation on,
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  he included in
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  what became a four factor model when he analyzed
• 7:04
  mutual funds.
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  So, so we have that connection.
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  And later on in my group at Goldman Sachs, I hired Mark
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  and Ray Ross Stevens was two years
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  behind me in the program.
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  I think it was two years. And I was his official mentor.
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  Somehow I agreed to do that. I didn't even know Ross.
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  I, I, I don't, I don't think I mentored anyone else.
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  But Ross has built an amazing business at,
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  at, at Stone Ridge.
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  So, so he was there.
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  A lot of, you know, big name academics were still there.
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  Merton Miller was still there.
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  I didn't, I I didn't really know Merton until I went
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  to defend my, or propose my dissertation.
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  Scary time to meet Merton when you're
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  presenting your dissertation.
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  And it was amazing.
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  I'm sure a lot of people have the same memory,
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  but the finance seminar was like Tuesday,
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  late afternoon at the University of Chicago.
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  And it, people sat in the whole u and you were at the front
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  and on the far, far right, Fama sat
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  and on the far far left, Merton sat
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  and Merton got into my dissertation.
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  It ended up being very good.
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  It was nerve wracking, but, but he, he, he, he liked it.
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  But I'm going back and forth.
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  It was like a tennis game where my neck was,
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  was in trouble at one point.
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  Merton is asking a pretty hard question
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  and Fama is answering it for me.
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  'cause he's read my paper 20 times at that point.
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  And I started to interrupt to answer it my way.
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  And I had a moment of clarity, thank God, where I'm like,
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  I'm not gonna interrupt Eugene Fama
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  as he answers the questions for me.
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  So it was a pretty heady time to be at Chicago.
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  Auntie Elman in, who's one of my partners at A QR
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  was my year in in the program.
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  My two co-founders of a QR John Liu and,
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  and Robert Krall were one year behind me in the program.
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  And, and I don't think I met Bob physically there,
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  I think I met him later on through probably John or Ross.
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  But Jon, John
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  and Ross ended up being my first two hires at Goldman Sachs,
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  along with Brian Hurst, who was our analyst.
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  And, and then Bob
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  and John ended up leaving Goldman with me to start a QR.
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  So it's fair to say Chicago was, was responsible
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  for an incredible number of good things in my life.
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  - That, that's fantastic.
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  And, and I guess like, you know, just
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  to close the thread on, on sort the Chicago years, like
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  when you were presenting, you know,
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  your dissertation on momentum
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  and I, I think this was maybe at the time when yourself and,
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  and your, she and Tim Timon when sort of, you know,
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  momentum was on the left hand side, you know, the equation.
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  And then I think, you know, mark sort of brought on our,
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  to be one of the right factors
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  - That's well put - With, with the, you know,
• 10:00
  four factor model.
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  Like was there an aversion to that?
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  You know, I think of Chicago
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  and Fama as sort of being very risk-based into risk-based
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  factors and you know, momentum never really squares neatly
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  into, you know, this idea that, you know, stocks
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  that have outperformed when we say momentum
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  for the listeners not there, that aren't familiar
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  with momentum means, you know, stocks
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  that have outperformed in the past 12 months are gonna
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  continue to outperform.
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  Those that underperform in the past 12
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  months are gonna continue to underperform.
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  I mean that, that sort of doesn't really fit into the
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  standard sort of rational Sure.
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  Factors of, you know, value
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  and size that are the, you know, former French factors.
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  Was there a version to, to this
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  or in a truly Chicago
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  Iconoclast way or, or we, we,
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  - It's a great, it's a great question
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  because as I'm, I'm finding these things
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  and I should say Jish and Tipman deserve pride of place.
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  I was probably only about six months behind them,
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  which I will regret forever.
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  But I, I think they, you know, we should say
• 10:57
  that I think they're the discoverers.
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  I mean people were trading momentum and,
• 11:01
  and actual Wall Street forever,
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  but the academic kind of formal discoverers,
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  I think my version of it was a better formation.
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  I'll give myself that. But they, they, they deserve that.
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  I was actually nervous about telling Gene who I'd never,
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  I still today have trouble calling him Gene,
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  he tells me too all the time,
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  and it's Professor Fama,
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  I'm much more comfortable with that.
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  But I was nervous going to tell Professor Fama
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  that I wanna write a dissertation wasn't only on momentum,
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  but that I was including this result in a, in a,
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  in a fairly prominent way.
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  And it, I distinctly remember telling him,
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  I wanna study price momentum for choosing individual stocks
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  and cowardly mumbling.
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  The second part that it works very well.
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  'cause you're right instinctively, you know,
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  for any so-called factor,
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  a factor is if you sort stocks or other assets.
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  But we always think about stocks first.
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  If you sort stocks on this characteristic,
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  the ones at one end
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  of the sort tend outperform ones
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  at the other end of the sort.
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  For any factor that you believe works,
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  there are always two possible explanations
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  and efficient market story where the ones
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  that are attractive on that factor are in some sense
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  as a portfolio, not individuals riskier than the other ones.
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  And therefore you get a return premium for bearing risk.
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  The other is a market inefficiency explanation where,
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  you know, it's not really a formal kind of measure
• 12:33
  of risk, it's a bias.
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  People have an error they're making, taking the other side
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  of errors can still be risky in the short term,
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  but it's not a, a so-called price risk.
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  It's not correlated in co varying with some
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  stochastic discount factor that we care about.
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  You can come up with a risk
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  or a behavioral explanation for any of these factors.
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  The plausibility I've always found,
• 13:01
  and I think the industry would agree with me,
• 13:03
  the plausibility of a risk-based explanation for momentum.
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  It's pretty hard to swallow. It's pretty hard to swallow.
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  I think I, I think probably also trying
• 13:11
  to appease a curry favor with Fama.
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  I think I called it the fool strategy when I started just
• 13:17
  buy what's going up and sell, what's going down
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  it people have tried, I've never seen a very satisfactory,
• 13:24
  oh, this as a portfolio represents risk.
• 13:27
  It has a bad left tail,
• 13:28
  but that left tail seems to be, if anything,
• 13:31
  a negative beta, not a positive beta.
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  I've never seen, I've never seen a
• 13:34
  really good story for that.
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  But I'm telling f and I mumbled the second part
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  and he hears me,
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  he prob actually I think he probably made me repeat it once,
• 13:43
  but, so I say I wanna study price momentum
• 13:46
  and I, I find it works very well and he pauses for a second
• 13:51
  and this still had a profound impact on me.
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  It was a very cool moment.
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  He just goes, if it's in the data, write the paper,
• 14:00
  which is about as close
• 14:01
  to a religious statement you can get at a, at a Fama.
• 14:05
  That man's respect for data, no matter
• 14:07
  what his prior might be is, is extreme.
• 14:12
  And it's, it's, it's, it's actually, it's actually very
• 14:16
  a great role model.
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  I think he'll be the first to tell you he never has liked
• 14:21
  that momentum works.
• 14:22
  I think he's called it the, the,
• 14:24
  the principle black mark on their kind of three
• 14:27
  and ultimately five factor models.
• 14:30
  But he doesn't deny the,
• 14:33
  the historical results if it's in the data.
• 14:35
  Fama wants you to write the paper.
• 14:37
  So that was a scary moment as you intuited that it might be.
• 14:42
  But it ended up being quite a warm and,
• 14:45
  and nice moment for me.
• 14:47
  - That's, that's fantastic
• 14:49
  and amazing to hear about, about Fama.
• 14:53
  I'm curious, so you know, you're a PhD student in, you know,
• 14:56
  your later years.
• 14:58
  Were you ever thinking about becoming a professor
• 15:00
  or you just knew that you wanted to sort
• 15:02
  of apply all this to, to
• 15:03
  - No, no.
• 15:04
  I, when I entered the PhD program,
• 15:06
  I assumed I was gonna be a professor.
• 15:09
  I, I got slowly seduced to
• 15:11
  what you might call the dark side.
• 15:13
  You mentioned my friend John Byner.
• 15:15
  I should explain why you mentioned him
• 15:17
  'cause you also worked at Goldman Sachs Asset Management
• 15:20
  where, where John ran fixed income for many years.
• 15:24
  He was much more junior in 1991 when John
• 15:28
  and a few other people I knew working there
• 15:31
  offered me a summer job.
• 15:32
  I was still a PhD student
• 15:34
  and it was come for the summer, see what you think.
• 15:37
  I went there, I had a fun summer
• 15:39
  and they said, come
• 15:40
  for a year you can work on your dissertation part-time.
• 15:43
  See, see if you like it. And I did that.
• 15:47
  And at the end of the year, maybe it was, maybe it stretched
• 15:50
  to more like a year and a half.
• 15:53
  I still hadn't really decided when, again, dumb luck.
• 15:57
  Any story that you think ends well
• 15:59
  and I hope it's not the end of my story,
• 16:00
  but I hope, I hope it has gone well.
• 16:03
  Any story that has gone well has a lot of luck along the way
• 16:06
  and I'm, I always try to be humble about that.
• 16:09
  Pimco, the West Coast, you know, most known
• 16:12
  for fixed income, huge respected money manager.
• 16:16
  Read the first piece I wrote in the Journal
• 16:18
  of Portfolio Management.
• 16:20
  It had the exciting title option, adjusted spreads
• 16:24
  and a steep yield curve.
• 16:26
  The movie version has yet to be made.
• 16:29
  But they, they liked the paper, ended up calling me
• 16:33
  and said, would you be interested in coming out
• 16:35
  and starting a quant group for us?
• 16:37
  And I did talk to them.
• 16:40
  They offered, they actually did offer me a job.
• 16:43
  And I, I was incredibly naively honest.
• 16:47
  I I I walked up to my superiors at Goldman
• 16:50
  and I said, I think I lucked into this,
• 16:52
  but I think this is what I wanna do.
• 16:54
  I can, I can pursue the academic stuff
• 16:56
  that I really have grown to love
• 16:59
  and I can see if it works in the real world.
• 17:01
  Which I found very attractive.
• 17:03
  And I will have to admit, I did notice
• 17:05
  that if it works in the real world, you,
• 17:08
  you probably make a little more money than
• 17:09
  a professor makes.
• 17:10
  And someone who says they chose Goldman Sachs over being an
• 17:13
  academic and doesn't kind of admit that was at least part
• 17:16
  of the equation for them is probably not
• 17:17
  telling the full truth.
• 17:20
  And my, my, the partners I reported to said,
• 17:25
  Hey, we're looking to start a group like this
• 17:27
  and to this day I'm not a hundred percent sure that was true
• 17:31
  or if they were just really opportunistic
• 17:33
  and quick on their feet.
• 17:35
  I think it was probably true.
• 17:37
  I, I think there, I got lucky again,
• 17:41
  this'll really date the story because this did not end well.
• 17:45
  But I think Goldman wanted to start a group doing this,
• 17:47
  having really no idea what this group would
• 17:50
  because of the then success of the hedge fund,
• 17:52
  long-term capital about
• 17:56
  6, 7, 8 years later, 1998.
• 17:59
  That did not end so, so, so pleasantly.
• 18:02
  But they helped, they helped me out a lot in my life.
• 18:05
  'cause Goldman I think was like,
• 18:07
  these guys are making a ton of money.
• 18:09
  They're academics applying their stuff to Wall Street,
• 18:12
  you know, cliff, why don't you try to do that here?
• 18:14
  And we didn't do very similar things to long-term capital.
• 18:17
  But I I I told Fama in French,
• 18:21
  I I think I'm gonna stay and do this.
• 18:22
  This is perfect. Fama was not happy with my choice.
• 18:27
  He wanted me to go on the academic job market
• 18:29
  and it still saddens me.
• 18:31
  'cause I love f and I didn't wanna make him, you know,
• 18:33
  I didn't wanna cause unhappiness.
• 18:36
  I do take it as a bit of a compliment that he was,
• 18:39
  that he was upset if he,
• 18:40
  if he thought I wouldn't be a good academic, he,
• 18:42
  he wouldn't have been upset.
• 18:43
  But I still remember his comment to me.
• 18:46
  I told him, I think I'm gonna stay at Goldman
• 18:47
  Sachs and build this group.
• 18:49
  And he just goes, why do you wanna stay and be a salesman?
• 18:55
  And it was very Fama que comment, you know, if I,
• 18:58
  and I was, I I would never say this
• 19:01
  to Gene if he hears this podcast, I'm saying it now.
• 19:04
  I I was thinking you guys work with DFA, right?
• 19:06
  You, you, you do this
• 19:08
  and I'm just gonna do it from over, over there.
• 19:11
  So there are points on a spectrum.
• 19:12
  But yeah, I saw the opportunity
• 19:14
  to pursue the academic stuff, trade it in the real world
• 19:17
  and, and, and, and do really well if it, if it worked
• 19:21
  - Well, I'm just curious, I mean like
• 19:23
  how Goldman was set up at the time.
• 19:24
  I mean, my sort of sense of things, understanding a bit
• 19:28
  of the history is that, you know,
• 19:29
  Goldman was a big innovator at that time in the sense that,
• 19:32
  you know, there, there were people there like Fisher Black,
• 19:35
  like Bob Litman that were in the fixed
• 19:36
  income research group.
• 19:38
  This was sort of even before Goldman Sachs asset management
• 19:42
  came to be, I think in the early 1990s.
• 19:45
  And and I, that's kind of where yourself and, and,
• 19:47
  and John Byner and, and Charmine Ma Armani
• 19:49
  and all these other folks kind of come in.
• 19:51
  But I mean the fact that you, you know, one of the sort
• 19:54
  of co-founders of the Black Shoals, you know,
• 19:56
  Merton model was, was at Goldman.
• 19:58
  You know, the the,
• 20:00
  we talk a lot about black Letterman optimization, you know,
• 20:04
  still being used in in portfolio management.
• 20:06
  I mean Goldman Sachs, you know, at that time was, you know,
• 20:07
  a hugely innovative environment.
• 20:09
  It totally makes sense that, you know,
• 20:10
  young CLS would wanna work there
• 20:12
  and to, to this day you're still writing academic papers.
• 20:15
  So, so it it never stopped you from,
• 20:17
  from doing research as well?
• 20:18
  - No, no. And that was a major attraction of it.
• 20:21
  The ability to fuse both worlds.
• 20:23
  Who was who and who was where is a bit complicated.
• 20:26
  Bob Litman was on the sell side,
• 20:28
  not on the asset manager side at the time.
• 20:31
  He actually came over when I left
• 20:34
  to run my group actually in a broader role than I had
• 20:37
  to run my group among other things.
• 20:39
  But he moved to asset management.
• 20:41
  I think it's a direct response to, to myself
• 20:43
  and a few other of our member,
• 20:45
  our group leaving Fisher Black being there was some of the,
• 20:49
  again, dumb luck in my life.
• 20:51
  I got to spend a lot of time with Fisher.
• 20:53
  He was really a sounding board.
• 20:55
  He wasn't building models with us.
• 20:58
  I remember one time I was showing him one
• 21:00
  of our early models
• 21:02
  and he kept calling it a DM model
• 21:06
  and I, I kept thinking he meant Deutschemark
• 21:08
  because they used to be a Deutschemark
• 21:11
  and he actually meant is it to Data Mind?
• 21:15
  And I didn't, you know, he was using it as a shorthand.
• 21:18
  But Fisher was, was was great.
• 21:20
  One of the Fisher stories I love is he wrote a paper
• 21:24
  that was quite critical of some of Fama
• 21:26
  and French's early work.
• 21:28
  And his first line of the paper was Fama
• 21:32
  and French misinterpret their own data.
• 21:35
  I could be slightly off on that, but I'm getting very close.
• 21:39
  He only thanked a tiny group of people, including me.
• 21:43
  And from the bad luck of alphabetical order,
• 21:47
  I'm the first one thanked.
• 21:49
  I am not done with my dissertation yet for Fama and French.
• 21:53
  So I, I should have given them more credit.
• 21:56
  But I call up Ken as soon as I saw it
• 21:59
  and I say, you know, I gave Fisher some comments,
• 22:01
  but I don't agree that he that with, with that,
• 22:04
  that you're misinterpreting your own data.
• 22:07
  And Ken was like, we know Fisher, don't worry, don't worry.
• 22:11
  'cause Fisher was absolutely brilliant,
• 22:15
  suffered from less groupthink than anyone I've ever met.
• 22:19
  Emperor always had no clothes for him.
• 22:21
  He is always willing to say it,
• 22:23
  but I will say nine outta 10 things.
• 22:25
  He said, were a little, this, this is very
• 22:30
  disrespectful, but were a little wacky sometimes.
• 22:33
  The 10th one was brilliant and nobody ever thought of it
• 22:37
  and it was well worth the price of admission.
• 22:40
  But that, that, that did scare me.
• 22:43
  But now Litman was on the sell side, came over after I left.
• 22:46
  But we did use a version of Black Litman in building our,
• 22:49
  our, our process.
• 22:51
  So yeah, there was a guy, Bob Jones,
• 22:53
  who doesn't get enough credit in the quant world.
• 22:56
  Bob was running a quant equity product, again,
• 23:00
  I don't think we even called it factor investing,
• 23:02
  but doing a lot of the early stuff
• 23:05
  and some innovative new stuff of his own.
• 23:07
  And he was a very early practitioner
• 23:09
  of quantitative investing and he was there.
• 23:11
  So yeah, it certainly was not even close to just me.
• 23:14
  I was, I landed in a fertile environment
• 23:17
  - That, that's fascinating.
• 23:18
  So, so my understanding is like you came in,
• 23:20
  you into the summer,
• 23:22
  you're working on fixed income stuff
• 23:23
  for the most part. And, and then this,
• 23:26
  - The summer was fixed income when I came back for the year.
• 23:30
  It was fixed income while writing my dissertation on
• 23:34
  what we would now call quant equity,
• 23:36
  including momentum at night
• 23:38
  and on the weekends. That was
• 23:41
  - A come by day quant equity by night.
• 23:44
  - Yeah. Later it was probably the hardest, craziest time
• 23:47
  of my life except many years later.
• 23:49
  My wife and I had two sets of twins 18 months apart
• 23:53
  when I was building a QR.
• 23:56
  So that was a fairly similar crazy period just
• 24:00
  with a different nocturnal activity instead of, instead
• 24:05
  of writing, you know, laboring away at a keyboard at night,
• 24:08
  I was feeding and changing children.
• 24:12
  If, if my wife watches this, she's gonna go,
• 24:14
  you didn't change too many children, so let's,
• 24:16
  I might not send this one to her
• 24:18
  - That, that's too funny.
• 24:20
  That's, so I guess it's fair to say
• 24:22
  what was quant equities always sort of your, your passionate
• 24:25
  and that was kind of what, what sort of led them to say,
• 24:27
  Hey, you know, cliff, we need you
• 24:29
  to start a quant equities group or a quant group in general.
• 24:32
  Also maybe do quant macro assets as well.
• 24:35
  And, and then that, that was where the impetus
• 24:37
  for starting these two groups, you know,
• 24:39
  I think one was called qd, one was called QAs
• 24:42
  and now I think it's still called quantitative investment
• 24:44
  strategies years later.
• 24:46
  But that was sort of the, the impetus for, for that
• 24:48
  - You're giving us, you're giving us
• 24:50
  all way too much credit.
• 24:51
  It was not that well thought out.
• 24:53
  It was thought out at the level.
• 24:55
  I said before that other people seem to be making money
• 24:58
  with academic stuff.
• 24:59
  Let's see if Cliff can build a group to do that.
• 25:03
  The, in fact the way I remember it for the first,
• 25:07
  I don't know how many weeks we were kind of walking
• 25:10
  around a little nervous that we had nothing to do.
• 25:12
  We started a quant group and it was, and and,
• 25:15
  and to some extent nobody had a real plan for what we,
• 25:18
  I think they thought I had one and I thought they had one.
• 25:22
  And what happened, Bob Jones was doing the quant equity.
• 25:25
  We ended up doing a parallel effort,
• 25:26
  but we weren't gonna touch that early,
• 25:29
  early on he was doing a good job at it.
• 25:32
  There was a group run outta London non quants active,
• 25:36
  you know, concentrated stock pickers running a portfolio
• 25:40
  that had done well but had hit a very rough patch.
• 25:44
  And I think we did the performance attribution on them
• 25:48
  and it turned out that they were actually pretty good,
• 25:51
  at least ex post i, the statistical valid validity of this,
• 25:56
  I have no idea if we were, if, if, if we had a T statistic
• 25:59
  above 0.5,
• 26:01
  but in the performance attribution they had been good stock
• 26:04
  pickers country by country
• 26:06
  and always in the wrong countries.
• 26:08
  So the first thing Goldman asked us
• 26:10
  to do was this macro question, can you decide
• 26:13
  where in the world to invest using these
• 26:15
  quantitative methods?
• 26:17
  I think I said of course we can do that.
• 26:20
  Give us, give us some time.
• 26:21
  And then our four person group got in a tiny room
• 26:25
  and we said, how are we gonna do that?
• 26:29
  I said, of course before I had a clue embarrassingly,
• 26:33
  'cause I think it's kind of obvious what we did,
• 26:35
  it took a little bit, but within a day
• 26:37
  or two we just said countries are just portfolios
• 26:40
  of individual stocks.
• 26:42
  This is childishly simple.
• 26:44
  If we aggregate up the numbers that people like pharma,
• 26:47
  French, my dissertation had been building factors on,
• 26:51
  if we aggregate them up for countries, if Germany is,
• 26:55
  is is double the price to earnings price to sales price
• 26:58
  to Book of France
• 27:01
  and has worse one year price momentum,
• 27:03
  you normally don't get that 'cause they're
• 27:04
  negatively correlated factors.
• 27:06
  But if that will do you expect Germany to do worse?
• 27:10
  And the answer of course was yes,
• 27:13
  this is a bit of a cooking show.
• 27:14
  I got the cake already baked in the back, I'm taking you
• 27:17
  through the steps, but I know how it turns out.
• 27:19
  And we built the tool that that
• 27:22
  that the active group did use.
• 27:25
  We, we ended up building it for stocks, bonds,
• 27:27
  and currencies using different factors.
• 27:29
  But they all were some version of value momentum
• 27:32
  or what we might today call a carry factor.
• 27:35
  What does it pay you if you, if nothing happens.
• 27:38
  But we also realized pretty quickly that we can give advice
• 27:42
  to the, to the active groups at Goldman,
• 27:46
  but we could also directly run money using these.
• 27:50
  So by, by near the end of 1994, Goldman Sachs seeded
• 27:55
  something called the Global Alpha Fund that
• 27:59
  used both macro and our version of what Bob Jones was doing.
• 28:04
  That we ran it separately of a quantitative equity model
• 28:07
  to run a series of long short strategies, long short
• 28:12
  country futures, long short bond futures,
• 28:15
  long short currencies,
• 28:17
  and then long short individual stocks within
• 28:19
  many of these countries.
• 28:21
  All with a goal to be market neutral,
• 28:22
  very aggressively targeting very high vol.
• 28:26
  And that grew into a lot of the things that that we did.
• 28:30
  - That's, it's, it's an amazing history.
• 28:33
  And also just all the, I think all the people
• 28:36
  that I I my sense you helped hire
• 28:38
  and bring in, you know, people who are PhDs, you know, many
• 28:41
  of whom you know, I think folks and,
• 28:43
  and many of whom at University of Chicago, you know,
• 28:46
  at business school, I think Mark Carhart, Ray Romanowski,
• 28:50
  I think, you know, maybe George, you know, there's also
• 28:55
  - Georgio was after me.
• 28:56
  Okay, mark and Ray hired Georgio when they
• 28:59
  took over when I left.
• 29:00
  - Okay. Well then, then there's also, you know,
• 29:02
  Don Mulvihill, he was early, he was a University
• 29:04
  of Chicago guy as well, you know, built their big tax,
• 29:08
  tax loss harvesting strategy, which one
• 29:09
  of their largest strategies to this day.
• 29:12
  And and and many others, you know, who, who came to,
• 29:15
  you know, lead the group, you know, afterward, you know,
• 29:17
  think do Gary RAF and others.
• 29:19
  Yeah, yeah. I'm just so, you know, fast forward,
• 29:21
  I guess five years later, you know, the, the infancy
• 29:25
  of a QR, you started it in 1998.
• 29:29
  What John knew Robert Krall and, and,
• 29:30
  and Dave Kabil, some of these were classmates of yours and,
• 29:34
  and were working at, at Goldman Sachs as well.
• 29:37
  And what was the impetus for starting EQR
• 29:40
  and going out on, on your own and,
• 29:42
  and starting something totally de novo,
• 29:45
  - 80% naked greed?
• 29:49
  I think, I think we did the math
• 29:50
  and said as well as you can do at Goldman Sachs,
• 29:52
  if you build your own asset manager,
• 29:54
  you do somewhat, you do somewhat better.
• 29:58
  Again, it's very similar to me telling you earlier that that
• 30:02
  that going to Goldman as opposed to academia had something
• 30:07
  to do with, with how well you do.
• 30:09
  It's just, just being honest.
• 30:10
  I think 20% was an early recognition on, on my part
• 30:15
  that I I don't always work and play well with others.
• 30:19
  That you can't say whatever you want at Goldman Sachs.
• 30:23
  You can't say whatever you want at a qr.
• 30:25
  Only I can a a a company has to have some, you know,
• 30:30
  if, if, if, if someone at a QR suddenly has an opinion
• 30:33
  that a product of ours isn't great,
• 30:36
  absolutely should tell us that we should debate it.
• 30:39
  But if they went out and submitted a paper on it without
• 30:42
  telling us, that would be upsetting.
• 30:45
  And I knew I wanted to be the guy who could could say
• 30:47
  and write what, what he wanted.
• 30:50
  Again, I'm not claiming some altruistic, this is,
• 30:53
  I'm giving you 20%,
• 30:55
  the 80% greed was still, was still there.
• 30:58
  But I think I I pretty early on,
• 31:01
  even though I loved Goldman, Goldman was very kind to me.
• 31:03
  I did well at Goldman.
• 31:05
  Realized that I, that I, that I was a guy who,
• 31:07
  who ultimately had to be on his own.
• 31:10
  And then David Caber still co-founding partner,
• 31:14
  still my my co-founder still active today.
• 31:18
  He deserves a lot of credit.
• 31:21
  At some points when we've had tough times, I I, I wanted
• 31:23
  to blame him, but on that credit,
• 31:27
  'cause he spent the better part of a year convincing me
• 31:31
  that we, that that if we left
• 31:33
  and it became a we, it it could work,
• 31:36
  people would actually invest with us
• 31:38
  because I I was not the the
• 31:41
  as secure about the stuff as I am now.
• 31:44
  This notion that a bunch of people turning 30 could leave
• 31:48
  and attract capital.
• 31:51
  I had to be convinced of and David did convince me
• 31:55
  - Well that that's that's fantastic and,
• 31:58
  and, and so great to hear.
• 31:59
  It's amazing. I mean a QR is amazing in
• 32:03
  how transparent it is in terms
• 32:06
  of its investment process in terms
• 32:08
  of the factors that you're using.
• 32:10
  And it's, it's amazing how many sort of public goods
• 32:13
  that you're sort of generating in, in doing research in, in,
• 32:16
  I think in many ways succeeding, you know,
• 32:19
  Goldman Sachs asset management just in terms of the,
• 32:21
  the amount of research and output that you're doing on,
• 32:25
  you know, on factors and various things.
• 32:26
  And, and a QR has been a real leader in this respect, in,
• 32:30
  in in factor investing and,
• 32:32
  and you know, some of the biggest factors I think
• 32:34
  that you're, you you've been focused on
• 32:36
  and as, as other investors have value, momentum, quality.
• 32:41
  I'm curious, you know, now in sort
• 32:44
  of the factor-based investing world, you know,
• 32:47
  there's been a lot of discussion about, you know,
• 32:51
  what factors have, you know, the greatest sort of relevance
• 32:55
  and, and sort of staying power, you know, A A QR is also,
• 32:59
  you know, taking the lead on on some new factors like invol,
• 33:02
  low volatility, bending against beta as a factor.
• 33:05
  I'm curious, where do you stand on your favorite factors?
• 33:12
  - You know, it's, it's funny, we,
• 33:14
  this does not affect the weights we put on things.
• 33:16
  We've actually moved to a more systematic
• 33:18
  approach to weights.
• 33:20
  It looks at ins, sample out a sample data.
• 33:24
  So favorites, I would be terrified
• 33:27
  that someone would think I'm sitting around, you know, and,
• 33:29
  and just overriding all the data
• 33:31
  and saying, I kind of like this one.
• 33:33
  But I've always thought two of the originals,
• 33:36
  which still matter, they don't matter as much as they used
• 33:39
  to 'cause the models have grown.
• 33:40
  But the value and momentum stuff, I've often joked that
• 33:45
  it's a almost a, a rach test, almost a personality test.
• 33:50
  Someone's either wired to be a contrarian
• 33:53
  or to be a jump on the bandwagon person.
• 33:56
  And I, i, I can't test this,
• 33:58
  but I've always thought one of the harder parts
• 34:00
  of active stock picking is
• 34:02
  that these two things do really work
• 34:05
  and it's hard to keep both in your head at once
• 34:07
  'cause you're wired for one or the other.
• 34:09
  You know, all else equal.
• 34:10
  I'm wired to be a contrarian in
• 34:12
  almost every part of my life.
• 34:13
  Sometimes excessively so, so
• 34:17
  favorite in maybe an emotional sense I'm at,
• 34:21
  at core a value guy.
• 34:24
  I don't think, certainly not today.
• 34:26
  I think in the past I've probably let that, let,
• 34:29
  that let me overweight it maybe even a little too much.
• 34:32
  We've certainly moved away from that over time.
• 34:35
  But if I had to have a favorite, I also love the fact
• 34:38
  that value, you know, can work
• 34:41
  for a host of different reasons.
• 34:43
  I think it was Ray Ball, I hope I have his first name right,
• 34:47
  who wrote a paper in the early days of factor investing
• 34:50
  who pointed out that value's kind of a catchall
• 34:52
  for expected returns.
• 34:54
  It can be behavioral, it could be risk premium,
• 34:56
  but for the same cash flows, if expected returns are higher,
• 35:00
  the multiples all equal lower.
• 35:03
  So it it, you know, the theoretical justification, no matter
• 35:06
  what the empiric now value
• 35:09
  of all the factors we trade value is probably the
• 35:12
  most episodic.
• 35:13
  Meaning it's not IID it goes through some long periods
• 35:17
  of being in favor and long periods of being out of favor.
• 35:21
  I think that's probably part
• 35:22
  of why it doesn't get arbitraged away.
• 35:25
  You know, after a lot of years marketing these things,
• 35:27
  I'm pretty good at turning a negative into a positive.
• 35:30
  But you know, the harder something is to do,
• 35:33
  the more plausible it is that not enough people do it
• 35:37
  to make it go away.
• 35:38
  So there's always good and bad news when you say
• 35:40
  something is hard to stick with.
• 35:42
  The bad news is it's hard to stick with the good news is
• 35:45
  that maybe why it doesn't go to zero.
• 35:50
  But, you know, I love, but I love momentum.
• 35:54
  I've never been as much a momentum guy even though it's
• 35:56
  somewhat one, one of the things I'm known
• 35:57
  for in academia is being, you know,
• 36:00
  after Jian Tipman very early in creating that,
• 36:03
  that is again, more emotional.
• 36:05
  That's a matter of personal preference.
• 36:07
  I used to do, I I finally stopped about a decade ago,
• 36:10
  but for the first like 20 years of doing this,
• 36:13
  I'd make the same annoying joke when people would bring me a
• 36:16
  trade that we were doing and I'd look
• 36:18
  and I'd go, are you telling me we're buying more
• 36:21
  of the Japanese yen
• 36:22
  because it is more expensive than it was last month?
• 36:27
  'cause that's what momentum does.
• 36:29
  And often that would be more important than a
• 36:31
  valuation in the short run.
• 36:33
  And someone would look at me,
• 36:34
  they got the joke at some point, very deadpan
• 36:36
  and go, yes, that is what I'm telling you.
• 36:39
  And I'd be like, okay, I was just checking.
• 36:41
  'cause you know, in the, in some fiber of my being, it just
• 36:45
  that, that would always just bug me.
• 36:46
  But being a value momentum,
• 36:49
  being a value investor without momentum would scare me
• 36:52
  because value does have, its, its its long dark periods
• 36:58
  as they said about Winston Churchill in the
• 37:00
  thirties in the wilderness.
• 37:03
  Betting against beta is is one of my favorite factors
• 37:06
  because the empirics are so strong and
• 37:09
  because the story is so beautiful, it's simply,
• 37:12
  it's a basic cap m story with restricted leverage
• 37:15
  where leverage is e people are either unwilling
• 37:17
  or it's too expensive to do
• 37:19
  and it leads directly to low beta stocks,
• 37:22
  which really help you in a world
• 37:24
  where you leverage the Ency portfolio.
• 37:27
  But if you're unwilling to do that, they're kind of orphans
• 37:30
  and it's a very, very sweet story
• 37:32
  that shows up in the data very nicely
• 37:36
  profitability.
• 37:37
  One of the quality factors I love
• 37:40
  because it's empirically so strong.
• 37:42
  I think it's one of the weaker theoretical stories.
• 37:47
  I don't have a great, frankly we'll do things
• 37:49
  for different combinations.
• 37:50
  We like to have both, but a super strong empirical story
• 37:54
  and, and and, and a less strong theoretical
• 37:57
  or common sense even story
• 37:59
  or vice versa can still get you in the, in in the process.
• 38:02
  You'll get more of a weight if you got both.
• 38:05
  I, I'm actually dating myself, we've changed
• 38:07
  to a more systematic approach.
• 38:09
  So that's, that's a little less true than it used to be,
• 38:11
  but I'm just gonna, I'm just gonna go with it.
• 38:13
  But you're asking one of my favorites.
• 38:15
  I love the empirical results in
• 38:17
  and out of sample of profitability.
• 38:20
  I've never liked the stories why you should get paid
• 38:23
  for buying more profitable companies.
• 38:26
  I've never seen anyone and I, I don't keep up
• 38:28
  with the academic literature quite as much as I used to,
• 38:31
  but I've still not seen anyone come up
• 38:32
  with a great theoretical story
• 38:34
  for why you should get paid for that.
• 38:37
  Though it's so strong empirically in
• 38:39
  so many different places it's passed the out sample test.
• 38:43
  So yeah, I have different loves and, and and, and,
• 38:47
  and ones I'm a little more queasy about.
• 38:49
  But again, we don't let my loves and queasiness matter.
• 38:54
  I don't think we ever let 'em dominate
• 38:56
  and we don't let 'em matter as much as they used to.
• 38:59
  - Well, I I'm just curious a little bit about market time
• 39:02
  or, or sorry, factor timing, let's just say,
• 39:05
  - Okay - And you've, I think always,
• 39:06
  and correct me if I'm, I'm wrong, I've always sort
• 39:08
  of seen you as a critic of of factor timing a bit.
• 39:12
  And I'm just curious
• 39:13
  how investors should maybe think about periods
• 39:15
  where like traditional factors like value underperformed
• 39:19
  for over a decade or longer.
• 39:20
  Like obviously value in the US you know, has become,
• 39:25
  you know, bending against tech specific story, which,
• 39:28
  you know, if you've been doing that for the past 15 years,
• 39:30
  it's been a very difficult thing.
• 39:32
  Obviously, you know, you can do
• 39:35
  sector neutral sorts where you're doing, you know,
• 39:39
  you still have the same weight that you know the tech
• 39:43
  sector will have in the, in say the s and p 500,
• 39:46
  but then you'll, you know, within each sector, you know,
• 39:49
  you will pick stocks that have the most value.
• 39:51
  So you'd still have the same overall tech exposure
• 39:53
  for example, but you just would pick
• 39:54
  tech stocks as the most value.
• 39:56
  What do you think about that? I mean there's lots
• 39:57
  of STAAR people out there that are trying
• 39:59
  to time the market sort of in, in have various sort
• 40:02
  of market timing strategies.
• 40:03
  You know, when you have a big crash in the s and p
• 40:06
  and your expected returns go up.
• 40:08
  I'm just curious what you think about factor timing,
• 40:09
  market timing in general.
• 40:11
  - You asked a ton of stuff in that question
• 40:14
  and I'm not gonna remember all of it.
• 40:17
  But first on the industry adjustment thing,
• 40:19
  the sector adjustment thing,
• 40:21
  I think we were the first to do that.
• 40:23
  Myself and Ross Stevens wrote a paper in 1995,
• 40:26
  never published, but you can still find on the SSRM
• 40:29
  and I think we get pride a place on showing
• 40:31
  that most factors,
• 40:33
  and I think value is particularly strong,
• 40:35
  do better if you do it within industries
• 40:37
  and don't take an industry bet that the apples to oranges,
• 40:41
  you know, to go to the extreme comparing tech
• 40:43
  to a textile on price to earnings.
• 40:46
  Are you ever gonna not be short tech
• 40:49
  and cannot be right to, to have a permanent bet?
• 40:53
  Momentum was the only one that had very similar power
• 40:58
  for, for both.
• 40:59
  And I think that's very intuitive
• 41:01
  'cause there's no measurement problem.
• 41:03
  Momentum is momentum.
• 41:04
  It's, it's, it's return value has a big measurement problem.
• 41:07
  Does this, does this ratio mean the same thing
• 41:10
  for this industry as this other industry?
• 41:13
  So I do, I do have to brag for a second value.
• 41:17
  The, the simple farm
• 41:18
  and French definition price
• 41:19
  to book in the us I don't remember.
• 41:22
  It's on a certainly more than a decade,
• 41:25
  probably like a 15 year drawdown versions of value
• 41:29
  that are global, which fa and French do also.
• 41:33
  It's not a knock on them and they use more measures than
• 41:36
  just price to book and don't take that industry bet.
• 41:40
  The longest we've seen, which have been quite painful,
• 41:42
  have been kind of two and a half year drawdowns.
• 41:45
  Actually you're in drawdown for longer than that
• 41:47
  so you have to make it back.
• 41:48
  But two and a half year negative periods made back at about
• 41:51
  that same amount of time
• 41:54
  where multifactor processes are often not nearly as bad.
• 41:57
  Remember we,
• 41:59
  except if a client specifically wants one factor
• 42:02
  for some purpose they have,
• 42:04
  we're always trading a whole bunch of factors at once.
• 42:07
  So, you know, the value drawdown, the this,
• 42:11
  the simple version has been out of favor for a while though.
• 42:15
  I wrote a piece defending it
• 42:17
  just a couple years ago called The Long Run is lying to you.
• 42:21
  I mean to most people, 10 to 15 years is an eternity.
• 42:24
  If you ran a practical investment product doing only this
• 42:29
  15 years later, only you and your mom are still invested
• 42:34
  and your mom has asked for the redemption papers, she wants
• 42:37
  to know kind of what the rules are for when she can get out.
• 42:41
  But in real life at modest sharp ratios, remember, you know,
• 42:45
  if we're trying to achieve a sharp ratio, call it one,
• 42:49
  and we have many, many factors in many, many countries,
• 42:52
  each individual one can't be close to one or
• 42:54
  otherwise we'd be a sharp ratio of five.
• 42:57
  So they're not giant in any single country.
• 42:59
  One factor is not a giant edge.
• 43:01
  So when you do the math, a standard deviation event,
• 43:04
  you know, if you're, if you're a 0.5, which is gigantic
• 43:07
  for a single factor, right?
• 43:08
  If you were 0.5 across tons of things, if you were 0.5,
• 43:11
  it takes less than a two standard deviation event
• 43:13
  to be down for a decade.
• 43:15
  So these things
• 43:17
  to a statistician are often much less shocking than they are
• 43:21
  to a, a real world person who has to go in in year nine
• 43:25
  and a half and say still down.
• 43:27
  The other thing is one thing I, I do think we pioneer,
• 43:31
  I'm bragging a lot on this call,
• 43:32
  you're bringing it outer me, but
• 43:34
  during the tech bubble in 99
• 43:37
  we released the first to publicly do this.
• 43:39
  You never want to claim you're really the first
• 43:40
  'cause we do things privately.
• 43:42
  We don't write about, we don't publish everything John,
• 43:45
  we only publish the things we think are mostly out there.
• 43:49
  So I gotta give other people the benefit of the doubt also.
• 43:52
  But we wrote a piece trying to measure the, the long term,
• 43:56
  not short term attractiveness of the value strategy
• 43:58
  and introduced this idea called the value spread
• 44:01
  that many others use now.
• 44:02
  And if you, if you go back
• 44:04
  and look at the academic papers,
• 44:06
  all the early work were sorts, ordinal sorts
• 44:09
  that built a portfolio of the better and went went short
• 44:12
  or underweight portfolio of, of of the worse.
• 44:15
  We asked the question particularly
• 44:17
  'cause value is getting creamed for a year and a half.
• 44:22
  Okay, how extreme is it?
• 44:24
  And to our knowledge, at least publicly again,
• 44:26
  we were the first to say, alright,
• 44:28
  pick your favorite valuation ratio.
• 44:30
  We, we looked at multiple ones.
• 44:32
  If you, if you like price to sales, what's the price
• 44:35
  to sales of the cheap portfolio
• 44:37
  compared to the expensive one?
• 44:39
  Is it more different than it normally is
• 44:42
  or tighter than it normally is?
• 44:45
  And the idea is when there's bigger differences,
• 44:47
  when the expensive ones are at a much higher multiple than
• 44:50
  the, than the cheap ones than normal,
• 44:53
  they're always look more expensive, right?
• 44:55
  You created the measure, you sorted the stocks.
• 44:58
  If, if you sort on something the the ones
• 45:00
  that are higher will be higher
• 45:02
  but how much higher varies through time.
• 45:06
  And we did find that if you have a long horizon,
• 45:08
  you know three years is good, five years is even better,
• 45:12
  that there is some power to it.
• 45:13
  When that spread is larger things go on the,
• 45:17
  the value strategy will do better.
• 45:19
  When, when spreads are tight, the sharp ratio is lower.
• 45:22
  We also found, and here's an example of publishing,
• 45:26
  I'm still a little bitter about this one.
• 45:28
  An official a QR publication strategy
• 45:32
  is the perfect a QR publication strategy is you discover
• 45:36
  something you think no one else knows that has some alpha,
• 45:39
  you trade it for a decade
• 45:41
  and you write a paper on it a minute and a half
• 45:43
  before someone else is gonna write a paper on it
• 45:46
  'cause it's gonna be out there anyway.
• 45:47
  Might as well get the credit.
• 45:49
  Obviously that's art, not science.
• 45:51
  And we've been trading the fact
• 45:53
  that factors themselves have momentum.
• 45:54
  They tend to trend for many years.
• 45:57
  And someone else wrote the paper
• 45:58
  and we were very, I was very angry
• 46:00
  that we, that we missed that one.
• 46:02
  We, we let someone sneak in before us. But that matters too.
• 46:06
  In the short run, fighting momentum is always a problem
• 46:08
  that this result repeats again and again and again.
• 46:12
  So never bet your life on factor timing.
• 46:16
  It's still particularly on an individual factor,
• 46:19
  pretty low risk adjusted return.
• 46:21
  But the perfect time to like any factor is when it looks
• 46:24
  quite cheap versus history.
• 46:25
  But when the last six to 12 months are starting
• 46:27
  to look pretty reasonable,
• 46:29
  you never like any value plus momentum strategy,
• 46:33
  you never nail the peak of the trough
• 46:35
  because you need to see it start to work.
• 46:38
  But because trend following is real,
• 46:41
  that's a higher sharp strategy.
• 46:42
  And when I've messed up in this field,
• 46:45
  it's when I found the valuation so attractive
• 46:48
  that I've said, ah, we gotta do a little even
• 46:50
  before the trend turns, turns out the trend is, has tended
• 46:54
  to still have efficacy.
• 46:57
  So I do think you shouldn't do much factor timing.
• 47:01
  I think most of your factor timing should actually be trend
• 47:03
  based, not valuation based.
• 47:06
  And valuation only gets interesting when you're at something
• 47:08
  I jokingly call the hundred 20th percentile.
• 47:13
  You're a guy who will understand sometimes I make that joke
• 47:15
  and people don't understand there is no such thing
• 47:17
  as the hundred 20th percentile,
• 47:20
  I mean like wider than you've ever seen before,
• 47:23
  but mostly don't do it.
• 47:25
  And to be totally fair,
• 47:28
  the times I've been most cynical about factor timing is when
• 47:32
  one of my frenemies, Rob Arnot, is very, very into it.
• 47:36
  Somehow when he writes something I,
• 47:38
  I instinctively take the other side too much
• 47:40
  'cause Rob's a brilliant guy.
• 47:41
  So I have been a little schizophrenic on,
• 47:44
  on on factor timing.
• 47:47
  Historically we've,
• 47:48
  we've not been flighty in the portfolios.
• 47:50
  We've always done a little with some valuation and,
• 47:54
  and particularly trend following factor trends are
• 47:57
  just, we think they're real.
• 47:59
  - Yeah. Well I'm curious just to maybe lightning round
• 48:02
  through a few of these topics that,
• 48:04
  that you've spoken a bit about fire
• 48:05
  - Away.
• 48:07
  - You know, one is, you know,
• 48:10
  in academia at least there's a bit of a a replication crisis
• 48:14
  and, and in fact research has I think suffered a bit in,
• 48:17
  in academic finding
• 48:19
  and it's not being published as much anymore.
• 48:21
  And I think a big question is, you know,
• 48:22
  what are the real factors
• 48:24
  and you know, to what, what's really PA what's sustainable
• 48:27
  and maybe one a factor discovered it's arbitraged away
• 48:30
  and people pile into it.
• 48:31
  I'm just curious, you know, what you think about factor
• 48:34
  research and, and how do we sort of
• 48:37
  dis over time build an understanding of
• 48:41
  what are the essential factors in the structure of prices?
• 48:44
  - Sure. A again, you ask questions that I
• 48:47
  I could not do a lightning round.
• 48:48
  I can go on for a long time.
• 48:51
  You know, the literature well it's actually
• 48:53
  how big the replication crisis is in factor investing is a
• 48:56
  contentious topic.
• 48:58
  Las St. Peterson, Brian Kelly
• 49:00
  and i, I, they work with me
• 49:02
  so I i I think they had co-authored
• 49:04
  so I don't wanna imply it was just them.
• 49:08
  They take the, the, the view in their paper
• 49:12
  that the replication crisis is not that bad for one thing.
• 49:16
  I think some people hold it a hold replication to too high
• 49:19
  of a standard literally for close to 30 years.
• 49:23
  As a rough rule of thumb,
• 49:25
  we've said if we get half a back test going
• 49:27
  forward, we'll be thrilled.
• 49:30
  'cause we know there's some p hacking and everything.
• 49:33
  I mean obviously there's some things you might wanna
• 49:35
  discount more, some you might wanna discount less.
• 49:38
  Not saying it's a be all end all half is a pretty cheap
• 49:41
  and easy kind of wimpy guess, you know,
• 49:45
  how many times in life do you just split the difference?
• 49:47
  But for, for at least we've been consistent in our wimpy
• 49:50
  guess that changes what you think of as success
• 49:53
  or failure by a lot against that standard.
• 49:59
  We find the results are fairly reasonable.
• 50:02
  I think value plus momentum has been about half
• 50:04
  of a back test since we've started looking at it.
• 50:07
  So we don't think it's quite that bad.
• 50:10
  But obviously data mining p hacking overfitting
• 50:13
  is a perennial issue when it comes to
• 50:18
  which factors are easier to arbitrage away.
• 50:21
  I think the hardest ones are ones, again,
• 50:25
  like the value factor
• 50:27
  that's based I think on basic risk or behavioral reasons
• 50:31
  and I've drifted more to the behavioral side over my career.
• 50:35
  I I think it's just proximity to Fama
• 50:37
  being away from Fama, I drift.
• 50:39
  And if I, if I walked into his office,
• 50:41
  I'd probably snap back into, into the risk side being a,
• 50:44
  a total coward to loves the man.
• 50:46
  But factors say,
• 50:51
  say value is based on behavioral errors people make.
• 50:54
  That means they're pretty deep behavioral errors over
• 50:57
  extrapolating good and bad times overpaying for them.
• 51:02
  It also value as it's seen as it's shown us goes
• 51:04
  through long periods of pain.
• 51:06
  Even if it pays off long term like we discussed earlier,
• 51:10
  that can make something very hard to arbitrage away.
• 51:13
  That could be bad news to someone living through it.
• 51:15
  But good news to whether it's gonna be around for as close
• 51:19
  to forever as you can say in this business factors most
• 51:22
  susceptible to being the way are legal
• 51:26
  information advantages.
• 51:29
  When I say information advantages, I'm quite careful
• 51:31
  to please a QR compliance and throw in the word legal.
• 51:34
  But if you've built a data set
• 51:36
  and you know, nowadays people call this alternative data
• 51:40
  and we are actually fairly into it.
• 51:41
  We, we, we certainly do this if someone builds a new data
• 51:45
  set and is only gonna sell it to one or two managers.
• 51:48
  And that itself is a very interesting negotiation.
• 51:51
  'cause you have to decide if you think it's real.
• 51:52
  They charge a lot more than traditional data
• 51:54
  'cause they're only giving it to one or two managers.
• 51:57
  But you don't expect to trade this for 20 years.
• 52:00
  It's an an informational advantage.
• 52:02
  It's, you know, you're, you're looking down to a satellite,
• 52:05
  you're doing a credit card summary of of,
• 52:08
  of what's going on in the real world
• 52:09
  and you have an edge for a while.
• 52:12
  But those kind, any factor that's about speed of in info,
• 52:16
  getting information and acting faster than other people,
• 52:19
  you know, and unless you're the fastest in the world,
• 52:22
  which some people do try to be,
• 52:24
  that is probably not even close to a permanent edge
• 52:26
  and everything is on a spectrum.
• 52:29
  - Absolutely. Okay, next question.
• 52:32
  How has machine learning changed, if at all, if at all how,
• 52:36
  you know, your systematic approach
• 52:38
  to investing at a QR works?
• 52:40
  - Well, it's, it's creeping in is understating it,
• 52:44
  but it is, it is starting to show up everywhere.
• 52:47
  And I, I'm nervous saying this
• 52:48
  'cause I've said this publicly
• 52:50
  and the financial press has picked it up and overdone it.
• 52:53
  I've said that I probably slowed us down
• 52:55
  by a couple years in machine learning
• 52:57
  because I was a little cynical.
• 53:00
  We've always told this story like I was telling you
• 53:02
  that we care about both data and story.
• 53:05
  And when you move into machine learning, you,
• 53:07
  you gotta lean more heavily on data.
• 53:09
  It's kind of the point, right?
• 53:11
  You're, you're not telling the story,
• 53:13
  you're surrendering yourself, if anything, a little more
• 53:15
  to the data to, so I, I've had a couple of headlines like
• 53:19
  as this does flip-flop on machine learning,
• 53:22
  that kind of stuff drives me crazy.
• 53:23
  Of course, like anyone in business is driven crazy sometimes
• 53:26
  by the, by the press.
• 53:28
  We invested a lot in machine learning
• 53:32
  when it was all on the arm,
• 53:33
  when we didn't know if it would work.
• 53:35
  We hired Brian Kelly outta Yale.
• 53:37
  One of the major machine learning, and I, I'm biased,
• 53:41
  but I think the top machine learning guy in finance.
• 53:43
  We, we explored
• 53:45
  and built machine learning in, in our existing funds.
• 53:48
  People like Andrea Fini, Laura Serbin here.
• 53:52
  So we were not so cynical that we didn't pursue it.
• 53:55
  I just had smaller hopes than maybe some others did
• 53:59
  because I was so used to this world of, of,
• 54:02
  of being more worried about overfitting than anything else.
• 54:05
  And one thing machine learning does is it deals
• 54:08
  with very complex problems with more parameters.
• 54:11
  This is quite controversial right now in academia.
• 54:13
  And I will not try to do Brian Kelly's job
• 54:15
  because I'll do it horrendously badly.
• 54:18
  But his paper, the virtue of complexity, arguing
• 54:21
  that ML changes the equation on on, on
• 54:23
  how many parameters you can have
• 54:24
  and the dangers of overfitting versus under fitting.
• 54:28
  I think Brian's right,
• 54:29
  but if I try to, I'll, I'll lose the debate if I try
• 54:32
  to do it, but it is changing how we do things.
• 54:36
  There are factors
• 54:37
  where we usually can still get some intuition out of,
• 54:40
  out of what's going on.
• 54:42
  I think there are pure ML players
• 54:44
  that just throw away all intuition
• 54:46
  and I don't think we've quite gone there,
• 54:48
  but has ML moved us more towards the data?
• 54:52
  I got a question once. I never thought
• 54:54
  of it this way from a client about an ML factor
• 54:56
  and saying, can you gimme a, a precise,
• 54:59
  intuitive explanation of what it's doing.
• 55:01
  I can go, I can give you a very imprecise one,
• 55:04
  but if I could tell, be very precise about it.
• 55:07
  What is it doing? Where's the machine learning?
• 55:10
  What is it doing? If I can just give you a, a,
• 55:14
  a off the cuff, easy one line answer to it.
• 55:18
  You know, one of the primary areas where,
• 55:20
  where ML has helped so far is processing natural language.
• 55:24
  Being able to tell if something is good or bad.
• 55:26
  News and quants have done this for a million years.
• 55:30
  The old fashioned way to do it is to build up keywords
• 55:33
  and phrases and assign numerical scores.
• 55:36
  Way oversimplifying.
• 55:38
  Do you see the word increasing plus one
• 55:42
  count up all, you know, similar words.
• 55:44
  And of course you, you immediately see the problem if the
• 55:46
  actual sentence was massive, embezzlement is increasing,
• 55:50
  plus one was probably not your best guess.
• 55:54
  Now quants can survive looking silly 47%
• 55:58
  of the time if we get it right 53% of the time.
• 56:01
  So I'm not knocking this old approach,
• 56:03
  I'm just saying it's very noisy.
• 56:04
  It turns out that applying a natural language processing to
• 56:08
  to language data language is a very nonlinear thing.
• 56:12
  Whether a certain word is good
• 56:14
  or bad might depend on this sentence.
• 56:16
  I got the simple one I just gave,
• 56:18
  but it might depend on three sentences ago.
• 56:20
  Nothing is perfect. Language is very complicated,
• 56:23
  but it turns out NLP applied to to, to textural data,
• 56:28
  to forecast returns.
• 56:29
  We found to be very powerful
• 56:31
  and we've had some great success with it.
• 56:34
  I can tell you in general what it's doing.
• 56:36
  It's doing a fundamental form of momentum
• 56:38
  or good things happening.
• 56:40
  But can I tell you precisely why it likes this stock?
• 56:44
  I cannot. And when you do ml, you often have to give that up
• 56:48
  or again, what the heck is the ML doing?
• 56:52
  - So next question's. Private markets versus
• 56:54
  public markets. You're,
• 56:55
  - Oh, we're shift, we're shifting gears.
• 56:57
  - I know you're, you're a big public markets guy
• 57:00
  and also a bit outspoken on, on making comparisons
• 57:04
  between public markets and private markets.
• 57:05
  Obviously private markets just in terms of assets have
• 57:09
  totally exploded in in in recent years.
• 57:13
  You know, do I understand this right?
• 57:15
  That, you know, when you talk about so-called volatility
• 57:18
  laundering and, and making the,
• 57:21
  but you how you can't really compare public market returns
• 57:24
  to private market returns.
• 57:26
  Is that the issue?
• 57:27
  Is that with Marx, how private markets do marks?
• 57:31
  Is that, that you know, how they value themselves.
• 57:34
  It's not just a frequency issue
• 57:36
  and that, you know, public markets, you have every tick
• 57:39
  that the Australian public markets,
• 57:40
  but it's, it's a, a quality of marks that, you know,
• 57:44
  these firms say private equity firms are,
• 57:46
  are valuing themselves
• 57:47
  and there's a bit of a bias in terms of
• 57:49
  how they value themselves and,
• 57:51
  and that kind of distorts Sure.
• 57:54
  Volatility and return metrics a bit for, for private funds,
• 57:57
  say private equity funds, comparing them
• 57:58
  to say public funds, publicly traded funds.
• 58:02
  - I'll give you a short version
• 58:03
  of my complete private equity story.
• 58:08
  First the term volatility laundering, which was mine.
• 58:11
  I, I feel kind of bad about 'cause it's pretty snotty
• 58:15
  and you know, I'm not really implying anything
• 58:17
  illegal is going on here.
• 58:18
  I wanna be clear about that.
• 58:20
  But laundering is when something doesn't
• 58:23
  show up that's there.
• 58:25
  And the way my sense of how most privates are marked
• 58:30
  is, let's go to two extremes.
• 58:32
  You can mark any asset at one of two extremes
• 58:34
  or anywhere in between you could market at
• 58:36
  where you could sell it at today.
• 58:39
  And even that has some issues.
• 58:40
  What if I tried to sell a ton of it today?
• 58:42
  Maybe you get a lower price
• 58:43
  but you know what you're trying to do.
• 58:45
  And for public markets that's quite easy to do.
• 58:47
  You look up the price and you market there.
• 58:50
  The other end of the spectrum is you can market at
• 58:52
  what you think it's worth
• 58:56
  and that's not illegitimate.
• 58:59
  You know, that's an interesting thing.
• 59:01
  My sense of the way where privates do is much closer to
• 59:04
  where they think it's worth.
• 59:06
  I think if markets move, they factor that in a little.
• 59:09
  I don't think they're, they run to a a, a zero beta
• 59:12
  but they're mostly marking to their model to
• 59:15
  what they think the thing is is worth.
• 59:17
  And I don't object to that.
• 59:19
  If I get to do what I object to
• 59:23
  is different standards.
• 59:25
  And then the worst thing is when these are mixed
• 59:27
  and people will show an efficient frontier
• 59:30
  using market prices for public assets
• 59:34
  and what the private people think it's worth prices
• 59:39
  for the private assets.
• 59:41
  And you'll see this, you'll see some, some investors put out
• 59:46
  their, their assumptions
• 59:47
  and they'll draw 'em on the efficient frontier
• 59:50
  and they'll have like public equities at 16% vol
• 59:53
  with an expected 10% return.
• 59:56
  Private assets at a 5% vol
• 59:58
  with an expected 14% return.
• 1:00:01
  You could debate the 14 versus the 10,
• 1:00:05
  but the, the the 16 against
• 1:00:07
  five, I think that's what I said.
• 1:00:10
  You cannot debate that that's just not apples to orange.
• 1:00:13
  That is apples to oranges.
• 1:00:15
  And it's, it pisses me off, off
• 1:00:18
  because I gotta live in the standard of, of
• 1:00:20
  what the market will pay me.
• 1:00:21
  I am just as capable of telling you
• 1:00:23
  what I think our portfolio is worth.
• 1:00:26
  And when we started a QR we started, I mentioned
• 1:00:28
  before, right before the.com bubble
• 1:00:30
  of the late nineties really took off and we were down a lot
• 1:00:33
  before we did very well.
• 1:00:36
  Well if I marked to what I think my portfolio is worth,
• 1:00:39
  I would've said all these tech stocks
• 1:00:41
  that were short, I don't think they're worth this.
• 1:00:45
  So I'm gonna mark it to what I think I'm worth.
• 1:00:47
  And if I tried to tell my clients actually we're up a little
• 1:00:50
  bit, I think they would sue me
• 1:00:53
  because they would say no you're not.
• 1:00:55
  Look at the prices. And
• 1:00:57
  what I don't get is the private people can do
• 1:00:59
  exactly the same thing.
• 1:01:01
  They are the world's best people at valuing a company.
• 1:01:05
  They could tell you where I could sell it today just
• 1:01:08
  as easily as they could tell you where their model is.
• 1:01:10
  And for some reason they get to do it one way
• 1:01:12
  and I get to do it another.
• 1:01:14
  And that part of the story I'm certain of
• 1:01:16
  that their risk is much higher than you think.
• 1:01:20
  If we go through a 10 year bear market,
• 1:01:22
  that risk will show up and
• 1:01:23
  that's pretty much all you really worry about is the
• 1:01:25
  giant bear market.
• 1:01:27
  So I do think they're understating their risks.
• 1:01:29
  It can even matter for prospective returns going forward.
• 1:01:33
  'cause imagine, and I find this very easy to imagine
• 1:01:36
  'cause I think this is actually what's going on,
• 1:01:38
  but imagine a lot of investors have grown to value this
• 1:01:44
  volatility laundering.
• 1:01:46
  It's easier to stick with if you don't have to deal
• 1:01:48
  with the real current market to market prices, right?
• 1:01:52
  The public markets are way down in 2022.
• 1:01:55
  Privates are only down a little easier to stick with.
• 1:01:58
  Well in the early days when David Swenson at Yale was,
• 1:02:01
  was pioneering using privates for institutions,
• 1:02:05
  it was pretty easy In his book,
• 1:02:07
  pioneering portfolio management talks about
• 1:02:09
  this all over the place.
• 1:02:11
  It was particularly easy to say you get paid a premium
• 1:02:14
  for owning the illiquid private asset.
• 1:02:16
  'cause who wants something not marked to market?
• 1:02:18
  That's hard to well imagine today.
• 1:02:22
  And again I think this is actually at least
• 1:02:23
  to some degree what's going on.
• 1:02:25
  Imagine today that that is a desired feature, not a bug.
• 1:02:30
  That not having to mark to market
• 1:02:31
  and not having to report the full
• 1:02:33
  volatility is a good thing.
• 1:02:35
  Well you pay up for a good thing.
• 1:02:38
  You accept a, you,
• 1:02:39
  you demand a higher expected return if you have
• 1:02:42
  to take on a bad characteristic,
• 1:02:44
  you accept a lower expected return if you have to take on,
• 1:02:47
  if you're forced to take on
• 1:02:48
  or if you take on a characteristic you want.
• 1:02:51
  So I think it can actually affect the
• 1:02:53
  expected returns going forward.
• 1:02:54
  I would not expect, I won't tell you
• 1:02:56
  what I think it's gonna be, it's all over the place,
• 1:02:58
  but I whatever premium and this is fought over
• 1:03:01
  'cause private data is private data.
• 1:03:03
  We don't have the best apples to to, to, to,
• 1:03:06
  to apple's comparisons.
• 1:03:07
  But whatever the past has been,
• 1:03:09
  my strongest guess is privates will not enjoy the same
• 1:03:12
  advantage over public going forward.
• 1:03:14
  'cause what used to be a bug is now a feature.
• 1:03:18
  - Hmm. That's,
• 1:03:20
  - And I live in Greenwich, Connecticut
• 1:03:21
  and I gotta be careful if I go near a golf course
• 1:03:24
  that people won't aim at me.
• 1:03:27
  - That's too funny. Yeah. The the there's a lot, a lot
• 1:03:29
  of private equity folks out there now increasing.
• 1:03:31
  Yes. Increasing number of them.
• 1:03:32
  - Yes. The old cliche applies to me.
• 1:03:34
  Some of my best friends are private equity managers.
• 1:03:38
  - It's that, that's too funny.
• 1:03:40
  I I hope they're, they're so kind to you.
• 1:03:43
  I wanna sort of, my last question
• 1:03:45
  or two here for you is really about your
• 1:03:47
  investment philosophy.
• 1:03:49
  And I remember when Fama
• 1:03:52
  and Schiller both won the Nobel Prize in 2013
• 1:03:54
  along with bars Hansen.
• 1:03:56
  It was I think a pretty seminal moment.
• 1:03:59
  And I mean in part
• 1:04:00
  because they were giving a prize to both Fama
• 1:04:05
  who is sort of the standard bearer,
• 1:04:06
  the risk-based decision markets view as well as
• 1:04:10
  Robert Schiller who is really the standard bearer, the,
• 1:04:14
  I would say very behavioral finance view.
• 1:04:16
  And I've always felt that you are,
• 1:04:18
  you're somewhere sort of in between.
• 1:04:20
  You recently wrote a paper titled The Less
• 1:04:23
  Efficient Market Hypothesis.
• 1:04:24
  I'm curious what you meant by that and
• 1:04:26
  - Sure.
• 1:04:27
  - What your kind of broader view is. Well
• 1:04:28
  - Let me start out by saying Fama should have won a sobel,
• 1:04:32
  solo Nobel Prize 10, 15 years earlier than that.
• 1:04:37
  I'm still a little bitter.
• 1:04:39
  - Not that Well they could still give him a second prize.
• 1:04:40
  I mean you could lobby for that.
• 1:04:42
  - I don't know how often, I don't know if
• 1:04:43
  that's ever been done, but I I know in the sciences people
• 1:04:46
  have won in other categories.
• 1:04:48
  Yeah, but, but it's gonna be hard for it
• 1:04:50
  to get a second one in economics.
• 1:04:52
  I think. So I think pharma revolutionized the field.
• 1:04:55
  We, the efficient market hypothesis,
• 1:04:57
  no matter whether you believe it's close to holding
• 1:05:00
  or not, is, is kind of the firmament we start
• 1:05:04
  with to test everything.
• 1:05:05
  So, you know, I think he's the MVP of modern finance.
• 1:05:09
  With that said, make it up numbers.
• 1:05:13
  I was never a pure behavioralist.
• 1:05:15
  Remember I wrote my dissertation a a big part on momentum,
• 1:05:18
  which is very hard to reconcile with, with,
• 1:05:20
  with risk-based explanations.
• 1:05:22
  But I was probably 75 25 f French risk-based
• 1:05:28
  and I'm probably 75 25 behavioralist at this point.
• 1:05:34
  You know, the real world smacking you in the head, living
• 1:05:36
  through multiple things where I would use the word bubble.
• 1:05:40
  I'd use the word bubble about early 2000.
• 1:05:42
  I'd use the word bubble about late COVID late 2020.
• 1:05:47
  I don't use that word very much.
• 1:05:49
  I still use it a lot less than a fair amount
• 1:05:51
  of active managers twice in a career is not,
• 1:05:54
  I think too abusive, but jean don't like that word.
• 1:06:00
  So the less efficient market hypothesis was really
• 1:06:03
  A-A-A-A-A-A 20 page op-ed,
• 1:06:08
  the Journal of Portfolio Management had its 50th,
• 1:06:10
  I think 50th anniversary
• 1:06:11
  and they invited some of us who've been publishing there a
• 1:06:14
  long time to write papers and invited papers are awesome
• 1:06:18
  'cause they're not refereed.
• 1:06:20
  So you have a, you, you have a lot more latitude.
• 1:06:24
  Frank Zi the wonderful publisher there.
• 1:06:27
  I think he, he would do some quality control if I submitted
• 1:06:30
  something insane, but it's not quite the same.
• 1:06:33
  He encouraged us to muse a little bit more.
• 1:06:35
  It was supposed to be like an old man
• 1:06:37
  reflecting on what they've learned.
• 1:06:39
  And I do think over my career I have moved on this
• 1:06:43
  and I think markets have actually changed somewhat that,
• 1:06:47
  that that value spread thing we described earlier, the,
• 1:06:50
  the spread between cheap and expensive by March of 2000,
• 1:06:54
  the.com bubble hit levels that were not close
• 1:06:57
  to anything in 50 years of history when the next two
• 1:07:02
  to three years happened
• 1:07:03
  and we made way more than all that money back.
• 1:07:06
  And life was good. If you had said to me,
• 1:07:08
  are you gonna see something as crazy in your career again,
• 1:07:13
  I don't think, I hope I never would've promised anything,
• 1:07:16
  not just for legal reasons, but for intellectual reasons.
• 1:07:19
  Never say never. But I, I think I would've said very,
• 1:07:21
  very unlikely for one, it was the biggest in 50 years.
• 1:07:27
  I, you know, as much as I'd like to live forever,
• 1:07:29
  I don't think I necessarily have a 50 year career.
• 1:07:32
  Two, your question presupposes I
• 1:07:34
  and others like me will still be around in your career.
• 1:07:38
  And we saw this
• 1:07:39
  and then by the end
• 1:07:41
  of COVID it was getting close before COVID.
• 1:07:43
  So I can't blame COVID for the whole thing.
• 1:07:46
  By the end of COVID by late 2020, it was substantially wider
• 1:07:51
  of value spread as we measure it than we saw
• 1:07:53
  in the.com bubble.
• 1:07:55
  So if that doesn't make you go,
• 1:07:56
  has something changed versus history
• 1:07:58
  over the last 20, 30 years?
• 1:08:00
  And I have a few hypotheses.
• 1:08:02
  I know you are very, the inelastic market hypothesis, how
• 1:08:06
  that affects indexing and whatnot.
• 1:08:09
  The, the growth of indexing could matter.
• 1:08:12
  I I am not someone who thinks the growth
• 1:08:16
  of indexing is the worst thing
• 1:08:18
  that's ever happened in the world.
• 1:08:19
  There are a fair amount of people who are,
• 1:08:21
  I think a little hysterical about it,
• 1:08:24
  but we all know that the, the, the classic kind of finance,
• 1:08:28
  you know, brain teaser, what if everybody tried
• 1:08:31
  to cap weight index is gibberish, right?
• 1:08:34
  There's literally nobody thinking about prices.
• 1:08:38
  It's hard to believe we jumped to gibberish going from
• 1:08:40
  99.999% indexing to a hundred.
• 1:08:44
  So the fact that we have a lot more people cap weight
• 1:08:47
  indexing and a lot and a lot fewer people looking at
• 1:08:49
  companies, could that matter for this?
• 1:08:51
  Could that let prices drift further?
• 1:08:54
  I don't have a formal model but yeah, I accept that.
• 1:08:57
  My favorite hypothesis and I I stress the word hypothesis
• 1:09:00
  'cause nobody knows is I'm gonna really sound like a,
• 1:09:04
  an old man yelling at clouds now.
• 1:09:08
  But it, the,
• 1:09:09
  the the social media environment 24 7 gamified trading.
• 1:09:15
  I think most people of any political persuasion,
• 1:09:18
  I won't say everyone 'cause we have a lot of people
• 1:09:20
  with some odd views these days,
• 1:09:23
  but I think most people would probably agree
• 1:09:25
  that this environment has made our politics worse.
• 1:09:28
  It's pushed us into bubbles.
• 1:09:31
  It's confirmation bias has gone way up.
• 1:09:35
  It, it has made herd mob behavior
• 1:09:40
  easier to get going.
• 1:09:42
  Markets are voting mechanisms.
• 1:09:45
  They're not arbitrage mechanisms, they're voting mechanisms.
• 1:09:48
  The old schleifer ny limits of arbitrage said no,
• 1:09:52
  it could be an arbitrage,
• 1:09:53
  but if you can't hold it, it doesn't last.
• 1:09:55
  If, if if somebody has is making an error.
• 1:09:58
  Fama French wrote a paper on this too.
• 1:10:00
  If somebody is making an error, you take the other side of
• 1:10:03
  that error but not enough tobit to zero.
• 1:10:06
  'cause the early part
• 1:10:07
  of taking the other side makes you a decent expected
• 1:10:10
  return and low risk.
• 1:10:11
  But as you keep adding that to your portfolio,
• 1:10:14
  you're closing the gap
• 1:10:15
  and the expected return goes down and the risk goes up.
• 1:10:17
  'cause you have to own more of it.
• 1:10:19
  So if, if, if the world makes net errors as fa
• 1:10:23
  and French say, and I'm paraphrasing,
• 1:10:25
  less errors miraculously balance each other,
• 1:10:28
  prices will be set in a vote in a dollar weighted vote.
• 1:10:32
  So I find it very hard to believe
• 1:10:34
  that this environment has made our politics much worse.
• 1:10:37
  But a voting mechanism called the market has been
• 1:10:40
  immune to it.
• 1:10:42
  I could be cheap and point to the meme stocks.
• 1:10:45
  I think they are a extreme mutated
• 1:10:48
  example of what I'm talking about.
• 1:10:50
  But I think it has, it has made the idea of,
• 1:10:54
  of mispricings,
• 1:10:56
  even if they're not all the time occasional large
• 1:10:58
  mispricings from mob behavior, much more plausible
• 1:11:01
  to me. Sorry, go
• 1:11:04
  - On.
• 1:11:05
  There's, there's this question about quantities, right?
• 1:11:06
  So, so i I think it's it's very much integrated in into this
• 1:11:10
  where, you know, right now there's in academic finance a big
• 1:11:13
  debate happening and,
• 1:11:15
  and you mentioned this about, you know, the role quantities
• 1:11:19
  and and what do we mean by that?
• 1:11:20
  You know, it's, if if you hypothetically had, you know,
• 1:11:23
  a a billion dollars on the sidelines in cash
• 1:11:26
  that just went into the stock market, what would the effect
• 1:11:28
  of that, you know, influx of cash say $1 billion,
• 1:11:32
  would it be, you know, a permanent increase
• 1:11:36
  of $5 billion or multiplier five?
• 1:11:38
  And that's kind of what the very behavioral folks like
• 1:11:41
  Xavier Gbe and Ralph Koan argue in their new paper
• 1:11:44
  Inelastic Markets hypothesis.
• 1:11:46
  It's called the Inelastic Markets hypothesis.
• 1:11:48
  Some people would argue it's a bit less, you know,
• 1:11:51
  there was a paper in the 1980s by Andre Schleifer
• 1:11:54
  that you know, you know, does the demand
• 1:11:56
  curve for stocks slow down?
• 1:11:57
  I sort of, I have a tongue in cheek paper called the elastic
• 1:12:00
  markets hypothesis that argues it is somewhere in between,
• 1:12:02
  probably similar to yourself.
• 1:12:04
  But there is this question about, you know,
• 1:12:06
  what is the shape of the demand curve for assets
• 1:12:10
  and you know, in efficient markets world,
• 1:12:11
  I think I would say it's sort of perfectly elastic.
• 1:12:13
  Very, very horizontal.
• 1:12:15
  And in that sort of Fama world
• 1:12:18
  or modern finance world, information was key.
• 1:12:20
  Arbitrage were key. Yeah. But it seems like
• 1:12:22
  - Prices expected discounted cash flows,
• 1:12:24
  which doesn't move with a trade.
• 1:12:26
  - Exactly. And it seems like this kind of, you know,
• 1:12:28
  say very behavioral vision of very inelastic markets
• 1:12:33
  is is almost going sort of pre-modern finance
• 1:12:35
  where everything was about supply and demand.
• 1:12:38
  And I'm just curious what you think about, you know, how qu
• 1:12:41
  how quantities work.
• 1:12:42
  I mean a QR I'm sure spends a lot
• 1:12:44
  of time thinking about market impact
• 1:12:46
  of trading transaction costs.
• 1:12:48
  You know, there there's this famous, you know,
• 1:12:49
  three half model
• 1:12:51
  or you know, quad model of market impact out there.
• 1:12:53
  I mean how do you think about sure quantities market impact,
• 1:12:56
  is it a short term thing and things revert
• 1:12:58
  or is it a permanent thing in the same respect that gibe
• 1:13:01
  and coin and argue there's this five x
• 1:13:03
  multiplier that's kind of a permanent thing.
• 1:13:04
  Sure. How do you think about that?
• 1:13:06
  - Okay, it's your fault. Again,
• 1:13:08
  this is a nine part question.
• 1:13:10
  A first full disclosure.
• 1:13:12
  I think the world of Ralph Cogen, we gave him an award
• 1:13:16
  for the inelastic market hypothesis paper.
• 1:13:18
  'cause we used to give out an annual award
• 1:13:21
  and he is actually the A QR professor
• 1:13:23
  of finance at the University of Chicago.
• 1:13:26
  So with that said, I am,
• 1:13:30
  as you predicted closer to you on this.
• 1:13:34
  I i I don't, I my guess,
• 1:13:37
  and it really is a guess, you're closer
• 1:13:39
  to the academic debate than I am.
• 1:13:42
  My guess is it comes out
• 1:13:44
  that markets are certainly not perfectly elastic gene,
• 1:13:48
  gene Fama when it comes to the efficient market hypothesis.
• 1:13:50
  Something like second week of class.
• 1:13:52
  He tells you markets are certainly not perfectly efficient
• 1:13:55
  and the class guess 'cause they're Gene Fama's class
• 1:13:58
  and the University of Chicago
• 1:14:00
  and he looks at him like, he doesn't say this
• 1:14:02
  'cause a very nice man, but he is like, what are you idiots?
• 1:14:04
  Perfection is a stupid hypothesis.
• 1:14:06
  The real world isn't perfect.
• 1:14:09
  So I'm gonna guess some of it holds up.
• 1:14:11
  I'm gonna get some of the critiques that are coming out,
• 1:14:13
  bring those results a little back towards the middle.
• 1:14:17
  But one thing I stress is I make very little
• 1:14:20
  of my living based on market direction.
• 1:14:23
  I'm, I make it much more based on stock
• 1:14:25
  and other asset selection.
• 1:14:26
  We do macro also,
• 1:14:27
  but thinking about just stocks much more
• 1:14:30
  of my life is about a thousand
• 1:14:31
  to 1500 stocks long against a thousand to 1500 stocks short.
• 1:14:36
  And the, the, the idea that
• 1:14:38
  that is highly inelastic is far less
• 1:14:41
  plausible in the market.
• 1:14:42
  We can debate the market,
• 1:14:44
  but the market has few close substitutes
• 1:14:48
  and you can't have, if you, if if you have few substitutes,
• 1:14:51
  it's at least plausible to be somewhat inelastic.
• 1:14:55
  But if you have a lot of close substitutes,
• 1:14:57
  I think it gets very implausible.
• 1:14:59
  And individual stocks have tons of close substitutes.
• 1:15:03
  So I have not seen you, you might be more,
• 1:15:05
  you probably are more familiar with the literature,
• 1:15:08
  any paper claiming that that individual stocks
• 1:15:12
  in a say a market neutral trade are highly inelastic.
• 1:15:18
  It. Have you?
• 1:15:21
  - Well, I I I don't, I think the claim that Quebec,
• 1:15:26
  - It's a hadda paper that has something along this line.
• 1:15:28
  - There, there are some that have tried
• 1:15:30
  to push the inelastic markets hypothesis thing
• 1:15:33
  beyond just sort of macro assets,
• 1:15:34
  which is I think the original sort of Ralph coin argument
• 1:15:38
  that, that it may exist also in, in at the stock level.
• 1:15:43
  But I'm, I'm skeptical of claims about either.
• 1:15:47
  - Yeah, but, and when it comes to market impact,
• 1:15:50
  first of all it's very real.
• 1:15:52
  You know, if you assume zero market impact,
• 1:15:54
  you way overestimate what you'll make from any model
• 1:15:57
  or active process.
• 1:15:58
  But comes
• 1:16:01
  to reversion at the individual stock level,
• 1:16:04
  one stock against another.
• 1:16:06
  I think most of market impact is not permanent
• 1:16:10
  Because again, being close substitutes, I don't think it,
• 1:16:13
  it, it, it moves prices to the same extent.
• 1:16:16
  And it would be kind of weird if the prospects
• 1:16:19
  of one stock against another were permanently altered
• 1:16:22
  because somebody put a trade on one day.
• 1:16:24
  You can imagine the market has to absorb that a lot
• 1:16:27
  of people, which is not us,
• 1:16:29
  make their living from so-called statistical arbitrage
• 1:16:32
  even going into the high frequency trading world.
• 1:16:35
  And a lot of that is about what tends to trend
• 1:16:37
  and what tends to reverse at the short term.
• 1:16:39
  And again, this is not my world.
• 1:16:41
  I wrote a paper that was very close to state
• 1:16:43
  of the art stat arb in 1995,
• 1:16:47
  and I've been told this by stat arb people
• 1:16:48
  and it's my fifth story, you know,
• 1:16:49
  every fisherman has the one that got away the big one.
• 1:16:53
  And I looked at it and said, doesn't cover TCOs.
• 1:16:55
  And moved on. And,
• 1:16:58
  and it did ultimately, if you kept that, it covered TCOs,
• 1:17:01
  I should have kept with that one big error.
• 1:17:03
  But I think most
• 1:17:05
  of the market impact we look at is relatively short term.
• 1:17:10
  And, and the jury I think is still out on market
• 1:17:14
  wide market impact.
• 1:17:16
  Even there, I find it hard
• 1:17:17
  to believe you get a forever change in price.
• 1:17:20
  But is it last, can it last longer?
• 1:17:23
  Is it hard to take the other, harder
• 1:17:25
  to take the other side there, the plausibility of the, of,
• 1:17:28
  of the kogen etal story, she only has one co-author,
• 1:17:31
  so I shouldn't say etal,
• 1:17:33
  but I can never pronounce the name right.
• 1:17:34
  So
• 1:17:36
  - Get back is alright friend's last name,
• 1:17:39
  - Yes.
• 1:17:40
  We, we don't mean this meanly
• 1:17:41
  or a wonderful researcher, the plausibility of that story,
• 1:17:45
  but when he says how many people will take a market bet,
• 1:17:50
  we tend to run most things to a zero beta.
• 1:17:52
  So if somebody wants to go long extra stocks, we tend to
• 1:17:57
  mostly, I won't say completely, mostly not be the people
• 1:18:00
  who take the other side of that.
• 1:18:02
  So then, you know, a lot of funds are wired to do that now.
• 1:18:06
  So the notion that that some of that maybe again,
• 1:18:09
  full permanency, I just instinctively find hard to believe.
• 1:18:14
  But the idea that is far closer to, to permanent,
• 1:18:17
  that lasts much longer.
• 1:18:18
  I, I I find that plausible, the, the market wide argument,
• 1:18:22
  which I think is where most of this is occurring,
• 1:18:26
  is mostly spectator sport.
• 1:18:28
  For me, it, I don't think it affects very much what we do,
• 1:18:32
  but I will be watching to see which one
• 1:18:34
  of you guys ends up winning.
• 1:18:37
  - Well, yeah, well I feel like there's,
• 1:18:38
  there's some similarities with the, the, the less
• 1:18:42
  efficient markets hypothesis in the elastic markets
• 1:18:45
  hypothesis and things are almost efficient
• 1:18:48
  but not perfectly efficient.
• 1:18:49
  And there, you know, there's some things
• 1:18:51
  that are behavioral out there, but, but, but not a ton.
• 1:18:53
  So, so maybe we'll we will, we'll take
• 1:18:55
  that tag team for the win.
• 1:18:56
  Cliff, I really wanna thank you for coming on.
• 1:18:58
  This has been an amazing conversation.
• 1:19:00
  - Oh, I really enjoyed it, John.
• 1:19:01
  These were fantastic questions and thank you for having me.
• 1:19:06
  - This is the Capitalism and Freedom,
• 1:19:07
  the 21st Century podcast, an official podcast
• 1:19:09
  of the Hoover Institution Economic Policy Working group
• 1:19:11
  where we talk about economics, markets, and public policy.
• 1:19:14
  I'm John Huntley, your host. Thanks so much for joining us.