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Masters in Business: AQR Scott Cliff Asness (Audio) · Masters in Business

0:03
Speaker 1: This is Masters in Business with Barry Riddholts on Bloomberg Radio.
0:08
Speaker 1: Welcome to Masters in Business. I'm Barry Ridholts. You're listening
0:11
Speaker 1: to Bloomberg Radio and today's guest is someone uh you
0:16
Speaker 1: will find absolutely fascinating. First, let me give you a
0:19
Speaker 1: brief intro. How do I begin to describe Cliff ass Ness.
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Speaker 1: He is the founder of a q R, which is
0:26
Speaker 1: a hundred and twenty billion dollar asset management firm slash
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Speaker 1: hedge fund. A q R stands for Applied Quantitative Research.
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Speaker 1: Fascinating gentleman, really really interesting background. Undergraduate University of Pennsylvania,
0:41
Speaker 1: Wharton Economics, you pen engineering degree a double engineer, double major.
0:47
Speaker 1: Goes to University of Chicago, ends up with an m
0:50
Speaker 1: b a slash PhD in the Chicago program. Um eventually
0:55
Speaker 1: takes a time off to go to Goldman Sachs, where,
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Speaker 1: at age twenty nine, he's the head of the quant group.
1:01
Speaker 1: Just an absurd, absurd resume. Pull up the papers that
1:06
Speaker 1: he's written, dozens of award winning best and Best in Show,
1:11
Speaker 1: Best of the Year. Just really a phenomenally interesting and
1:16
Speaker 1: insightful guy. A little background as to how we met
1:20
Speaker 1: and how he ended up doing the show because um,
1:22
Speaker 1: as he generally said during the interview, I stalked him
1:25
Speaker 1: for a while. So Cliff rights very frequently, which is
1:30
Speaker 1: something that we do in our shop. And I really
1:32
Speaker 1: like people who take all their thoughts and put them
1:35
Speaker 1: down in an organized, structured fashion, and they have something
1:40
Speaker 1: interesting to say, and lots of lots of things that
1:43
Speaker 1: he writes are just fascinating and insightful and just a
1:47
Speaker 1: touch of humor, a dollarp of humor thrown in. You
1:50
Speaker 1: look at some of the headlines of his white papers
1:52
Speaker 1: or his blog post and you'll see some really clever
1:56
Speaker 1: amusing titles that with lots and lots of pop cultural references.
2:01
Speaker 1: In addition, there ain't a whole lot of hedge fund
2:04
Speaker 1: managers running the sort of money he is that tweet
2:07
Speaker 1: on a regular basis, and so he published. He was
2:11
Speaker 1: one of the signatories to the letter to Ben Ben
2:13
Speaker 1: Bernanke worried about hyper inflation and the collapse of the dollar,
2:17
Speaker 1: and I called him in a number of people out
2:19
Speaker 1: on it. And while many of the people who who
2:23
Speaker 1: were on the wrong side of the hyper inflation debate
2:26
Speaker 1: kind of pretended they never said it, Cliff came right
2:29
Speaker 1: back at me on Twitter. Well, you know there's still time,
2:33
Speaker 1: but so far it looks like I'm wrong and you're right. Hey,
2:35
Speaker 1: you don't get that sort of response from people. Usually
2:38
Speaker 1: you get a lot of arrogance and pushed back and
2:40
Speaker 1: we'll see, you know, it's only been five years, give
2:42
Speaker 1: it another decade. But he's not that way. He's like, well,
2:45
Speaker 1: so far, I've clearly been wrong. And you know, he's
2:48
Speaker 1: a real data guy and I respect that tremendously. Um,
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Speaker 1: you'll find he's not your typical billionaire hedge fund manager. Uh.
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Speaker 1: Extremely Even though he doesn't do a lot of media,
3:01
Speaker 1: and even though I stalked him for a good year
3:03
Speaker 1: to get him on the show, he was really stunning
3:07
Speaker 1: Lee charming and self effacing and witty and not at
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Speaker 1: all what you would imagine a lot of people in
3:15
Speaker 1: his position are like. And if you read some of
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Speaker 1: his writings, they're very very you know, here's the data,
3:23
Speaker 1: here's the context, here's here's what the details are, and
3:26
Speaker 1: that's pretty much it. They're very aggressive, they're a little
3:28
Speaker 1: in your face, but he's got the goods to back it.
3:31
Speaker 1: If he's in your face, it's because the numbers are there,
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Speaker 1: and he's at heart a numbers guy. So we stopped
3:38
Speaker 1: just around the two hour mark. I'm not kidding when
3:41
Speaker 1: I say this could have gone another two hours. We
3:44
Speaker 1: absolutely would not have run out of questions. I find
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Speaker 1: him to be one of the most fascinating people in finance.
3:51
Speaker 1: Lots of what he writes is really really influential. It
3:55
Speaker 1: moves the debate forward, it moves the argument of finance forward.
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Speaker 1: And um, I think this is one of the most
4:02
Speaker 1: interesting conversations we've had on the show. So without me
4:06
Speaker 1: babbling a whole lot further, I think you're really gonna
4:08
Speaker 1: enjoy our conversation with a qu Rs Cliff Astenus. This
4:17
Speaker 1: is Masters in Business with Barry Ridholts on Bloomberg Radio.
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Speaker 1: My special guest today is someone who I've been chasing
4:24
Speaker 1: for a while to have on the show. I'm gonna
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Speaker 1: introduce him first and then give you his curriculum vitae,
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Speaker 1: and that will be the first half hour of the show.
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Speaker 1: Cliff Fastness, welcome to the program. Thank you, Barry. I'm
4:35
Speaker 1: excited to be here. Um, I'm I'm actually really excited
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Speaker 1: to have you. Let me. For those of you who
4:40
Speaker 1: are not in the financial industry and may not be
4:42
Speaker 1: familiar with Cliff assness name. We have a little bit
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Speaker 1: of of financial royalty here. First, you're the founder and
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Speaker 1: chief investment officer of a q u R, which runs
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Speaker 1: about a hundred and twenty billion dollars in both hedge
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Speaker 1: funds and traditional strategies. Have a few co founders, David
5:01
Speaker 1: Cambrella and John lou Those are the guys that you
5:03
Speaker 1: were working with at Goldman Sachs. Correct, When before we
5:06
Speaker 1: get to Goldman Sachs, you graduated you penn with a
5:10
Speaker 1: degree in economics from Wharton and a simultaneous degree Bachelors
5:14
Speaker 1: of Science and Engineering. Yes, either one of those is
5:18
Speaker 1: a heck of a degree. To have a lot of
5:20
Speaker 1: a lot of effort, a lot of work, both of
5:22
Speaker 1: them at once. That's really an impressive. I didn't know
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Speaker 1: any better and had no social life there you go.
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Speaker 1: So you go from Wharton to Chicago at the Booth School.
5:34
Speaker 1: But may not it was, It wasn't, but it was
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Speaker 1: just Chicago. I'm giving David as due was the Booths
5:39
Speaker 1: School just wasn't called it yet. And you the research
5:41
Speaker 1: assistant to Eugene Fama, known as the father of the
5:45
Speaker 1: efficient market hypothesis. We'll get to that a little later.
5:48
Speaker 1: Because you're not exactly an E. M. H type of guy,
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Speaker 1: and um, he chaired your doctoral dissertation. You got your
5:54
Speaker 1: PhD um which is not in any specific atis a PhD.
5:57
Speaker 1: And no people always ask me this. It's you get
6:00
Speaker 1: it from the business school. You effectively major in whatever
6:03
Speaker 1: you wrote your dissertation in, but it's a generic business
6:06
Speaker 1: school PhD. And so you decide to write your dissertation
6:09
Speaker 1: on how you could use momentum and value to beat
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Speaker 1: the market when your doctoral thesis advisor is the guy
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Speaker 1: who says, no, you can't. Yes, that was with some trepidation.
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Speaker 1: He and his co author was also one of my advisors.
6:22
Speaker 1: Ken French had already done a lot of work on value.
6:24
Speaker 1: French from again More Financial Royalty. To say the least,
6:29
Speaker 1: they had already done a lot of the pioneering work
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Speaker 1: on value investing and value investing. It's still a fight.
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Speaker 1: It's still arguable whether it works because markets are irrational
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Speaker 1: or whether it works because it's a risk premium of
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Speaker 1: some kind. And we could spend a whole show talking
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Speaker 1: about that momentum. I guess some people argue about it
6:46
Speaker 1: but I think the literature is far stronger on the
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Speaker 1: irrational side. So yeah, it was scary to tell you.
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Speaker 1: I tell I've told the story for years, going up
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Speaker 1: to Gane and saying I want to write a dissertation
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Speaker 1: on on price momentum, the simplest of all momentum to
6:59
Speaker 1: dr Farm and and then I mumbled the second part.
7:02
Speaker 1: And it works really well because a dissertation saying momentum
7:06
Speaker 1: is terrible is a perfect Fama dissertation. You know, look
7:08
Speaker 1: at these fools following this to his credit, and this
7:11
Speaker 1: is actually I think a warm story. Um, he has
7:13
Speaker 1: his opinions, and he but he is brilliant and he's
7:16
Speaker 1: open minded. Um, you don't change his mind. But he
7:19
Speaker 1: is very comfortable. He said, if it's in the data,
7:22
Speaker 1: write the paper. Wow, that's really interesting. So the weather
7:25
Speaker 1: in Pennsylvania is bad enough, what made you say I
7:29
Speaker 1: want to go to Chicago? It gets worse? So I
7:31
Speaker 1: had to choose between Chicago and Stanford. They had precisely
7:34
Speaker 1: the same scholarship program for Peach. Peach season has got
7:38
Speaker 1: a good deal, we don't actually pay. It was exactly
7:41
Speaker 1: the same, and UM, Chicago at that point probably still
7:44
Speaker 1: UM was just an incredible program. I got advice from
7:47
Speaker 1: so many people saying, if you if you're serious about finance,
7:49
Speaker 1: and you can go to Chicago, go But it was
7:51
Speaker 1: pretty hard enough to go to Stanford. I can imagine.
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Speaker 1: Did you know you would be working with Fama beforehand? No,
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Speaker 1: he kind of chooses you, and I got lucky and
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Speaker 1: he chose me. Um. He was one of the reasons
8:02
Speaker 1: I'd certainly heard of him, but less so than you
8:03
Speaker 1: can imagine. I was a little naive. Um. I just
8:06
Speaker 1: walked around, said I want to study this more. Where
8:08
Speaker 1: should I go? I got the advice to go there,
8:10
Speaker 1: took jeans class. Uh. And then at the end of
8:13
Speaker 1: his the first year, he asked somebody to ta this
8:16
Speaker 1: class next year. And I got lucky. He gave me
8:19
Speaker 1: the tap. That's great. Now you leave, you graduate, you
8:22
Speaker 1: get your your doctor. And now I left without the
8:24
Speaker 1: doctorate for Goldman. I left to take a year off,
8:27
Speaker 1: to take a year off, that's to find myself. No,
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Speaker 1: I I got it eventually. It's not doctor essence because
8:34
Speaker 1: it's massively pretentious for finance. These were doctor and I'm
8:37
Speaker 1: afraid you'll ask me to check your hernia. Well, I
8:41
Speaker 1: did get the PhD along the way. So now you
8:45
Speaker 1: end up at Goldman you actually be eventually become director
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Speaker 1: of Quantitative Research for asset management. At age twenty nine,
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Speaker 1: you formed the Goldman Sacks Global Alpha Funds, which was
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Speaker 1: a quant vehicle for deploying their capital using your quantitative metrics.
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Speaker 1: That's at age twenty nine. And then sixteen years ago
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Speaker 1: you found a q R, which stands for applied quantitative research.
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Speaker 1: Didn't know anyone knew that anymore. Yes, I guess that,
9:14
Speaker 1: but I guess that and punched it in when it
9:16
Speaker 1: came up thought about starting our own firm. We had
9:18
Speaker 1: a whole bunch of you know, three of us who
9:20
Speaker 1: were thinking about it. Should we do this? Should we not?
9:22
Speaker 1: We ended up spending almost all the time talking about
9:24
Speaker 1: what we should name it, and we came up with
9:25
Speaker 1: the IBM of Firms, about the most boring name you
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Speaker 1: can imagine, but if it's what we do exactly, and
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Speaker 1: it's better because when you first see a q R,
9:32
Speaker 1: it's like, all right, it's as in this, and then ye,
9:34
Speaker 1: people are still looking for the you know they're looking
9:36
Speaker 1: for right, and I left out by the way, some
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Speaker 1: of the awards top paper um from the Graham and
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Speaker 1: Dot Award in two thousand three and two thousand eleven
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Speaker 1: best perspectives piece for the financial analyst general? Is that
9:48
Speaker 1: what that is? Two thousand four and then the Portfolio
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Speaker 1: Management Best Paper Award O one oh three and that's
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Speaker 1: all the time we have. Thanks for coming by, Cliff.
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Speaker 1: So so really, the first question I gotta ask you
10:02
Speaker 1: in the last minute we have in this segment is
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Speaker 1: what made you decide to go into not just finance
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Speaker 1: but quantitative finance. First, there was total indecision. Um. I
10:14
Speaker 1: took this. You mentioned my undergrad I was an engineer
10:17
Speaker 1: in a business school student because I had no idea
10:19
Speaker 1: what I wanted to do. I was fairly mathematical, and
10:22
Speaker 1: my father read about this program, this dual degree program,
10:25
Speaker 1: and said, why don't you do this? You can decide later.
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Speaker 1: That was the total amount of planning. I like the finance.
10:31
Speaker 1: I liked I found it intellectually interesting, and to be honest,
10:35
Speaker 1: you know, you never know. You look back when you
10:37
Speaker 1: were twenty and you try to figure out if you
10:39
Speaker 1: planned it or not. Um, but I think I was
10:41
Speaker 1: attracted to the idea of something that I found intellectually fascinating,
10:44
Speaker 1: that you could make an actual career out of that.
10:47
Speaker 1: Wasn't the archaeology. I just upset all the archaeologists out there.
10:50
Speaker 1: I'm Barry, what helps. You're listening to Masters in Business
10:53
Speaker 1: on Bloomberg Radio. My guest today is Cliff Assness. He's
10:56
Speaker 1: the founder and chief investment officer of a q R,
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Speaker 1: which Fortune Magazine called one of the most important and
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Speaker 1: influential hedge funds in the world. Cliff, let's talk a
11:08
Speaker 1: little bit about the quant crisis of oh seven, and
11:11
Speaker 1: I'm just gonna quickly set the table. We had. We
11:15
Speaker 1: had the bear Sterns wobbling a bit, then the bear
11:18
Speaker 1: Sterns hedge funds around June of oh seven, right, they
11:22
Speaker 1: blew up and that was a whole another issue. And
11:26
Speaker 1: then we saw in August a lot of things hit
11:29
Speaker 1: the fan all at once. That was a really rough period.
11:33
Speaker 1: That began a period where let's I don't want to
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Speaker 1: talk about the entire financial crisis, but the quant crisis
11:39
Speaker 1: and oh seven, you guys saw a short period of
11:42
Speaker 1: time you lost of assets that that had to be
11:46
Speaker 1: a hellish period to live through. I um, we can't
11:49
Speaker 1: do this segment because I've blocked it all out. Okay,
11:51
Speaker 1: so it's just all suppressed so before we then, let
11:54
Speaker 1: me let me try and free that up just so,
11:59
Speaker 1: so just sscribe for listeners who may not be familiar
12:02
Speaker 1: with what quants are. What is a quant what is
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Speaker 1: the quantitative approach to invest? Sure? Um, there are some
12:09
Speaker 1: other people who will do quantitative techniques. I'll describe it
12:12
Speaker 1: very generally and as it applies to us. Quant investment
12:16
Speaker 1: managers are about I would say two words describing very well.
12:19
Speaker 1: They're about averages, and they're about diversification. They're not about
12:23
Speaker 1: kind of the sexy side of here's my best stock pick? Uh.
12:27
Speaker 1: Sometimes I respond if people ask me, you know, what's
12:29
Speaker 1: your favorite stock or what's your biggest holding, I look
12:32
Speaker 1: at them and go, I don't know. Which is usually true,
12:35
Speaker 1: and they get very confused because we are on thousands
12:37
Speaker 1: of But let me give you some examples. If cheap
12:41
Speaker 1: stocks measured you have any of your favorite ways, price
12:44
Speaker 1: divided by earnings, cash flow, dividend sales tend to beat
12:47
Speaker 1: expensive stocks on average over time. On average, yes, on
12:51
Speaker 1: average over time, not always, not even close to always.
12:54
Speaker 1: I wish it was always. If they tend to win,
12:56
Speaker 1: and that's all you knew and all you believed, you
12:59
Speaker 1: wouldn't bet on it by picking your three favorite cheap
13:01
Speaker 1: stocks and shorting your three least favorite expensive stocks. You
13:06
Speaker 1: believe in the average, you would do it with a
13:08
Speaker 1: very diversified portfolio of cheap and you would sell or underweight,
13:11
Speaker 1: depending if it's a hedge fund or if it's a
13:13
Speaker 1: traditional portfolio, a very diverse side portfolio of expensive and
13:17
Speaker 1: you'd hope that your logic and your historical evidence repeated
13:21
Speaker 1: itself over the next long term. We have found, along
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Speaker 1: with others, this is not unique to us. A set
13:28
Speaker 1: of things that work like value is one of them.
13:32
Speaker 1: I didn't choose the random of the example randomly. You
13:35
Speaker 1: gotta be careful When I say work, I mean like
13:36
Speaker 1: a statistician two out of three years, three out of
13:39
Speaker 1: four years. If your car work like this, you'd fire
13:41
Speaker 1: your mechanic. You need an economic story why it works.
13:45
Speaker 1: I don't believe in just data. I believe in try
13:46
Speaker 1: and understand why it works. And you need a lot
13:49
Speaker 1: of out of sample tests, for instance, in value and
13:51
Speaker 1: and all and all the things we do, we have
13:53
Speaker 1: found it's not just individual stocks. We always end up
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Speaker 1: discussing individual stocks, but cheap countries measured at an analogous
14:00
Speaker 1: way at the country level, Cheap bond markets using real
14:03
Speaker 1: bond yield slope of the yield curve, cheap currencies using
14:06
Speaker 1: purchasing power parity, cheap commodities using just commodities to press
14:10
Speaker 1: first day long term average. Anything we've looked at shows
14:13
Speaker 1: some tendency for cheap to beat expensive. Then other things
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Speaker 1: exist momentum, which you mentioned. I wrote my dissertation on
14:21
Speaker 1: things that are getting better relatively recently tend to beat
14:24
Speaker 1: things that are getting worse. It feels like the opposite
14:26
Speaker 1: of value, but very different times. Say that again, things
14:28
Speaker 1: that are getting better relatively recently six to twelve months, um,
14:33
Speaker 1: be things that are still on a down slopes um.
14:36
Speaker 1: And this, uh, this is a bit of a blow
14:38
Speaker 1: to the efficient market hypothesis. Maybe one day people will
14:40
Speaker 1: reconcile them. Um. But that kind of shouldn't be. It's
14:44
Speaker 1: it's a little too easy. I called the new to
14:46
Speaker 1: newspaper strategy. If you're only trading price momentum, we need
14:48
Speaker 1: a newspaper from today, in a newspaper from a year ago.
14:52
Speaker 1: I think I when I when I wrote this dissertation
14:54
Speaker 1: for gene Fama, who is both efficient markets and the
14:57
Speaker 1: value Guru. I didn't say this is better than value.
15:00
Speaker 1: That would have been both wrong investing and bad graduation strategy.
15:04
Speaker 1: I said it complements value very well. They both make money.
15:08
Speaker 1: Value is a much slower turnover strategy. You often do
15:11
Speaker 1: find stocks, even though it doesn't sound common. You often
15:13
Speaker 1: find stocks that are still cheap, not as cheap as
15:16
Speaker 1: they were a year ago, but have good momentum. So
15:19
Speaker 1: both of those tend to work on average, so do
15:21
Speaker 1: other things like high quality. High profitability tends to beat
15:24
Speaker 1: poor profitability, low beta, low wrist stocks tend to beat
15:28
Speaker 1: high risk stocks. To a quant You want to trade
15:31
Speaker 1: those very diversified in as many places, not just for
15:33
Speaker 1: individual I keep saying stocks, but I have to correct myself.
15:38
Speaker 1: Anything you can trade that's liquid and you have good
15:41
Speaker 1: data on which both makes it better investment because you
15:44
Speaker 1: can spread your bets more and makes you more confident.
15:48
Speaker 1: It's not just random data mining. So I want to
15:50
Speaker 1: go back to what you said, an out of sample set,
15:53
Speaker 1: and you didn't use the words mean reversion, but essentially
15:56
Speaker 1: what you're describing is using mathematics to analyze is any
16:00
Speaker 1: place that you can put your money to work and
16:03
Speaker 1: look to take advantage of that eventual mean reversion, meaning
16:08
Speaker 1: cheap stocks will eventually get back to be fairly priced,
16:11
Speaker 1: as will expensive stocks will eventually come back down to
16:14
Speaker 1: fair If you put the two together, I would readily
16:16
Speaker 1: agree that value investing is highly related to the idea
16:20
Speaker 1: of mean reversion. They're almost almost synonyms. Um. If you
16:24
Speaker 1: add momentum in there is just that. Again, there are
16:26
Speaker 1: more that we do. But if that's the second one, UM,
16:29
Speaker 1: we would say things without a doubt mean revert. But
16:32
Speaker 1: they get there eventually. They tend to keep going the
16:34
Speaker 1: same direction for at least a little while longer before
16:36
Speaker 1: they mean. Way to put that they overshoot. And just
16:40
Speaker 1: for the lay people listening, you mentioned out of sample
16:43
Speaker 1: set um. So if we're looking at tech stocks um,
16:48
Speaker 1: or we're looking at recent data, describe how you would
16:51
Speaker 1: use an out of sample set to confirm that a
16:54
Speaker 1: sample refers to the following problem. If you give me
16:56
Speaker 1: enough data or anyone good with computers and statistics, I
17:00
Speaker 1: will find you something that has worked in the past.
17:03
Speaker 1: It might be total gibberish. One of the most famous ones,
17:07
Speaker 1: UH is butter prices in Bangladesh help predict the SNP fight.
17:11
Speaker 1: I could be mangling that, but it's something like I
17:13
Speaker 1: recall that and the same thing with the decreased number
17:15
Speaker 1: of pirates and the the pirates decreased number of pirates
17:20
Speaker 1: and the increased in in measured temperatures. People say you
17:23
Speaker 1: could randomly find any two things. The Super Bowl effects
17:26
Speaker 1: another example by after the NFC, but not just the
17:28
Speaker 1: NFC or an old because they had adjusted for the
17:31
Speaker 1: Steelers who won annoyingly often. And if it's screwed up
17:34
Speaker 1: the rule. So there's no real cure for this, but
17:39
Speaker 1: there's there's some there's something you can do. You can
17:41
Speaker 1: go look somewhere you haven't looked yet. One great out
17:44
Speaker 1: of sample test. It's time and it's now been twenty
17:48
Speaker 1: five years since my dissertation. That's frightening to say out loud,
17:51
Speaker 1: and and the stuff is held up and that's a
17:53
Speaker 1: wonderful out of sample test. But when I'm as you
17:55
Speaker 1: said twenty nine at Goldman Sachs, a good career strategy
17:59
Speaker 1: was not first, we wait twenty five years and if
18:01
Speaker 1: it works, we pounce. We keep going right. So another
18:04
Speaker 1: thing you can do is say, well, if this logic
18:06
Speaker 1: works to pick stocks in the US, doesn't work in Europe,
18:09
Speaker 1: doesn't work in Japan. All right, If it works for
18:11
Speaker 1: stocks around the world, doesn't work for bond markets, doesn't
18:13
Speaker 1: work for commodities, doesn't work for currencies. So finding these
18:17
Speaker 1: things work for other things was both lucrative because you
18:20
Speaker 1: could do it in more places, but also very calming
18:24
Speaker 1: because it made you think that's a much smaller chance
18:27
Speaker 1: that you're just lucky. It's not random. There's actually a
18:30
Speaker 1: theme underneath. I'm Barry Ridhults. You're listening to Masters and
18:33
Speaker 1: Business on Bloomberg Radio. My guest today is Cliff Assness.
18:37
Speaker 1: He's founder and c i O of the most influential
18:41
Speaker 1: hedge fund in the world. Am I giving you a promotion?
18:43
Speaker 1: What did they call you? One of the most influential hedgefund?
18:46
Speaker 1: Take that? And the irony is you've been fairly critical
18:50
Speaker 1: of the hedge fund industry in general. Let's let's talk
18:54
Speaker 1: a little bit about that. What what's your beef with
18:56
Speaker 1: with hedge Well, first, I've been a schizophrenic on the
18:59
Speaker 1: hedge fund industry. You know, it's kind of like having
19:00
Speaker 1: a little brother, if other not that they're my little brother.
19:03
Speaker 1: But if they someone else picks on your little brother,
19:05
Speaker 1: you defend them, and if your little brothers having a
19:07
Speaker 1: good day, you punch him all. We started out in
19:11
Speaker 1: the year two thousand, this is fifteen years ago. We
19:13
Speaker 1: wrote a paper called do hedge funds hedge? When I
19:16
Speaker 1: was a younger whipper snapper, and I got yelled at
19:18
Speaker 1: by half the big names in hedge funds. Was both
19:21
Speaker 1: fun and scary. Uh. And we said there to net
19:24
Speaker 1: long the word hedge implies their hedging, and they were
19:28
Speaker 1: about forty percent net long stocks, meaning if you went
19:32
Speaker 1: long a dollar in short sixty cents, you're still exposed.
19:37
Speaker 1: That that might be a good idea for total returns,
19:40
Speaker 1: but you shouldn't pay two and twenty for that because
19:41
Speaker 1: you can get net long from Jack Bogel forty. They
19:45
Speaker 1: actually Vanguard, so you're referencing ran a big study after
19:50
Speaker 1: the o A crisis, and they found that a sixty
19:53
Speaker 1: forty unleveraged portfolio beat something like the hedge funds out
19:59
Speaker 1: there and once fees they want. Well on the little
20:03
Speaker 1: brother comment, I think that's mostly right, but I'm gonna
20:05
Speaker 1: take a little issue with that. Here's the criticism. I
20:07
Speaker 1: think hedge funds. UH do a lot of very good
20:10
Speaker 1: strategies that make economic sense and have evidence behind them.
20:14
Speaker 1: Forgetting fees and forgetting hedging, merger arbitrage lending capital after
20:18
Speaker 1: a situation has occurred. You have a liquidity risk of
20:20
Speaker 1: a deal blow up risk, you get paid for that.
20:22
Speaker 1: Convertible arbitrage is largely I think a liquidity premium trend
20:26
Speaker 1: following and managed futures. I'm a momentum part of what
20:29
Speaker 1: we do. We think it works. There are others good strategies.
20:33
Speaker 1: They do them partially not fully hedged, which is weird
20:36
Speaker 1: and adds to the price because effectively you're getting a
20:39
Speaker 1: party of return from an exposure. You should get more cheaply,
20:42
Speaker 1: and then they charge a ton for it. So this
20:44
Speaker 1: is self serving because it's kind of how we do it.
20:46
Speaker 1: But we think hedge fund should fully hedge and then
20:48
Speaker 1: charge less. We wrote this paper that's critical. Some of
20:53
Speaker 1: the criticisms of hedge funds. The worst ones are when
20:55
Speaker 1: they compare them to a stocks. Now their net long
21:00
Speaker 1: long term about forty of the stock market and geeks
21:03
Speaker 1: speak a point for beta. Stock market goes up ten
21:05
Speaker 1: percent a day, you expect four percent on your hedge funds,
21:08
Speaker 1: but if it goes down ten percent, you expect to
21:10
Speaker 1: only lose. So stocks is a bad comparison. In general.
21:16
Speaker 1: That guy who bet against Warren Buffett is discovering this um.
21:20
Speaker 1: It wasn't my bet, so I'm allowed to It's not
21:22
Speaker 1: ex posts. It could have been bad for Warren too
21:24
Speaker 1: if we had a bear market. It's just a a
21:28
Speaker 1: bad comparison. And in five six years of bull markets,
21:31
Speaker 1: it's a terrible comparison to hedge funds. Sixty forty is
21:34
Speaker 1: much better, but it's still about more long stocks than
21:38
Speaker 1: than hedge funds have been historically, even before the last
21:41
Speaker 1: few years. You can show this, and when the market
21:43
Speaker 1: goes up fifteen percent a year for five years, that's
21:46
Speaker 1: three percent a year of a drag. So I think
21:49
Speaker 1: it's a much better comparison. But if you do the
21:51
Speaker 1: actual dollar for dollar, you end up critical of a
21:54
Speaker 1: few things. They don't Actually this will sound weird, but
21:56
Speaker 1: take enough risk. We don't want risk, we want turn
22:00
Speaker 1: But that's how you get return is from them taking
22:02
Speaker 1: more risks. So after you headge out that that market exposure,
22:05
Speaker 1: they're not quite doing enough to really move your portfolio
22:08
Speaker 1: that much. But there has historically been in our dad
22:11
Speaker 1: to some excess return. So I called this a tepid defense. First,
22:15
Speaker 1: I've criticized him as being too not long, not a
22:17
Speaker 1: good enough deal. But then when others have made comparisons
22:21
Speaker 1: that UM one is not terrible, the stocks a terrible comparison,
22:26
Speaker 1: I think it's been too negative. I think the truth
22:29
Speaker 1: is there are good strategies UM they just don't do
22:32
Speaker 1: it at as a good enough deal, at at an
22:34
Speaker 1: aggressive enough way to make it a good deal for investing.
22:36
Speaker 1: So in. In In other words, and I know you don't
22:38
Speaker 1: like to pat yourself on the back, but I will
22:41
Speaker 1: so you that I love You looked at this and
22:45
Speaker 1: basically said, well, when we look at the world of
22:47
Speaker 1: hedge funds, they're they're not embracing risk relative to what
22:51
Speaker 1: their mandate is, and they're not fully hedging, and they're
22:54
Speaker 1: charging too much. And you said, we can hit all three,
22:58
Speaker 1: but check off all three boxes. We could really embrace
23:02
Speaker 1: appropriate measured risk in a way that makes sense, not
23:06
Speaker 1: kind of be a little bit pregnant, but really be
23:08
Speaker 1: fully in. We could fully hedge this position and instead
23:12
Speaker 1: of charging two plus of the profits, the Delta fund
23:16
Speaker 1: is one in ten um. By the way, could we
23:20
Speaker 1: have had this conversation a few years ago or would
23:23
Speaker 1: you have been prevented by uh the SEC rules back then.
23:27
Speaker 1: I have no idea. I know they once sent me
23:30
Speaker 1: on TV where I had not passed my Series seven.
23:33
Speaker 1: I shouldn't take too much time here, but I had
23:35
Speaker 1: passed it and it passed it in at Goldman expired.
23:39
Speaker 1: We weren't a broken dealer, so I and then we started.
23:42
Speaker 1: I had to retake it, so they told me, don't
23:44
Speaker 1: talk about fees. It's one of the things you don't
23:45
Speaker 1: have to talk about if you haven't passed the test.
23:48
Speaker 1: The anchor, of course, immediately asking me about fees, which
23:51
Speaker 1: which he was told not to, and I had to
23:54
Speaker 1: look like the most evasive guy the man. I I
23:57
Speaker 1: kind of said, we think the low versus the industry,
24:00
Speaker 1: and I kind of look down and prayed he didn't continue,
24:03
Speaker 1: which he didn't. It was not my high point of media.
24:05
Speaker 1: I'm Barry rid Helts. You're listening to Masters in Business
24:08
Speaker 1: on Bloomberg Radio. My guest today is Cliff Assness. We
24:12
Speaker 1: were speaking earlier about hedge funds and in general, how
24:15
Speaker 1: they don't necessarily hedge and why they charged too much,
24:19
Speaker 1: and h u r S Delta Funds charges one in ten,
24:23
Speaker 1: which actually got of all people Vanguard founders Jack Bogel
24:29
Speaker 1: saying that seems like a pretty reasonable deal. I'm impressed,
24:33
Speaker 1: quote unquote, I'm impressed. Well, that's tough to do to impressed. Now,
24:37
Speaker 1: I've I've known Jack for a while. He's kind um.
24:39
Speaker 1: The mutual fund version of this is called the multi
24:42
Speaker 1: strategy fund Jack. I actually asked him about this afterwards,
24:46
Speaker 1: because if Jack Bogel says something nice about your strategy,
24:49
Speaker 1: you want to be able to quote it, and we
24:51
Speaker 1: mutually agreed how I could quote him, and I'm I'm
24:53
Speaker 1: I'm very fond of this. UM. I did not convince
24:56
Speaker 1: Jack to invest and recommend hedge funds. Jack does like
25:00
Speaker 1: tilting away from market cap and long home. He doesn't
25:02
Speaker 1: like value. You know, he's straight down the line. That's
25:05
Speaker 1: a separate issue. But he's straight down the line. So
25:07
Speaker 1: I'm not that persuasive. But I got him to say
25:10
Speaker 1: that what we do in hedge funds, because of the
25:12
Speaker 1: transparency and the fee structure, is the hedge fund he
25:16
Speaker 1: hates least I am quite proud of. That's a fantastic quote.
25:22
Speaker 1: So let's let's talk a little bit about hedge funds
25:24
Speaker 1: in general, because it seems to be a very bifurcated industry.
25:30
Speaker 1: Most hedge funds seem to be really significantly underperforming and
25:35
Speaker 1: a handful of winners. Um not. I'm not just referring
25:39
Speaker 1: to some of your big firms funds that have done well,
25:42
Speaker 1: but a handful full of firms, not just Renaissance technologies.
25:47
Speaker 1: It's really a a it's not a Gaussian distribution, it's
25:51
Speaker 1: not a bell curved. It's it's a fat head and
25:55
Speaker 1: a long his seat is getting close in to my
25:57
Speaker 1: new It's a fat head with a long tail, and
26:00
Speaker 1: it's really just a handful of giant winners and everybody
26:03
Speaker 1: else are also runs also rans. Why is that the
26:08
Speaker 1: industry has gotten more like that over over time? Uh?
26:13
Speaker 1: Part of it, Part of it is for good reasons,
26:15
Speaker 1: part of it is for bad reasons. I do think
26:19
Speaker 1: if the market is not perfectly efficient and some people
26:21
Speaker 1: can beat it, it's still a pretty narrow group. It's
26:23
Speaker 1: still pretty efficient and a pretty narrow group of people
26:26
Speaker 1: who can beat it, So still a handful in that sense,
26:29
Speaker 1: it's not that crazy. I think there's a negative reason
26:32
Speaker 1: to that, UM that investors of all types of all stripes, institutional,
26:36
Speaker 1: high net worth, retail, chase performance too much. Um. You know,
26:41
Speaker 1: they chase short term performance and they chase multi year performance.
26:44
Speaker 1: And certainly that's indicative and all I'll sequel. I prefer
26:47
Speaker 1: it when mine is good. UM. I whine about this
26:50
Speaker 1: much more when it's going against me than when it's
26:52
Speaker 1: going for me. But I think people should spend a
26:54
Speaker 1: lot more time on the process. Whether it makes sense
26:57
Speaker 1: if they have a very long term track record. But
26:59
Speaker 1: I think people chase too much, and particularly people seem
27:02
Speaker 1: to look at three to five year horizons and chase them.
27:06
Speaker 1: And one of the few things I'm that we actually
27:07
Speaker 1: know about three to five year horizons and finance is
27:10
Speaker 1: that's about the horizon. Things tend to mean revert, not continue.
27:12
Speaker 1: That's a full side. That's about a cycle. Where in
27:15
Speaker 1: the mutual fun world, that's about when somebody ends up
27:19
Speaker 1: on the cover. Hedge funds aren't that different, and usually
27:22
Speaker 1: all the money plows into it just as they've peaked
27:24
Speaker 1: in a reverse. And there are some people who defy
27:26
Speaker 1: all the odds and just keep going. But some of
27:29
Speaker 1: the very large hedge funds are this effect doing well
27:32
Speaker 1: for a while, and then the money just pours in,
27:34
Speaker 1: and I'll say it, I'll say it, even when it's us,
27:36
Speaker 1: it's probably too much. So is there a dollar amount
27:40
Speaker 1: what you would say, Okay, No, we have done that
27:43
Speaker 1: to various strategies. We are blessed and having fairly large
27:46
Speaker 1: capacity as quants. We trade very diverse side portfolios, big
27:51
Speaker 1: cap stocks, UH countries, currencies. But we have closed some
27:56
Speaker 1: arbitrage strategies and mergers and converts. We've opened and closed them.
28:00
Speaker 1: We've closed them whenever the market wasn't amenable to more capital.
28:04
Speaker 1: We've traded. We've closed some I wouldn't call him high frequency,
28:07
Speaker 1: but but faster trading strategies because those end of hit
28:10
Speaker 1: capacity faster, and there is no strategy we wouldn't close
28:13
Speaker 1: at some point. Because there's no strategy is an obvious
28:18
Speaker 1: statement that's still upsets some people. There is no strategy
28:21
Speaker 1: that doesn't get worse past a certain minimum necessary point
28:25
Speaker 1: as it gets bigger. But we see that time and again.
28:27
Speaker 1: We see the mutual funds. We see in hedge funds
28:30
Speaker 1: there's a dollar amount that the manager can handle successfully,
28:34
Speaker 1: can regularly make money and beyond that, it looks like
28:37
Speaker 1: they're just gathering assets for the sake of gathering assets,
28:40
Speaker 1: and the performance goes away. I think it's very strategy specific.
28:44
Speaker 1: You've got to really grill your managers. If you are
28:47
Speaker 1: inclined to look for stock picking skill and you are
28:50
Speaker 1: thrilled with someone's track record, it's not that hard to
28:52
Speaker 1: figure out if it was mainly small cap and microcap
28:55
Speaker 1: stocks and they're way too big for that. Now, if
28:58
Speaker 1: they were a currency trader of any kind, and you
29:00
Speaker 1: don't have to be a quant but they were a
29:01
Speaker 1: currency trader, um, maybe it's not that crazy that they
29:05
Speaker 1: can take and it requires some thought, but I think
29:07
Speaker 1: it's one of the key things that should be asked.
29:09
Speaker 1: How you pass the point where you can manage The
29:11
Speaker 1: answer is gonna be very different for different people, but
29:13
Speaker 1: it's always a good question. So let's talk I'm gonna
29:16
Speaker 1: pivot a little bit. Let's talk about high frequency trading,
29:19
Speaker 1: because I know you use you You guys are not
29:21
Speaker 1: an h f T, but you certainly use a lot
29:24
Speaker 1: of um computer generated or algorithmically driven trading to be
29:29
Speaker 1: more productive, more efficient, lower cost for your execution. You're
29:33
Speaker 1: a pretty big defender of HFT. We're an s f T,
29:36
Speaker 1: a slothful frequency trader UM and and you know we've
29:41
Speaker 1: been I'll be honest, I've been trying to be very
29:43
Speaker 1: clear and careful about this. We have been a public
29:45
Speaker 1: defender of of high frequency trading and and we are quantitative.
29:49
Speaker 1: So it's easy to confuse those two and not. We're defenders.
29:52
Speaker 1: We don't think there's anything wrong with it, but we
29:53
Speaker 1: don't want people to think we're doing something we're we're not.
29:56
Speaker 1: We're not high frequency traders. It is my belief that
30:00
Speaker 1: that there's there's nothing perfect. I'm not excusing all behavior
30:03
Speaker 1: and saying nothing wrong goes on UM, but that's true
30:07
Speaker 1: of every industry, of every group. I'm not sure HFT
30:09
Speaker 1: is worse either. Most of what goes on in h
30:12
Speaker 1: f T, which also explains most of the behavior that
30:14
Speaker 1: scares people, is about making markets. They make most of
30:18
Speaker 1: their money. But if Barry wants to trade comes to
30:20
Speaker 1: the market, they will take the other side of Barry's trade. Uh,
30:23
Speaker 1: and then they will try to hedge that risk. And
30:25
Speaker 1: they will And and why do they have to trade
30:27
Speaker 1: high frequency? Well, they put out a bid in an offer,
30:30
Speaker 1: they'll buy it for somewhat less than they'll sell it
30:32
Speaker 1: to Barry. I'm going to use you as my example
30:34
Speaker 1: the way. Now, let's say the market moves a little bit.
30:36
Speaker 1: They gotta change those bids and offers, or else you
30:41
Speaker 1: or I could sneak in and get too good of
30:42
Speaker 1: a deal on them. They got to move it with
30:44
Speaker 1: the market. So they are canceling, correcting constantly. Why do
30:47
Speaker 1: they have to trade it near light speed? I still
30:49
Speaker 1: laughed at the speed of light has something to do
30:51
Speaker 1: with my industry. It shouldn't. We're not in. We're not in.
30:54
Speaker 1: Electrons can move down optic fiber cable from your office
30:59
Speaker 1: to the exchange. I geek equations, but that little c
31:01
Speaker 1: for the speed alike should not be in any finance equations.
31:04
Speaker 1: Yet it does show up occasionally. But most of their
31:07
Speaker 1: need for speed, and I sound a little too top
31:09
Speaker 1: gun there, but most of their need for speed comes
31:11
Speaker 1: from having to beat other high frequency traders to Yes,
31:15
Speaker 1: it is an arms race, but to be the one
31:17
Speaker 1: to execute Barry's trade. So by and large, and I
31:22
Speaker 1: think everyone almost everyone agrees with this. I shouldn't say everyone, um,
31:26
Speaker 1: but from Vanguard, Jack Bogel, other people have looked at
31:29
Speaker 1: this agree, they've lowered costs for investors. Um, there's always
31:34
Speaker 1: been market making has always been a middleman. We might
31:36
Speaker 1: all not love a middleman, but there's always been someone
31:38
Speaker 1: to do this. They do it cheaper than the more
31:41
Speaker 1: mob monopolistic specialists and old market makers used to do it.
31:45
Speaker 1: Um things that look like front running that scare people.
31:48
Speaker 1: Market makers used to do once someone starts to buy
31:50
Speaker 1: a lot. Unethical front running is you have some information
31:56
Speaker 1: that someone's about to trade that you shouldn't have. But
31:58
Speaker 1: observing trades and saying where there's something, that's probably something else,
32:02
Speaker 1: so we're gonna start to move the price that's gone
32:04
Speaker 1: on forever. It's just rational. Anyone making a market's going
32:06
Speaker 1: to do that. So it's not a blanket defense. But
32:09
Speaker 1: we think most of what they do is provide trading
32:12
Speaker 1: liquidity cheaper. The two big complaints that I think are credible.
32:16
Speaker 1: The first is what some people call packet sniffing. So
32:19
Speaker 1: it's not an order that's been executed, it's not a trade.
32:22
Speaker 1: It's cliff is sending an order to buy a hundred
32:25
Speaker 1: thousand x y z. They sniff that order out before
32:28
Speaker 1: it hits the exchange and jump in line in front
32:30
Speaker 1: of you and flip what you were about to buy
32:33
Speaker 1: to you anyway, but at half a sent higher. So
32:36
Speaker 1: that's one complaint. The other complaint is, you know, it's
32:40
Speaker 1: like the old joke about a banker is happy to
32:42
Speaker 1: lend you money as long as you don't need any
32:44
Speaker 1: They provide liquidly until it's necessary, and then hey, this
32:47
Speaker 1: is getting a little too hairy for us, will close.
32:49
Speaker 1: Either of those are are jokes. Those are quite serious
32:51
Speaker 1: and accurate statements. Um. But I will agree with you
32:55
Speaker 1: um on the first one, and I'll take a little
32:57
Speaker 1: issue with the second one about them running away. Um,
33:00
Speaker 1: when it comes to information they're not supposed to have.
33:03
Speaker 1: If anyone has private information that they're not supposed to have,
33:07
Speaker 1: that should be stopped. I think that's a relevantly tiny
33:09
Speaker 1: part of what goes on. And I think you know,
33:11
Speaker 1: traders have attempted to do stuff like this since time immemorial.
33:14
Speaker 1: There was probably someone on the outside of Rome, uh
33:18
Speaker 1: during this two thousand years ago. But if we find
33:21
Speaker 1: a place someone's finding information that that's private, that's using them,
33:26
Speaker 1: they're jumping ahead of me or you, that should be stopped. Well,
33:29
Speaker 1: I think that it's private it's that the exchange has
33:31
Speaker 1: the information, they're providing it to people who are paying
33:35
Speaker 1: for it. You get into a gray area when people
33:37
Speaker 1: are paying for it. If it's disclosed and they're paying
33:39
Speaker 1: for it, you could make a caveat emptor article that
33:42
Speaker 1: we all know we're being front run. And these these
33:45
Speaker 1: profits are are It's a competitive world. These guys are
33:48
Speaker 1: hyper competitive with each other, so they then try to
33:50
Speaker 1: undercut each other and providing more competitive liquidity, and you
33:53
Speaker 1: have dark pools and you have other things that it's
33:55
Speaker 1: no longer as profitable as it was. I am still
33:58
Speaker 1: perfectly fine. I would like to see it be volume terry,
34:00
Speaker 1: but if I were setting up in exchange, I probably
34:03
Speaker 1: would make that not allowed. I think the confidence would
34:05
Speaker 1: be larger than the gained to that. So I think
34:07
Speaker 1: it's a bad idea, though I could make a more
34:10
Speaker 1: uh you know, geeky economic argument that if it's disclosed,
34:13
Speaker 1: it's okay. I'd like to see that stopped. The other
34:17
Speaker 1: the the other part in them running away, I think
34:19
Speaker 1: they do run away. I'm not taking issue with your facts.
34:22
Speaker 1: I just think market makers have always run away. I
34:25
Speaker 1: think this fantasy that uh that in the old days
34:28
Speaker 1: they would take a loss for the team, ready willing
34:30
Speaker 1: and able to make a market and to provide stability
34:33
Speaker 1: regardless of market conditions. I don't think you will ever
34:36
Speaker 1: find a market maker in these kind of markets and
34:39
Speaker 1: these kind of trading who buys at a price they
34:41
Speaker 1: know is is above the equilibrium price, or sells at
34:44
Speaker 1: a price they know is the true price. Two out
34:47
Speaker 1: of duty, and I don't think it's any less than
34:49
Speaker 1: was before. I think they used to run away before.
34:51
Speaker 1: So I'm not saying they're wonderful. I'm saying they're precisest, cowardly,
34:54
Speaker 1: and venal as they've always been, and we shouldn't expect different.
34:58
Speaker 1: They're the same as it ever was. It's just technology
35:00
Speaker 1: instead of you. You can hang around a little bit,
35:03
Speaker 1: we can keep talking. For the podcast portion. I've been
35:07
Speaker 1: speaking with Cliff Astness. He's the founder and c i
35:09
Speaker 1: O of a q R. If you enjoy these conversations,
35:13
Speaker 1: be sure and check out our complete chat. This will
35:16
Speaker 1: go on for hours after this. You can find that
35:19
Speaker 1: on Bloomberg dot com, at SoundCloud, and of course at
35:22
Speaker 1: Apple iTunes. Be sure to check out my daily column
35:25
Speaker 1: on Bloomberg View. Follow me on Twitter. At rid Halts. Cliff,
35:30
Speaker 1: what's your Twitter handle? Uh? Sumarian? Uh. Someone go read
35:36
Speaker 1: old Conan comics and that's how it's how they spelled
35:38
Speaker 1: in the old cord. It's also just search for Cliff
35:41
Speaker 1: Astness and where you can find it. This is Barry Ridholts.
35:43
Speaker 1: You're listening to Masters in Business the podcast. This is
35:47
Speaker 1: this is my favorite part where I don't have to
35:49
Speaker 1: worry about I'm taking the year piece off. I don't
35:52
Speaker 1: have to worry about um the radio, the times we
35:55
Speaker 1: just kicked back. Cliff, thank you so much for coming by.
35:58
Speaker 1: We were off air, we would des i ibing. I
36:01
Speaker 1: was chasing you for a while and I figured I
36:03
Speaker 1: would wear you down and get you here eventually. Yeah,
36:05
Speaker 1: I am. I wasn't really avoiding you, but I must
36:08
Speaker 1: tell you having so many amazing people, many of which
36:12
Speaker 1: I consider people I study, wore me down and said
36:15
Speaker 1: I want to be part of this group. That that's
36:17
Speaker 1: the that was the trick, only sucking up I'm gonna
36:20
Speaker 1: be doing in this whole thing, and that I do.
36:22
Speaker 1: I enjoyed that the my rolodex is not bad, not
36:26
Speaker 1: the greatest rolodex, but not too bad. And I knew
36:30
Speaker 1: enough people between Michael Mobison, Lasla Borrini and a handful
36:35
Speaker 1: of guys like that, I knew that there would be
36:37
Speaker 1: enough critical mass that I would eventually start honing in
36:41
Speaker 1: on guys like yourself and Bill Gross. So it's really
36:46
Speaker 1: and Bob Schiller and Ray Dally and triangulating and yeah, no,
36:49
Speaker 1: since I just name drop Bob Schiller. You mentioned something
36:53
Speaker 1: in one of your papers, by the way, I've I've
36:56
Speaker 1: read and my head of research has read a lot
36:59
Speaker 1: of what you guys crank out and put out some
37:02
Speaker 1: really interesting pagers. You talk about being somewhere between Fama
37:08
Speaker 1: and Schiller, And the funny thing is you and I
37:10
Speaker 1: both wrote something after the two of them won the
37:13
Speaker 1: Nobel the difference being mine was this fluffy, eight hundred
37:16
Speaker 1: word piece and you put out a three thousand word
37:20
Speaker 1: dissertation on why they're both right and that's why they
37:24
Speaker 1: both want the um. Well, it was it was fun.
37:28
Speaker 1: I co wrote that with John lou Is, one of
37:30
Speaker 1: my founding partners, and I'm also both of us had
37:33
Speaker 1: Fama as uh co chairs of our dissertation, so we
37:36
Speaker 1: really we we had a similar um. You know, we've
37:39
Speaker 1: worked together forever, so our perspective is similar in that sense,
37:41
Speaker 1: but we also had a similar history, and I would
37:45
Speaker 1: say we started out as Fama's students and are incredibly
37:49
Speaker 1: schooled by him in that efficient markets way of of thinking.
37:53
Speaker 1: I wrote my dissertation early on on momentum trading, which
37:56
Speaker 1: is already a bit heretical for the afficient markets hypothesis. UH.
38:00
Speaker 1: Today this is big fight about why certain things have
38:05
Speaker 1: worked historically, like like cheap beating expensive um. One camp,
38:10
Speaker 1: the efficient markets farmer camp, says, well, if cheap winds,
38:14
Speaker 1: it's because it's risk here, you're assuming more risk. You're
38:16
Speaker 1: buying these small cap or these value stocks that have
38:19
Speaker 1: fallen out of favor. You assume the risk and therefore
38:22
Speaker 1: pays off with a better return over time. Another exactly right,
38:26
Speaker 1: another camp, and that would be consistent with an efficient market.
38:29
Speaker 1: You can make more if you take more risk. And
38:31
Speaker 1: that's not that's not inefficient. You're just right. You're you're
38:35
Speaker 1: at the hundred dollar table and the payout. The other camp,
38:38
Speaker 1: the inefficient, the irrational camp, as typified by Bob is
38:42
Speaker 1: sharing in the Nobel Prize. But it's certainly broader than
38:44
Speaker 1: than Bob is. And I'm just using an example of
38:47
Speaker 1: value cheap each expensive because people make errors. If something
38:51
Speaker 1: is doing poorly, they think it'll go on forever, and
38:54
Speaker 1: it should be. You should pay less for it, but
38:56
Speaker 1: not as much. This would be a terrible English sense,
38:58
Speaker 1: but not as much. Less as the actual price. It
39:01
Speaker 1: goes too far, and vice versa. Things that are are
39:03
Speaker 1: going well. Um and we saw lots of examples of
39:06
Speaker 1: that in two thousands. We saw stocks trading for less
39:10
Speaker 1: than book value, less than cash on hand. Things like that.
39:13
Speaker 1: I'll never forget along the small cap slash value thesis.
39:18
Speaker 1: There was a company I had traded earlier, micro Music.
39:21
Speaker 1: It run up to two hundred bucks. It collapsed. I
39:24
Speaker 1: want to say it about two forty three with three
39:26
Speaker 1: dollars cash on hand, debt free, profitable. How is this possible?
39:31
Speaker 1: You're waiting for this thing to go out of business
39:34
Speaker 1: eventually gets it goes up, gets taken up by IBM.
39:37
Speaker 1: We UM. We launched a q R in late um
39:42
Speaker 1: after very good RONNERD Goldman, and the first thing we did,
39:47
Speaker 1: and we didn't quanto it's very diversified, but implicitly did
39:50
Speaker 1: was fight the tech bubble. Um value investing did not
39:55
Speaker 1: have a very good say the least it had. Five
39:58
Speaker 1: years of the nineties were pretty toil, but ninety nine
40:00
Speaker 1: was a crescendo of doom um. And I don't mean
40:04
Speaker 1: to be melodramatic phrase, but it certainly felt that way.
40:08
Speaker 1: And again it's not all we do. For instance, momentum
40:11
Speaker 1: was good enough to offset value for much of the
40:13
Speaker 1: late nineties, not nine. Value was so bad. Um. So
40:17
Speaker 1: I I cut my teeth at least at my new firm,
40:19
Speaker 1: not not originally a goldman on fighting the tech bubble,
40:22
Speaker 1: and I still have the scars of that. A lot
40:24
Speaker 1: of battles since that. That was still the worst. So
40:26
Speaker 1: let's talk about the word bubble, because I could say, yeah,
40:29
Speaker 1: you you basically think everybody over you, well, no doubt
40:33
Speaker 1: the tech bubble. That was a genuine bubble. But since
40:37
Speaker 1: then you you've said, people over use the term, almost
40:40
Speaker 1: to the point of making it meaningless. This is a
40:42
Speaker 1: great way to explain how I'm in the middle of
40:45
Speaker 1: Bob Schiller and Jean Fauma. Jean will tell you, and
40:48
Speaker 1: I've recently had this conversation with him again, there there's
40:52
Speaker 1: nothing he'll look back on and say that was definitely
40:54
Speaker 1: a bubble. And he's very intellectually consistent. No matter how
40:57
Speaker 1: strong you you feel and how smart you think you are,
40:59
Speaker 1: you don't want argue with with with Gene He's it's
41:01
Speaker 1: pretty hard to beat. I still don't agree with him
41:03
Speaker 1: on this, but I would run from that and hide.
41:09
Speaker 1: But I do believe bubbles occur. Gene gene Um at
41:14
Speaker 1: least he might not go as far as say they
41:16
Speaker 1: don't recur. He'll say we can't really prove they've occurred.
41:18
Speaker 1: I think there was a definite bubble in tech stocks.
41:21
Speaker 1: I think, and I want to accuse Bob Shulder directly
41:23
Speaker 1: of this. I love Bob. I don't know what he
41:25
Speaker 1: he does, but I think people on the irrational side
41:29
Speaker 1: use the word bubble too much, and certainly on the
41:31
Speaker 1: wall street side, there's a bubble and everything. I I
41:34
Speaker 1: had a presentation where I had with I threw up
41:36
Speaker 1: like twenty headlines from bubbles and markets to bubbles and
41:38
Speaker 1: individual stocks too. We have dumbed down the word bubble.
41:42
Speaker 1: The definition I like a bubble is it's still subjective,
41:46
Speaker 1: but it's I can't come up with any plausible future
41:50
Speaker 1: scenario where this yields a return that's even remotely acceptable.
41:53
Speaker 1: It could be a low return. So the funny thing
41:56
Speaker 1: about that line of yours is, I you just did
42:00
Speaker 1: in a column headline the bubble in bubbles. Well, we're
42:03
Speaker 1: gonna be in coort over that um. But your definition
42:09
Speaker 1: of what a bubble is is there is just no rational,
42:13
Speaker 1: reasonable price in the future based on what you paid.
42:16
Speaker 1: And this was real time, and we have the papers
42:18
Speaker 1: to show it. During the tech bubble, we tried very
42:20
Speaker 1: hard to say, could we be wrong about this? What
42:22
Speaker 1: if you assumed the high end of Woltere earning scores,
42:25
Speaker 1: which was insane assumptions at the time. They weren't gonna happen,
42:28
Speaker 1: But assume them. Assume people are willing to accept less
42:31
Speaker 1: on stocks than the past, because the less you'll accept
42:34
Speaker 1: in return, the more you can pay in price. That
42:36
Speaker 1: was counterintuitive because everyone's assuming it would go on forever.
42:39
Speaker 1: Let's assume it's rational in a in a in a
42:42
Speaker 1: fish in markets world, we couldn't get close to the
42:44
Speaker 1: current price. I was willing to call that a bubble.
42:47
Speaker 1: I wrote, um, something I called a partial book draft
42:50
Speaker 1: that never you asked me earlier, why I've never in
42:52
Speaker 1: the book? I tried once? Uh, you don't want to
42:55
Speaker 1: write a book about something being a bubble and have
42:58
Speaker 1: it start to crash around you. I kept revising the
43:00
Speaker 1: book for about six months, and then I decided I'd
43:03
Speaker 1: rather make money than finish the book. And no one
43:06
Speaker 1: wants a book. Hey, there was a bubble and I
43:07
Speaker 1: called it, but I forgot to tell you real time. Um,
43:10
Speaker 1: but I was, I was writing this thing. I wrote
43:12
Speaker 1: articles on it that thank god, we're we're out there,
43:14
Speaker 1: but that i'd call a bubble. Let me give you
43:16
Speaker 1: a current example of something that I think is a
43:19
Speaker 1: very expensive market, but people call a bubble all the time.
43:22
Speaker 1: Is the bond market? Bond really yields using forecasts of inflation,
43:27
Speaker 1: talking about US bonds are perilously close to zero. They
43:31
Speaker 1: bounce around. They've been that is about as low as
43:35
Speaker 1: they've ever been several times in history. That's pretty bad.
43:39
Speaker 1: Zero is pretty bad. Can I come up with a
43:42
Speaker 1: scenario where these bonds do okay over the next ten years? Well,
43:47
Speaker 1: I don't call this a plausible scenario. Can I come
43:50
Speaker 1: up with is not my best guess, just just something
43:53
Speaker 1: that's not wholly ridiculous. And you can come up with
43:56
Speaker 1: it with one word, and you know the word Japan.
43:58
Speaker 1: You knew it, and now you have harmony yielding almost
44:02
Speaker 1: briefly yielding lessons by no means. And and just to reiterate,
44:05
Speaker 1: am I predicting the US turns into Japan? From pretty
44:08
Speaker 1: much still now? Um? Maybe ninety to two thousand at
44:11
Speaker 1: the worst. Um, But is it possible? Yeah, So I
44:15
Speaker 1: am willing to say it's a quite expensive market versus history.
44:18
Speaker 1: That's a different statement. Bubble has a level of assurance,
44:20
Speaker 1: it has a level of insanity. It is a level
44:22
Speaker 1: of you should go out and short this thing. The
44:24
Speaker 1: other problem with bubble is much more practical, and this
44:27
Speaker 1: applies even when they're real bubbles. To the tech bubble,
44:29
Speaker 1: timing is always an issue. People forget again. Bob Shill
44:33
Speaker 1: has done tremendous work, but he started saying it at
44:36
Speaker 1: least um a rational juberance was more or less his
44:42
Speaker 1: phrase that the Greenspan said it tanked the market and
44:47
Speaker 1: then had to retreat from it. I think that was
44:49
Speaker 1: quietly Bob's fault, which I would find so much fun
44:51
Speaker 1: if I were Bob. Congressional testimony tanks the market. But
44:56
Speaker 1: and this is notrefore proving the market is completely around.
44:59
Speaker 1: This is not a knock on Bob, because he'd be
45:00
Speaker 1: the first to tell you not to use his stuff
45:02
Speaker 1: to time the market and actively trade. But if you
45:06
Speaker 1: did from when he first started saying it, I'm not
45:09
Speaker 1: I don't think you made money. I think round trip,
45:11
Speaker 1: we don't think we ever got back to so we
45:14
Speaker 1: got lower returns than normal. Where his measures very good
45:16
Speaker 1: for forecasting, but to actually make money by being an
45:20
Speaker 1: active trade or you don't want to use the Schiller
45:22
Speaker 1: p with a ten year horizon. The Cape measure, yeah,
45:26
Speaker 1: which I love. If you look at the past thirty years,
45:29
Speaker 1: CAPE has been showing over valued market for something like
45:34
Speaker 1: the time, which you know that I think I'll trust
45:37
Speaker 1: your numbers. It's certainly something like that. I find that
45:41
Speaker 1: to be a little less of a knock. These things
45:43
Speaker 1: can wander away from normal for so long that it
45:47
Speaker 1: does impress upon people. While you don't want to trade
45:50
Speaker 1: over this, even over a even perhaps over a third
45:52
Speaker 1: year horizon, I have if I ever find an investor
45:55
Speaker 1: will give me a fifty year lock up, maybe I'll
45:57
Speaker 1: use the CAPE to to try to forecast things. And
45:59
Speaker 1: if you've been thinking about it, because you mentioned something
46:01
Speaker 1: earlier about this um, if you look at modern society
46:06
Speaker 1: and how much more productive and efficient. I'm trying to
46:09
Speaker 1: remember where I I'm pulling this from something you you wrote,
46:12
Speaker 1: but I'm I'm not remembering which paper this was. Uh,
46:15
Speaker 1: it might have been something more informal than a white paper.
46:19
Speaker 1: But look at at the lack of capital intensive heavy industries.
46:23
Speaker 1: Think about manufacturing and what used to go into stamping
46:27
Speaker 1: out locomotives versus hey, we're we're creating a software company.
46:33
Speaker 1: Look at uber with no hard costs other than some
46:37
Speaker 1: code and some serverses. But that becomes you know, that
46:42
Speaker 1: becomes a small cost of business as opposed to having
46:45
Speaker 1: to build these giants, steel mills and these you know
46:48
Speaker 1: that that sort of stuff. So maybe to some small degree,
46:52
Speaker 1: how more productive and efficient and less capital intensive these
46:55
Speaker 1: companies are. Maybe that rationalizes somewhat of a higher return
47:01
Speaker 1: at at at a higher pe, but that's really just
47:04
Speaker 1: a small part. It's certainly possible. The the argument I
47:07
Speaker 1: like best um is that that people required a much
47:13
Speaker 1: higher return prior to call the last thirty years because
47:18
Speaker 1: some behavioral and some structural structural as it was far
47:21
Speaker 1: costlier town stocks. You know, we all act like there
47:24
Speaker 1: was these Vanguard, Jack Bogel ten basis point index funds
47:27
Speaker 1: forever when they were the way to own stocks, and
47:31
Speaker 1: and this is the behavioral part, was generally far more
47:33
Speaker 1: concentrated portfolios. I don't think many people before this called
47:37
Speaker 1: the seventies owned the index. We look at it as
47:40
Speaker 1: if they did, but they tend to on the tent stocks.
47:42
Speaker 1: They're brokers recommended a huge that was insane, but at
47:47
Speaker 1: large cost. Concentrated portfolio, so riskier, we're to lower average
47:51
Speaker 1: return because the costs are much bigger trading through a
47:53
Speaker 1: broker at the old Brockdge costs before they changed the feast.
47:56
Speaker 1: So we look at the old returns like people got
47:58
Speaker 1: those from Bogel when they weren't. They were getting it
48:00
Speaker 1: from their stockbroker with more risk. So along the lines
48:04
Speaker 1: what you said, and I'd build on that, I'm not
48:05
Speaker 1: I'm not dismissing that. I'd say in the in the
48:08
Speaker 1: history we look at of stock returns, perhaps people required
48:11
Speaker 1: a much higher gross level because they didn't get all
48:14
Speaker 1: of it, They got much less of it, and it
48:16
Speaker 1: had to take on more risk to get it because
48:18
Speaker 1: their portfolios were more concentrated. So there is a case
48:21
Speaker 1: that it's a little more pessimistic than your case, a
48:24
Speaker 1: case that justifies higher prices today, but it also comes
48:28
Speaker 1: with a lower expected return. The lower expected turn is
48:30
Speaker 1: not wrong when prices are higher, returns are lower in
48:33
Speaker 1: this world. But it's rational. So let's get paid too much.
48:36
Speaker 1: You don't need to get paid that much. Let's talk
48:38
Speaker 1: about that, because you've said recently you're in the camp
48:42
Speaker 1: that anyone looking out ten years should rationally expect lower
48:46
Speaker 1: than average returns going far. Yeah, I'll be specific um
48:50
Speaker 1: without mean reversion in prices um. And because that's too
48:52
Speaker 1: much about forecasting some people. I mean, you know, I
48:55
Speaker 1: probably believe in a little mean reversion um in p
48:58
Speaker 1: s in real bond yields, but that's forecasting and guests work.
49:03
Speaker 1: Just you're you're not a big fan of forecast. I try.
49:06
Speaker 1: I mean, you know, you implicitly forecast a lot of things.
49:09
Speaker 1: But if I forecast, we want to forecast two thousand
49:11
Speaker 1: things and take tiny bets on all of them. I'm
49:14
Speaker 1: not especially not a fan of trying to forecast big
49:16
Speaker 1: giant things. Occasionally, something like the tech bubble will force
49:19
Speaker 1: me into a corner where there's no other bet to make.
49:21
Speaker 1: Either bet it goes up or down. But by and
49:24
Speaker 1: large we try to diversify, and and and if we
49:26
Speaker 1: do have to forecast, forecast, forecast as cowardly as possible.
49:31
Speaker 1: But right now Schiller pas are around UM without mean reversion.
49:37
Speaker 1: I mean to get about a four percent real return
49:39
Speaker 1: historically on stocks from here UM per example year, not
49:43
Speaker 1: including dividends, No include total return. Excuse me, real return
49:49
Speaker 1: over inflation that thank god you asked, because that's really
49:54
Speaker 1: including dividends. And after taking off one or two percent
49:58
Speaker 1: for inflation one or two now knowst long term, if
50:01
Speaker 1: inflation goes up, this goes up because not because earnings
50:03
Speaker 1: growth will move with inflation, we believe it makes some
50:08
Speaker 1: On a nominal basis, stocks are a great return, great investment.
50:11
Speaker 1: In a period of high inflation people all the time,
50:15
Speaker 1: surprise inflation tends to hit all assets, including stocks, But
50:19
Speaker 1: long term, steady state inflation is kind of what stocks
50:21
Speaker 1: are for UM Now bonds uh it bounces around call
50:26
Speaker 1: him fifty basis points tiny real real expected yield. So
50:31
Speaker 1: take your classic sixty forty portfolio six of four percent
50:36
Speaker 1: or four hundred basis points of fifty rounded, it's about
50:40
Speaker 1: two and a half percent real to forty and it's
50:43
Speaker 1: a bit above two and a half. It's not right.
50:45
Speaker 1: Did I mentioned quantas in my title? I can't do
50:47
Speaker 1: math in front of people. It's really so six times
50:51
Speaker 1: four is two four times where he's another twenty. Um,
50:57
Speaker 1: I liked a round teach Cliff a little bit, a
51:01
Speaker 1: little bit. But but but but I but I could
51:03
Speaker 1: take the derivative of it in a heartbeat. Um. The
51:08
Speaker 1: it's fun to have kids who are ten and eleven,
51:10
Speaker 1: by the way, because I'm trying to teach them division. Um,
51:13
Speaker 1: it's it's it's very humbling. Your kids are completely unimpressed
51:16
Speaker 1: that you're a quant of any kind of Like Dad,
51:18
Speaker 1: you're doing it wrong. That's not how I see the
51:20
Speaker 1: way they do it now. It is so different than
51:22
Speaker 1: the way we learned. I know. And the first thing
51:23
Speaker 1: I have to do is go on the web and
51:24
Speaker 1: figure out how they're teaching it now. Well, everything's gonn
51:28
Speaker 1: wrong to you and I but I gotta really it's
51:31
Speaker 1: like it's it's a lot of work. I got I'm
51:32
Speaker 1: learning relearning division. But that's not the Let me get
51:35
Speaker 1: back to it. It's two and a percent reel on
51:38
Speaker 1: rough the other numbers are all around it too, so
51:41
Speaker 1: we don't have forget this exact anyway. Long term, again
51:45
Speaker 1: rounding you made about five percent reel on sixty for
51:47
Speaker 1: about a hundred years, so we would forcus you make
51:50
Speaker 1: half the real return as you've made historically. Stocks are
51:54
Speaker 1: priced more expensively again Schiller P or dividend yield plus
51:57
Speaker 1: expected growth, whatever model you like, call it roughly about
52:01
Speaker 1: more expensive than about of the last hundred years at
52:05
Speaker 1: the time. Bonds this shocks some people are really not
52:09
Speaker 1: worse than stocks. They are more expensive than about nine
52:11
Speaker 1: at the time. The difference teen stocks and bonds. Bonds
52:14
Speaker 1: are bumping against zero, so they're more dramatic looking, but
52:17
Speaker 1: that difference in in spread is roughly average. But they're
52:21
Speaker 1: both really low. They usually don't happen at the same time.
52:27
Speaker 1: Stinks another bad being bad stinks to really and now
52:34
Speaker 1: I'm not being quantitative at all, really really stinks. We
52:37
Speaker 1: find forty portfolio is approximately as bad as it's ever
52:40
Speaker 1: been perspectively two and a half as bad as it's
52:46
Speaker 1: And by the way, people, the funny thing is with
52:48
Speaker 1: the math of this. People assume zero is as low
52:51
Speaker 1: as you can go. But if you pay attention you'll
52:54
Speaker 1: you'll know you can actually have negative no, not even
52:57
Speaker 1: real returns, negative nominal resent realized returns. Anything can happen
53:02
Speaker 1: even over longer periods, and people, many people think, um,
53:06
Speaker 1: I agree with that. If we ever had negative expected returns,
53:11
Speaker 1: that would be I'd be willing to use the word
53:13
Speaker 1: bubble there. We talked about that earlier because negative expect
53:16
Speaker 1: real returns were negative expected real returns very very hard
53:21
Speaker 1: for me to imagine a world where people would rationally
53:23
Speaker 1: on stocks and bonds, say, and we've seen some negative
53:26
Speaker 1: nominal returns on bonds. Those are places that have convenience yield,
53:31
Speaker 1: meaning keep my money protected. Or maybe they're worried about
53:34
Speaker 1: deflation and they're actually expecting positive real returns because you know,
53:37
Speaker 1: if deflation comes in a small negative nominal negative one
53:43
Speaker 1: percent of inflations minus two percent, that's still one percent
53:46
Speaker 1: better than inflation. But if you on your diversified broadly
53:50
Speaker 1: stocks and bonds portfolio, if instead of two and a half,
53:53
Speaker 1: which is disastrous versus history, we're expecting zero negative, I'd
53:57
Speaker 1: have to start, Uh there, I'd be screaming bubble here,
54:00
Speaker 1: I'm I'm screaming more expensive than history. Lower your expectations,
54:05
Speaker 1: which isn't the same as a bubble. Let me shift
54:06
Speaker 1: gears a little bit about with you and talk about
54:10
Speaker 1: um the small cap premium. I love the title of
54:14
Speaker 1: your paper. We got channel channeling Seinfeld a little bit.
54:18
Speaker 1: It's real and it's spectacular on the small firm effect.
54:22
Speaker 1: That was my blog entry. The title was even worse.
54:25
Speaker 1: What was the blog That was the blog entry title.
54:28
Speaker 1: The title of the paper was size Matters if you
54:31
Speaker 1: control your junk. It was a bit of a double end,
54:34
Speaker 1: which I've been apologizing for though. Well, well, the theme
54:38
Speaker 1: of the paper was people sort of downplay. So first
54:41
Speaker 1: let's back up. The small cap premium is by small
54:45
Speaker 1: cap stocks over time, and you'll outperform big cap stocks.
54:49
Speaker 1: Now that seems some people have been questioning that as
54:52
Speaker 1: that shrunk. But your your paper says, well, there's so
54:56
Speaker 1: much bad stocks. The SMB five, you know, get the
55:00
Speaker 1: same sort of junk filtering into that. So now when
55:03
Speaker 1: we look at the two thousand small cap stock, pull
55:06
Speaker 1: out the junkie lower quality stocks, and what do you
55:09
Speaker 1: have left? You're exactly right. You needed to do do the
55:12
Speaker 1: research and let me take people a little bit back.
55:14
Speaker 1: This is was the first crack in the armor of
55:16
Speaker 1: the famous capital asset pricing model. Back in the and
55:19
Speaker 1: efficient markets if you will UM back in the early eighties,
55:23
Speaker 1: a guy named Ralph Bonds, who was a former student,
55:25
Speaker 1: also found the first version that I know of. There
55:28
Speaker 1: might have been others of the small firm effect that
55:30
Speaker 1: after adjusting for the famous cap EM beta, small stocks
55:35
Speaker 1: beat large stocks, and you know, why should that be? Uh?
55:38
Speaker 1: And they've been all kinds of theories about this. CAPEM
55:43
Speaker 1: tries to adjust for that with beta. Some of the
55:45
Speaker 1: early studies said, maybe we're measuring risks wrong. Maybe the
55:48
Speaker 1: beta is really higher. That got you a little bit,
55:50
Speaker 1: not much, Maybe it's a liquidity premium. There are stories,
55:54
Speaker 1: but basically there's one thing. I don't want to confuse
55:57
Speaker 1: it with small value stocks. Buy cheap small stocks, at
56:02
Speaker 1: least in the data that's unassailable. UM. You know, going forward,
56:06
Speaker 1: as always, will history repeat? I believe it will there,
56:08
Speaker 1: but I can't prove it. You mentioned David Booth Dimensional Funds,
56:12
Speaker 1: which now runs if anything, that's that's that's the core
56:17
Speaker 1: premise of their's and I'm I am certainly believer in that.
56:21
Speaker 1: The small firm effect, though, is exactly what it says
56:23
Speaker 1: it doesn't buy small cheap, it buys all small stocks.
56:26
Speaker 1: Uh and should they beat large stocks. Well, that's far
56:29
Speaker 1: weaker than many of the other so called anomalies, the
56:32
Speaker 1: findings of value momentum, a few others in in in
56:36
Speaker 1: in the finance literature. It's kind of the weak sibling
56:39
Speaker 1: to those immediately went on a bad fifteen twenty year
56:42
Speaker 1: run after it was discovered. Has come back somewhat since then,
56:45
Speaker 1: but if you look at it through time, has other quirks.
56:47
Speaker 1: Almost all happens in January. That's not necessarily terrible, but
56:50
Speaker 1: it's a little weird. Confidence a little bit, even if
56:55
Speaker 1: you don't really understand why. Uh and in general wasn't
56:57
Speaker 1: nearly as strong as the other as the other fall value,
57:00
Speaker 1: as small value certainly, as as as momentum, small momentum,
57:04
Speaker 1: whatever you want to do. Most of the rest of
57:06
Speaker 1: empirical finance was better than the small firm effect. It
57:09
Speaker 1: was a weak and people have been losing faith in it.
57:12
Speaker 1: We discovered irrelatively recent research by US and others totally
57:16
Speaker 1: separate from this, looks at quality investing. This son Warren
57:20
Speaker 1: Buffett figured out thirty or forty years ago. It took us.
57:22
Speaker 1: Took us a little while to get to it. But
57:24
Speaker 1: what's what's this war is onto something? Well, we do
57:27
Speaker 1: it much geekier than he does, of course, and we're
57:29
Speaker 1: doing in a diversified way. He does it by picking
57:32
Speaker 1: the right quality, and he also cares about value and
57:35
Speaker 1: other things. But what's the quality stock? It's anything that
57:37
Speaker 1: you in not just theory, but in in intuition, anything
57:41
Speaker 1: that makes sense that you pay more for. Why would
57:43
Speaker 1: you pay more for stock? Well, if it's more profitable,
57:46
Speaker 1: if those profits are growing faster, and and you believe
57:49
Speaker 1: by the way this will continue, and you can show
57:51
Speaker 1: that that a profitable company tends to be profitable next
57:54
Speaker 1: quarter or whatnot, you should pay more for these. You
57:55
Speaker 1: should pay more for a lower risk company. All else
57:58
Speaker 1: equal if it makes as moch money now son as
58:01
Speaker 1: they don't of course, but if it makes as much money.
58:03
Speaker 1: We love low risk. I'll pay a little bit more
58:05
Speaker 1: if I have less risk for the same for the
58:07
Speaker 1: same money. And finally, if they're able to pay you
58:09
Speaker 1: a bigger dividend, while everything else is equal, growing the
58:13
Speaker 1: same same profitability, same same risk, why not more more
58:16
Speaker 1: dividends are good? These are all things that we call quality.
58:19
Speaker 1: We have a separate work and we're not the only
58:21
Speaker 1: ones to look at this, but we we of course
58:23
Speaker 1: love our version that looks at these things and goes
58:25
Speaker 1: all of these things seem to have an unexplained out performance.
58:30
Speaker 1: And you can get in your old debate as this
58:31
Speaker 1: risk is it is it inefficient markets? But we think
58:33
Speaker 1: it's very statistically valid. If you go look at other countries,
58:37
Speaker 1: it shows up again and again and again high quality
58:39
Speaker 1: beats low quality. Then we we decided to take a
58:43
Speaker 1: look at how does what does this mean for small
58:45
Speaker 1: and we noticed something that kind of jumped out at us.
58:48
Speaker 1: The small universe was very low quality, what we often
58:52
Speaker 1: called junkie. Something you fire universe to small capitalized. There
58:55
Speaker 1: are high quality small cap, but it had far more
58:58
Speaker 1: junk in it. And you said this early here um
59:01
Speaker 1: then as a percentage of the index than the large
59:04
Speaker 1: cap And I think it's fairly intuitive. But but it
59:08
Speaker 1: turns out that small was being seriously hurt if you believe.
59:13
Speaker 1: Not everyone believes it, but we believe it strongly. That
59:15
Speaker 1: high quality has and will win over over the past.
59:19
Speaker 1: High profitability is my favorite of them. That profitable firms
59:23
Speaker 1: uh will outperform for either risk or inefficient market reasons.
59:27
Speaker 1: Go look at small firms. You're shorting that effect. You're
59:30
Speaker 1: betting against it. Small firms are less profitable, far more
59:33
Speaker 1: junkie unprofitable firms, so you're betting against something that you
59:37
Speaker 1: think works. One thing that academics and quant and applied
59:41
Speaker 1: quants are pretty good at doing is saying, what if
59:43
Speaker 1: we remove that, What if we bet on small, but
59:46
Speaker 1: we make it so their average quality. Take what we
59:49
Speaker 1: know that's good, remove what we know is bad, and
59:52
Speaker 1: don't cheat. Don't only buy quality, but make it just
59:54
Speaker 1: average quality. So so small on net doesn't have a
59:57
Speaker 1: tilt towards high quality or junk, so it's just neutral
1:00:00
Speaker 1: on It turns out we've restored the small farm effect.
1:00:03
Speaker 1: Small crushes large does really well and about comparable to
1:00:09
Speaker 1: the value and momentum effects. We've restored it, we think
1:00:11
Speaker 1: to kind of equal standing with some of the other
1:00:13
Speaker 1: major findings in finance. We've also confused everyone, including ourselves,
1:00:18
Speaker 1: because we don't have a great economic story for this.
1:00:21
Speaker 1: It is too big of a premium. I don't yet,
1:00:23
Speaker 1: I like I'd like to start with gene Fama stories
1:00:26
Speaker 1: with efficient market stories and only be willing to go
1:00:28
Speaker 1: to inefficient markets if I fail, and small winning is
1:00:33
Speaker 1: one of the ones. Some people find it very intuitive,
1:00:35
Speaker 1: But when you ask them, why is it intuitive? Well,
1:00:37
Speaker 1: they're less liquid? Well then how come the higher quality
1:00:39
Speaker 1: ones when more consistently they're they're a little bit more
1:00:42
Speaker 1: liquid um. It's hard to come up with a great
1:00:45
Speaker 1: economic story why why why small winds value? I think
1:00:49
Speaker 1: it's quite easy. Whether you like risk or inefficiency momentum,
1:00:53
Speaker 1: you can tell pretty simple stories about people under reacting
1:00:56
Speaker 1: information small. If two small companies merge, do they suddenly
1:00:59
Speaker 1: go away up in price because now they're a big
1:01:01
Speaker 1: company and have a lower cost of capital, lower expect
1:01:04
Speaker 1: to return and a higher price. They don't seem to
1:01:06
Speaker 1: do that. They're weird things that go on. Why small
1:01:09
Speaker 1: works we have not helped, but we have helped for
1:01:11
Speaker 1: store to. It's kind of so you don't have you
1:01:13
Speaker 1: don't have an you don't have a narrative that explains
1:01:16
Speaker 1: the data as to why small minus junk trounce is large.
1:01:23
Speaker 1: There's not a narrow I really hate to say no,
1:01:25
Speaker 1: but I gotta go with not yet, let me be
1:01:27
Speaker 1: more optimistic and yet and it's nothing as simple as well.
1:01:31
Speaker 1: Big caps run into the law of big numbers. Small
1:01:34
Speaker 1: has huge head room and there's a lot of place
1:01:36
Speaker 1: for them to go as more and more people discover them,
1:01:39
Speaker 1: find them, and they grow and side Verry. That could
1:01:40
Speaker 1: absolutely be the answer, but that's a very inefficient With
1:01:43
Speaker 1: any other issues, you can say no, no, no, no
1:01:46
Speaker 1: on after you get that could be the answer. You
1:01:51
Speaker 1: jumped on the credit a little too fast because I said,
1:01:56
Speaker 1: I like to start to try to figure out a
1:01:58
Speaker 1: risk story in a markets stories are quite simple to
1:02:02
Speaker 1: come up with. Why people people are people under But
1:02:05
Speaker 1: these these these things they don't appreciate the upside. Inefficient
1:02:09
Speaker 1: market stories which I'm not quite willing to go there yet,
1:02:12
Speaker 1: but those are easy to come up with. Um they
1:02:14
Speaker 1: these are neglected people just don't pay as much attention,
1:02:17
Speaker 1: no Wall Street coverage whatsoever. They're unknown. There are a
1:02:21
Speaker 1: host of stories like that. I like to get there
1:02:23
Speaker 1: after I've exhausted all possibilities. I don't feel we have
1:02:26
Speaker 1: I have not found yet. This is the part A
1:02:29
Speaker 1: risk based story, a rational story, all those stories, when
1:02:33
Speaker 1: you say they grow faster, why don't the market recognize that?
1:02:36
Speaker 1: Barry market? If an efficient market the market should recognize
1:02:39
Speaker 1: exactly what you said, that it has upside and large
1:02:41
Speaker 1: cap does. There's not a lot of coverage. Remember, institutions
1:02:45
Speaker 1: have certain rules about what they can and can't buy.
1:02:48
Speaker 1: There's a hundred little stories. I'm completely I bet you
1:02:51
Speaker 1: know all of that. I am completely with you. And
1:02:53
Speaker 1: and at this point I have to say my money
1:02:55
Speaker 1: would be on some version of that being right because
1:02:57
Speaker 1: I haven't come up with the others. But they are
1:02:59
Speaker 1: in the inefficient markets irrationality camp. So so this conversation
1:03:04
Speaker 1: about small cap and value, UM, small value could be
1:03:08
Speaker 1: risk Let me throw that in because value can be
1:03:10
Speaker 1: very risky. But go on, But you you triggered two
1:03:12
Speaker 1: things I want to not forget to ask you. One
1:03:16
Speaker 1: is about smart beta and factor investing, and the second
1:03:20
Speaker 1: is what happens when you take a quantitative approach to
1:03:23
Speaker 1: warn Buffett. So where do you want to go with that?
1:03:26
Speaker 1: Let me start with with smart beta. UM. You and
1:03:28
Speaker 1: I talked about hedge funds earlier, and I gave you
1:03:30
Speaker 1: a schizophrenic answer. I'm gonna give you another one. UM.
1:03:33
Speaker 1: I am both a fan of smart beta and its
1:03:36
Speaker 1: most famous version, fundamental indexing. UH and UM A skeptic
1:03:41
Speaker 1: in a in a very narrow sense. Now before before
1:03:43
Speaker 1: you get into too much detail, I know you're friendly
1:03:46
Speaker 1: with Rob. Are not right. We're also friendly with And
1:03:50
Speaker 1: he was here not too long ago. Some people have
1:03:54
Speaker 1: called him the father of fundamental indexing. Um. I don't
1:03:58
Speaker 1: know if that's overstating it'll little bit um. I don't
1:04:01
Speaker 1: think it's overstating it. I I do think and and
1:04:03
Speaker 1: and and Rob's an honest guy. He'll he'll tell you this.
1:04:06
Speaker 1: People were doing some fundamental indusseries. Uh, there's some people
1:04:09
Speaker 1: of Goldman sacks. But the father of something is an
1:04:13
Speaker 1: early adapter who's the one most responsible for its popularity
1:04:16
Speaker 1: by far. And my disagreements with Rob and we we
1:04:21
Speaker 1: are friends, and we are co authors, have been intellectual,
1:04:24
Speaker 1: never about whether they would outperform Um. I believe, and
1:04:28
Speaker 1: I think the math is more of a proof than
1:04:30
Speaker 1: a belief. I'll go far on this one. That fundamental
1:04:33
Speaker 1: indexing versus the market is a rather clear, straightforward tilt
1:04:38
Speaker 1: towards value. If you build a fundament. Hold on, let
1:04:41
Speaker 1: me stop you there, because I want to break this
1:04:42
Speaker 1: down so so people who are listening to understand what
1:04:45
Speaker 1: this is. You take the SMP five hundred. It's a
1:04:48
Speaker 1: market capitalization waiting, which Rob points out and Jim O'Shaughnessy
1:04:53
Speaker 1: also points out that at the end of a cycle,
1:04:55
Speaker 1: everybody piles in and you end up with this wildly
1:04:59
Speaker 1: disproportion in it handful of stocks um attracting all the
1:05:04
Speaker 1: assets in the SMP five hundred. We saw it, we
1:05:07
Speaker 1: saw it again um in oh seven oh eight. But
1:05:10
Speaker 1: towards the end of that cycle, half a dozen to
1:05:14
Speaker 1: a dozen stocks are driving the vast majority of the
1:05:17
Speaker 1: gains in the index. And so when that and some
1:05:21
Speaker 1: prices are exceptionally high against fundamental summer very low, and
1:05:24
Speaker 1: so this old they just in the down cycle, they
1:05:28
Speaker 1: really the big ones end up really getting punished. And
1:05:32
Speaker 1: the first version that we knew that there was an
1:05:34
Speaker 1: issue here was if you just take the SMP five
1:05:37
Speaker 1: hundred and equal weight at all of them. Now there
1:05:39
Speaker 1: are reasons to say, why do I have Apple and
1:05:42
Speaker 1: some tiny little company at the same weight, But it
1:05:45
Speaker 1: tends to at certain points to outperform. So that's not
1:05:48
Speaker 1: what we're talking about. We're talking about waiting companies instead
1:05:52
Speaker 1: of based on their capitalization. You mentioned these earlier, as
1:05:55
Speaker 1: you mentioned uh dividends, earnings, growth, sales, growth, book value.
1:06:00
Speaker 1: There's a whole bunch of fundamental facts. Let me give
1:06:02
Speaker 1: you the example. First. I respect the heck out of
1:06:05
Speaker 1: both Rob and Jim. Um. They are a little bit
1:06:08
Speaker 1: more on that on that we talked earlier about the
1:06:10
Speaker 1: Schiller Farmer spectrum of efficient markets to inefficient. Maybe they're
1:06:13
Speaker 1: just more courageous than me. I'm in the muddy middle.
1:06:15
Speaker 1: They're a little bit more towards Schiller in their explanations.
1:06:17
Speaker 1: I'm always I think bubbles and insanity like you talked
1:06:20
Speaker 1: about do happen, just more rarely than probably those guys do.
1:06:23
Speaker 1: And I'm more willing to entertain efficient market risk based.
1:06:26
Speaker 1: Maybe it's just in my DNA and I'm still scared
1:06:28
Speaker 1: of my my professor. Um, but that wouldn't fature. You're right,
1:06:36
Speaker 1: if if fundam if a fundamental index. Let's make it
1:06:39
Speaker 1: simple and just use one measure. Earnings is formed waiting
1:06:42
Speaker 1: things by earnings. I had a friend, Bob Jones, who
1:06:45
Speaker 1: was doing this back in the eighties at Goldman sachs Um.
1:06:48
Speaker 1: It turns out that if you do the math and
1:06:50
Speaker 1: you compare and index weighted by earnings to one weighted
1:06:54
Speaker 1: by market cap. It's exactly not similar, but exactly the
1:06:58
Speaker 1: same as starting with a market cap and tilting with
1:07:02
Speaker 1: a precise, simple formula towards a low price to earning
1:07:05
Speaker 1: stocks and away from high price to earning stocks. So
1:07:07
Speaker 1: it's it's away from momentum and towards value. Towards Yeah,
1:07:12
Speaker 1: that's more accurate. Momentum will vary some sometimes, so it's
1:07:17
Speaker 1: away from growth towards value. What I think Rob has
1:07:21
Speaker 1: come up with is a great way to explain value
1:07:24
Speaker 1: investing in a different way, explaining it as ignoring prices
1:07:28
Speaker 1: and waiting by fundamentals, but you get to the exact
1:07:31
Speaker 1: same place. What I don't think and Rob knows this,
1:07:33
Speaker 1: we've argued about, we've debated it at the Q Group
1:07:35
Speaker 1: a quantitative of finance gathering is I think he's come
1:07:39
Speaker 1: up with a great way to explain in market value investing.
1:07:42
Speaker 1: But but that is different than saying he's come up
1:07:44
Speaker 1: with something that we didn't know about before we did
1:07:47
Speaker 1: know about value investing. If he's brought more people into
1:07:49
Speaker 1: the fold, if if, if on net this makes markets better,
1:07:53
Speaker 1: because I think more people did value investing, some of
1:07:55
Speaker 1: the efficacy would go away like it does for any strategy,
1:07:57
Speaker 1: but prices would be a little more accurate. These are
1:07:59
Speaker 1: all things, but I don't think it was as new.
1:08:02
Speaker 1: My title, as you know for this was was it
1:08:05
Speaker 1: fundamental indexing? Not uh not not indexing, not new, still awesome?
1:08:13
Speaker 1: That was a smart beta smart bank, not beta, not new,
1:08:18
Speaker 1: still awesome. And why is it not new? Because we
1:08:20
Speaker 1: knew about value forever? You gave the example in the
1:08:23
Speaker 1: tech bubble of a few stocks driving everything. You know
1:08:26
Speaker 1: what if you tilted towards towards low price to book
1:08:28
Speaker 1: to earn, low price to book, low price earnings, low
1:08:30
Speaker 1: price to sales, you avoided the exact same stocks, and
1:08:33
Speaker 1: you overweighed all the same jump that that a fundamental
1:08:36
Speaker 1: index or rob or not would be underweighting. Get to
1:08:39
Speaker 1: the same place. He came up with a great way
1:08:41
Speaker 1: to explain value investing as being indifferent to price. It
1:08:46
Speaker 1: is a very specific value tilt um. So it's it's wonderful,
1:08:50
Speaker 1: it's just less new. The paper he put out that
1:08:52
Speaker 1: I found endlessly amusing, if you can say that about
1:08:54
Speaker 1: a white paper, was that you could take any of
1:08:57
Speaker 1: these fundamental metrics Devan, and you'll earnings, growth, sales, growth
1:09:02
Speaker 1: and either the that metric or the inverse of that
1:09:07
Speaker 1: metric beats capitalization, meaning we're gonna take the and wait
1:09:11
Speaker 1: this by the fastest growing earnings or inverted and and
1:09:17
Speaker 1: reverse it. As long as it's essentially what that says is,
1:09:20
Speaker 1: as long as it's not market gap, it's going to
1:09:22
Speaker 1: be better what happens there. Um And I don't remember
1:09:25
Speaker 1: the specific measures e e use, but if you get
1:09:28
Speaker 1: anything close to an equal weighted portfolio and to a
1:09:32
Speaker 1: random or an equal way to portfolios, why you named
1:09:34
Speaker 1: after Malkiel's monkeys, Because that's the ultimate, uh you know
1:09:38
Speaker 1: statement about a random portfolio. If you get any kind
1:09:41
Speaker 1: of random portfolio, it ends up tilting towards small value.
1:09:46
Speaker 1: See rob phrases it in terms of say that again,
1:09:48
Speaker 1: if you take a random portfolio diversified enough that you
1:09:52
Speaker 1: can't randomly pick just tech stocks. Um, if you take
1:09:55
Speaker 1: a random portfolio, it starts to look a lot like
1:09:58
Speaker 1: an equal weighted portfolio. Both of those random or equal
1:10:03
Speaker 1: weight will on average look small because there are a
1:10:06
Speaker 1: lot more, and we'll look cheap because those guys were
1:10:11
Speaker 1: overpriced or expensive because of rational reasons. I'll try to
1:10:15
Speaker 1: not to not to fight that battle here. But those
1:10:17
Speaker 1: guys who are are very expensive are bigger market caps,
1:10:21
Speaker 1: so they're to make up the total market cap to
1:10:22
Speaker 1: have to be more so if you get that random portfolio,
1:10:26
Speaker 1: it's a fun finding, but it really is saying something.
1:10:30
Speaker 1: And again I this one. I'm not saying I'm not
1:10:34
Speaker 1: kind of lacking. There's great originality in this finding, but
1:10:38
Speaker 1: we kind of knew it. Again, Um, we were not beta,
1:10:41
Speaker 1: meaning it's not the market. We doubt there's beta going on.
1:10:44
Speaker 1: I get, I doubt this is tremendous. If you randomly
1:10:47
Speaker 1: select the portfolio probably goes the other way. You're probably
1:10:49
Speaker 1: a litt high beta and it's not new because we
1:10:51
Speaker 1: knew this for a lot. You're saying this was no
1:10:53
Speaker 1: great way to show it. It's awesome again, Yes, but
1:10:58
Speaker 1: if you pick a random portfolio, you will be smaller
1:11:02
Speaker 1: and cheaper than than than than the market. Um doesn't
1:11:05
Speaker 1: mean you should pick a random portfolio. Once you think
1:11:08
Speaker 1: small and cheap work, you might want to be a
1:11:10
Speaker 1: little more systematic about it than throwing darts. Rob's way,
1:11:13
Speaker 1: our way, other d phase ways, other ways I would
1:11:16
Speaker 1: say are better than random, right, but random will get
1:11:19
Speaker 1: you a little towards small and which is a fun finding.
1:11:23
Speaker 1: It absolutely is. Let's take, um, keep trying to buy
1:11:26
Speaker 1: Rob's monkeys, he won't sell them. Um, you could go
1:11:29
Speaker 1: and pick up monkeys pretty much anywhere these days. Any
1:11:31
Speaker 1: monkey will work to Maryland special monkeys. No, he just takes,
1:11:37
Speaker 1: you know, just just he takes discarded traders and and
1:11:42
Speaker 1: puts them. Uh. And I still work in this field.
1:11:44
Speaker 1: I'm not gonna laugh at that. On so do I.
1:11:47
Speaker 1: But on I care less than um. So let's talk
1:11:51
Speaker 1: about the Uh, let's talk about the buffet. A quantitative
1:11:55
Speaker 1: approach to Warren Buffett, which I thought was um white,
1:11:59
Speaker 1: it's charming, the right word for a white paper. We'll
1:12:02
Speaker 1: take charming. Um. This was written by colleagues of mine,
1:12:05
Speaker 1: David Cabilla, lass A Peterson and Andrea Frasini. Um. So
1:12:08
Speaker 1: I'm just gonna take credit for their work or blame
1:12:10
Speaker 1: because I'm the one. I'm the one here. Um, they
1:12:13
Speaker 1: did a really fun thing. Um. They And by the way,
1:12:17
Speaker 1: let me say beforehand, in case we don't get to it,
1:12:19
Speaker 1: we ended up with we We had tremendous, incredible respect
1:12:24
Speaker 1: for his investing skill beforehand, and we ended up there
1:12:27
Speaker 1: afterwards Um, when we say, and we do a couple
1:12:29
Speaker 1: of times, and I'm gonna guess he wouldn't agree with this.
1:12:33
Speaker 1: I have not spoken to a man personally about it.
1:12:35
Speaker 1: We say things like we explain his alpha, that is
1:12:39
Speaker 1: saying that after thirty forty years, we've seen that these
1:12:44
Speaker 1: are factor tilts. He did it thirty four years ago,
1:12:46
Speaker 1: thirty forty years ago. He's stuck with it through incredibly
1:12:49
Speaker 1: big ups and downs. He has not always made people
1:12:54
Speaker 1: were like nineties and seventies, he suffered greatly in the
1:12:59
Speaker 1: bear market. He's he's had his ups and downs. Um,
1:13:01
Speaker 1: But what they did was they took some of admittedly
1:13:04
Speaker 1: the standard things people like us look at the same
1:13:06
Speaker 1: things you and I have been talking about. Value. Yes,
1:13:09
Speaker 1: not surprisingly, he has a tilt towards systematic value. Doesn't
1:13:13
Speaker 1: mean he's a quant following the strategy. It just means
1:13:15
Speaker 1: his returns tend to correlate, tend to move when when
1:13:18
Speaker 1: cheap beats expensive, it's a better time for Buffett. A
1:13:21
Speaker 1: time like when expensive crushes cheap, all I'll see equal
1:13:26
Speaker 1: your guests would be not as good a time for Buffett.
1:13:28
Speaker 1: He had no waiting on momentum. Not surprising How could
1:13:31
Speaker 1: a man who's holding period preferred holding period is forever,
1:13:33
Speaker 1: an actual holding period is very very strong, very very long,
1:13:37
Speaker 1: really have a loading on momentum. You need to rebalance
1:13:39
Speaker 1: to load on momentum. If he had, when we would
1:13:41
Speaker 1: have doubted our data. He also loaded, though interestingly, on
1:13:45
Speaker 1: the major measures for quality, things like profitable companies and
1:13:48
Speaker 1: low risk companies. And he but he talks about that
1:13:51
Speaker 1: all the time. Is I buy this? Is this as
1:13:53
Speaker 1: if I'm buying a company that's private, unaware of what
1:13:57
Speaker 1: here's Here's it accurate but very nice, but still I
1:14:00
Speaker 1: think not not overnice, just just accurately, nice way to
1:14:04
Speaker 1: describe it. He does what he says he does, and
1:14:06
Speaker 1: that's what our guys verified. Now that might seem like
1:14:09
Speaker 1: a small thing, but do you know how many times
1:14:11
Speaker 1: you go look and find someone doesn't do what they
1:14:14
Speaker 1: say they do. He systematically, and he's not following a
1:14:18
Speaker 1: quant system, but he systematically with a great nice fit
1:14:21
Speaker 1: long term. Looks like someone looking for cheap, profitable, low
1:14:26
Speaker 1: risk stocks. And then here's something that I do believe,
1:14:28
Speaker 1: we just know he does. He applies a modest amount
1:14:31
Speaker 1: of leverage to it. If you buy low risk stocks
1:14:34
Speaker 1: you tend to and this is something we believe. So
1:14:37
Speaker 1: let's let's talk about let's let's pivot on this to
1:14:40
Speaker 1: your discussion on risk party where right. Well, that's why
1:14:44
Speaker 1: I wanted to bring it up, because most people talk
1:14:46
Speaker 1: about allocation in terms of asset class. When we talk
1:14:50
Speaker 1: about risk parity, we're really talking about allocation in terms
1:14:53
Speaker 1: of different types of risk. And you have said, you know,
1:14:57
Speaker 1: and I'll give you an opportunity to caveat that huge
1:15:02
Speaker 1: judiciously used with the right assets, used at the right time.
1:15:07
Speaker 1: A little bit of leverage is not a terrible thing. Yeah.
1:15:10
Speaker 1: Um And and here you guys can all think I'm
1:15:12
Speaker 1: wimpy for all the caveats, but you've got to be
1:15:14
Speaker 1: very careful. I would never want to be quoted. It's
1:15:17
Speaker 1: just saying leverage is a good thing because you go
1:15:19
Speaker 1: take a concentrated bet that's already scary, and lever you've
1:15:23
Speaker 1: made it super scary. You go lever something that's not
1:15:27
Speaker 1: very liquid. Um with leverage that has a shorter time
1:15:30
Speaker 1: arise and shorter term long term capital. That's the postal
1:15:35
Speaker 1: child for that. Yeah, it's they've certainly become that. Um
1:15:39
Speaker 1: I I we call that the death combination to bid
1:15:41
Speaker 1: melodramatic uh, and it can happen, you know if anyone does.
1:15:45
Speaker 1: No one has magic leveraging a a illiquid asset with
1:15:49
Speaker 1: with leverage have to pay back tomorrow. That's that's extremely dangerous.
1:15:52
Speaker 1: And as we saw in the last crisis, UM, borrowing
1:15:58
Speaker 1: long and funding it short is a meaning long term
1:16:01
Speaker 1: obligations that are funded with short term cash flow is
1:16:05
Speaker 1: a is a terrible combination. It always has been, and
1:16:08
Speaker 1: there's always the lower and it always happens again. Now
1:16:10
Speaker 1: here's where we think leverage can be useful. UM and
1:16:13
Speaker 1: you I, you and I just talked about the case
1:16:15
Speaker 1: of Warren Buffett where he has low risk, low BITA stocks,
1:16:18
Speaker 1: so he think that those do tend to outperform their
1:16:22
Speaker 1: risk over time. There has been a strong results for
1:16:26
Speaker 1: very long periods that lower stocks do better than they should.
1:16:29
Speaker 1: But that doesn't mean they actually do better than high
1:16:31
Speaker 1: risk stocks. They're supposed to lose, and they refuse to lose.
1:16:35
Speaker 1: If a lower stock keeps up with a high risk
1:16:37
Speaker 1: stock just keeps up, you go, wow, that's not supposed
1:16:40
Speaker 1: to happen. The risk premium if if correct correct. Having
1:16:44
Speaker 1: said that, if you don't apply some leverage. Buffett actually
1:16:47
Speaker 1: went about performed. He would have kept up at low risk.
1:16:50
Speaker 1: And I don't think he was too interested in He's
1:16:51
Speaker 1: not looking for risk adjusted return. He wanted to eat them.
1:16:54
Speaker 1: People always say you can't eat risk adjusted returns. With
1:16:56
Speaker 1: some mild leverage, you can. You can go too far.
1:16:59
Speaker 1: One No Warren buffets one point six to one. Pretty
1:17:02
Speaker 1: good idea and a guy who's also sitting with forty
1:17:04
Speaker 1: billion dollars of cash and no borrowed money, and the
1:17:07
Speaker 1: bullet proof and the way he structured his business exactly,
1:17:10
Speaker 1: there's a form of alpha to that. We say he's
1:17:12
Speaker 1: stuck with things through thick and thin. He that requires
1:17:15
Speaker 1: incredible mental fortitude. Even with a great structure. Anyone can
1:17:19
Speaker 1: cave on their own just from panic. He didn't do that,
1:17:21
Speaker 1: but he also structured it so no one else could
1:17:23
Speaker 1: make him throw in the towel. So now let's talk
1:17:25
Speaker 1: about something, because you're you're making me think of things
1:17:29
Speaker 1: we didn't get. Sorry, I'm I'm I'm I'm a tangent.
1:17:32
Speaker 1: No no, no, I love these digressions because you're making
1:17:34
Speaker 1: me think of other stuff we didn't get to. The August. Oh, seven,
1:17:39
Speaker 1: avoid I was hoping to run out the clock line.
1:17:40
Speaker 1: We could, we could do something. So so so let
1:17:45
Speaker 1: me set this up in a way that I couldn't
1:17:46
Speaker 1: done the radio because it was too long. So oh seven.
1:17:49
Speaker 1: So in Juno seven we have the bear Stones hedge
1:17:51
Speaker 1: fund collapse. A lot of quant funds are crunching the
1:17:55
Speaker 1: same numbers. I don't say they're looking at these same things,
1:17:58
Speaker 1: but a lot of the same data everybody looks at.
1:18:01
Speaker 1: And so you ended up with similar positions across a
1:18:04
Speaker 1: lot of different quand funds. You actually talk about this
1:18:08
Speaker 1: in the book The Quants to some some degree. And
1:18:11
Speaker 1: so August oh seven is a debacle, and your fund
1:18:16
Speaker 1: gets she lacked. You're down thirteen percent in a month,
1:18:19
Speaker 1: in a week, in a week, I'm sorry, in a
1:18:21
Speaker 1: week down So down thirteen percent in a week? What
1:18:26
Speaker 1: the you know? The number is better than I don't
1:18:28
Speaker 1: even remember the precise number, but I do remember the
1:18:30
Speaker 1: timeframe thirty and by the way, you wanted to the number. No, no,
1:18:35
Speaker 1: I believe you. I was not thirty nine billion across
1:18:38
Speaker 1: this is this You were smaller fund ten years ago,
1:18:41
Speaker 1: and this was right in the middle of the financial crisis.
1:18:45
Speaker 1: You started out at thirty nine billion and you ended
1:18:47
Speaker 1: at seventeen billion. Now that's got to be a painful
1:18:50
Speaker 1: thing most But before before you answer, you and you
1:18:56
Speaker 1: and I are two walking tangents, So so before you answer,
1:18:59
Speaker 1: I just want soon an't have to stand. This has
1:19:02
Speaker 1: a happy ending. Stuck with the model and you recovered
1:19:06
Speaker 1: and then some it actually turned out to be long
1:19:09
Speaker 1: term a winner. But what was it like in that
1:19:12
Speaker 1: month when you're just watching assets get destroyed? If you'll
1:19:15
Speaker 1: permit me, I want to back up one section on
1:19:18
Speaker 1: on your numbers sound exactly right on on the shrinkage
1:19:21
Speaker 1: of our firm, which is another sign I told you
1:19:23
Speaker 1: I have a great research stuff. With that said, one
1:19:27
Speaker 1: thing that I should have made clear much earlier is
1:19:29
Speaker 1: and I think you know this barriers. We're not, uh
1:19:33
Speaker 1: by any means only a hedge fund. That's a minority
1:19:35
Speaker 1: of what we do. A lot of what we do
1:19:36
Speaker 1: is long only. So a fair amount of that came
1:19:39
Speaker 1: from just market shrinking. You know, every asset manager shrunk
1:19:41
Speaker 1: by a lot. And we had a very rough time
1:19:43
Speaker 1: we had. That was a couple of months the whole
1:19:45
Speaker 1: market and then over the whole financial crisis. It was
1:19:48
Speaker 1: more than draw down. We actually didn't lose a ton
1:19:53
Speaker 1: of clients. We we we we lost some, but mostly
1:19:56
Speaker 1: we watched our assets shrink with the markets. But none
1:20:00
Speaker 1: of that makes your figures for August wrong. This was
1:20:02
Speaker 1: a harrowing. Maybe it wasn't near death, but it felt
1:20:06
Speaker 1: near death. Hold on a second, you said, and I
1:20:08
Speaker 1: love giving you your own quotes. Um, I'm looking for
1:20:14
Speaker 1: your exact quote. We were looking the grim reaper in
1:20:17
Speaker 1: the face. Well, I mean a particular person. I'm friendly.
1:20:21
Speaker 1: I'm friends with Ken Griffin Um, who is one of
1:20:24
Speaker 1: the head fund managers. I I believe in. I think
1:20:26
Speaker 1: this guy is a pretty amazing guy. Uh run citi
1:20:30
Speaker 1: um he Um is famous for being a smart investor.
1:20:34
Speaker 1: When something's very distressed and facing doom, he goes and
1:20:37
Speaker 1: buys it very cheaply. Ken called me near the negative
1:20:41
Speaker 1: of this, and my assistant just goes Ken Griffins on
1:20:43
Speaker 1: the line and um, I think, I actually wrote, I
1:20:45
Speaker 1: could see the valkyries coming, I could feel um and
1:20:48
Speaker 1: in fact, he just wanted to shoot. And and I
1:20:51
Speaker 1: literally said, you know, I think we're actually pretty stable here.
1:20:54
Speaker 1: I don't think we're but but if Ken thinks were dying.
1:20:56
Speaker 1: We must be dying. Um. So if you were an
1:21:00
Speaker 1: sharing you would you would say, if Ken thinks were dying,
1:21:02
Speaker 1: we're gonna be okay, Well I go to stay. I
1:21:05
Speaker 1: will be honest. For most people, I am a contrarian.
1:21:07
Speaker 1: Which can I start to think, maybe he knows something
1:21:09
Speaker 1: I don't know. Um, maybe he knows it's worse than
1:21:12
Speaker 1: I think. But so let's let's let's roll roll back
1:21:17
Speaker 1: what happened. You are exactly right. I do not deny
1:21:20
Speaker 1: for a second that that quants do related things. Everyone
1:21:24
Speaker 1: has their own twist on it, um how to measure
1:21:26
Speaker 1: it better. Some some parts of what we do or
1:21:29
Speaker 1: others do are completely different than others. But by and large,
1:21:32
Speaker 1: the two of the key things in all of quantitative
1:21:34
Speaker 1: management I've talked about with you for a few hours
1:21:36
Speaker 1: our value and momentum investing. You can have every twist
1:21:39
Speaker 1: in you in the world you want. We think value
1:21:41
Speaker 1: works better if you don't make an industry bed. For instance,
1:21:44
Speaker 1: if you buy the cheap and sell the expensive within
1:21:46
Speaker 1: every industry, within each each group. It can work a
1:21:50
Speaker 1: little bit for industries, but it is stronger risk adjusted.
1:21:53
Speaker 1: Certainly you're that industry is harder to compare one tiny example.
1:21:57
Speaker 1: Everyone has a twist, is my only example, but they're correlated.
1:22:01
Speaker 1: If you're if you're if you're running a quant shop,
1:22:04
Speaker 1: and let's do a hedge fund not to beat the benchmark.
1:22:06
Speaker 1: You're long cheap with good momentum, and you short expensive
1:22:09
Speaker 1: with bad momentum. Again, the models do other things than that.
1:22:12
Speaker 1: But let's say that's it, and I'm doing the same,
1:22:14
Speaker 1: And we've worked for ten years separately and we've done tweaks,
1:22:17
Speaker 1: and we both think our models better than the other
1:22:18
Speaker 1: guy and better than they were ten years ago. That
1:22:21
Speaker 1: might or might it's not gonna be true for both
1:22:23
Speaker 1: of us, but we both can be better than ten
1:22:24
Speaker 1: years ago, and one of us is in reality better.
1:22:26
Speaker 1: It's not gonna matter that the in the in the
1:22:28
Speaker 1: in the ten days value and momentum both get utterly obliterated.
1:22:32
Speaker 1: We both have a common theme that ven diagram is
1:22:34
Speaker 1: gonna have a huge overlap and it's gonna be the
1:22:36
Speaker 1: most extreme in that period. Now I can speak for ourselves,
1:22:40
Speaker 1: we were not I think maybe we were um, but
1:22:43
Speaker 1: I don't think we were naive about this. Going into
1:22:45
Speaker 1: that time, we knew a ton of more people were
1:22:47
Speaker 1: doing quants than when we started fifteen we started in
1:22:50
Speaker 1: the mid nineties, two thousands, twelve years later, we're a
1:22:54
Speaker 1: ton more people. We're doing it um. We measure we
1:22:58
Speaker 1: wrote a paper on this during the act bubble and
1:23:00
Speaker 1: we still do it. We measure the longs verse of shorts.
1:23:04
Speaker 1: How cheap are they? They're always a little bit cheaper. Remember,
1:23:07
Speaker 1: values a big part of our model. So if they
1:23:09
Speaker 1: weren't cheaper, we're just doing it wrong. So we hope
1:23:12
Speaker 1: they're cheaper or else, or else we're adding it up wrong.
1:23:14
Speaker 1: You know, if if if cheapest part of your model,
1:23:16
Speaker 1: a big part of your model, and what you're long
1:23:18
Speaker 1: is not cheaper than what you're short, you that's backwards.
1:23:22
Speaker 1: Your your math is off. So but but they're not
1:23:24
Speaker 1: always the same amount cheaper at the at the peak,
1:23:27
Speaker 1: that spread can vary dramatic. Flip it over to talk
1:23:30
Speaker 1: about how much more expensive the expensive stocks are, as
1:23:33
Speaker 1: you can imagine, at the peak of the tech bubble
1:23:34
Speaker 1: was the most that numbers ever been. The expensive stocks
1:23:37
Speaker 1: were ridiculously way more than going into August of of
1:23:43
Speaker 1: oh seven, and we were thinking about this, We're worried
1:23:45
Speaker 1: it was crowded. This number was about its long term average.
1:23:49
Speaker 1: Really yeah, which now there Now there are a lot
1:23:52
Speaker 1: more people doing quant but someone is on the other side.
1:23:54
Speaker 1: This is a question we try to ask ourselves all
1:23:56
Speaker 1: the time. Um, my colleague Aunt Dilman, and I are
1:23:58
Speaker 1: going to write a paper this with the title Who's
1:24:01
Speaker 1: on the other side. Anytime you do a strategy, a trade,
1:24:03
Speaker 1: whatever you do, you should say, who's money am I taking?
1:24:06
Speaker 1: And if you don't have a good answer, you probably
1:24:08
Speaker 1: shouldn't be doing it. We think who's the sucker at
1:24:11
Speaker 1: the exactly if it's not you, it's more if you
1:24:14
Speaker 1: can't spot after everyone has a different version after half
1:24:18
Speaker 1: an hour. If you can't spot the spot the soccer,
1:24:20
Speaker 1: it's you, it's the it's the trading version, the strategy
1:24:23
Speaker 1: version of that. So what we said to ourselves is
1:24:26
Speaker 1: this has not been arbitraged away. This is not a
1:24:28
Speaker 1: ton of capital that has made cheap stocks no longer
1:24:31
Speaker 1: very cheap, but it compressed that whole spectrum. And here's
1:24:34
Speaker 1: where I think, you know, I think we went further
1:24:36
Speaker 1: than most and even worrying about it. A lot of
1:24:38
Speaker 1: people were pretty blase about it, but we didn't get
1:24:41
Speaker 1: it right. We still had a disaster. So what happened
1:24:43
Speaker 1: is if you had asked me before August of oh seven,
1:24:46
Speaker 1: I would have thought that a not a precondition, but
1:24:52
Speaker 1: but two for something to crash, it was far more
1:24:55
Speaker 1: likely to crash it was expensive, and this was not expensive,
1:25:00
Speaker 1: it turns out, and this is really quite obvious. I
1:25:01
Speaker 1: just thought it was less likely, not impossible, that if
1:25:04
Speaker 1: everyone tries to sell on the same day, it doesn't
1:25:06
Speaker 1: matter if it's expensive or cheap. It was really just
1:25:08
Speaker 1: the crowded trade unwind it. And my version of what
1:25:11
Speaker 1: happened in July and August of oh seven it's very
1:25:14
Speaker 1: similar to what you said. But but I think we've
1:25:15
Speaker 1: actually tracked and we still don't know the actual firm
1:25:17
Speaker 1: that began the selling. We joked home patient zero um,
1:25:21
Speaker 1: but it was. But that's a great metaphor because a virus.
1:25:26
Speaker 1: It was the first kind of echoing. The first adimbiration
1:25:29
Speaker 1: of the of the of the financial crisis was in July,
1:25:32
Speaker 1: following that Bear Stern stuff. In July, credit got really
1:25:36
Speaker 1: pummeled and a lot of particular hedge funds had added
1:25:39
Speaker 1: quant during a great five year run, and this is
1:25:41
Speaker 1: I think dangerous. They weren't quants in their in their DNA,
1:25:45
Speaker 1: they added it. They they saw it was doing well.
1:25:47
Speaker 1: They hired a guy that gave him some capital and said,
1:25:49
Speaker 1: trying not to message. And then this is hard literally
1:25:51
Speaker 1: still hard to prove. But what we think happened is
1:25:53
Speaker 1: credit got slammed in July, and a lot of these
1:25:56
Speaker 1: people said, we just got a lower risk. We weren't
1:25:57
Speaker 1: particularly against quant, we just got a lower risk. And
1:26:00
Speaker 1: all try to lower everything. They do it once and
1:26:02
Speaker 1: we're very common in their quant traits. And then like
1:26:05
Speaker 1: like all these stories, Um, it just got rolling. And
1:26:09
Speaker 1: this was much more. This was not a bear market
1:26:11
Speaker 1: for quant. This was closer to a QUANT flash crash.
1:26:13
Speaker 1: It was no one's described it that way. I grew
1:26:16
Speaker 1: up it in the car on the way here. But
1:26:19
Speaker 1: the longer than the flash crash. But it was three
1:26:22
Speaker 1: weeks instead of a day. But it's not. It was
1:26:25
Speaker 1: a brutal three weeks. Yeah, the and roughly the first
1:26:28
Speaker 1: half of that was down on the next half of
1:26:30
Speaker 1: it was making two thirds of it back. Um, and
1:26:32
Speaker 1: you are right. We did stick with what we do.
1:26:35
Speaker 1: Which is a question I have to ask you, just
1:26:37
Speaker 1: from a um a judicial temperament question is how do
1:26:43
Speaker 1: you live with that as the c I O. Everyone
1:26:46
Speaker 1: at my firm is laughing here because, um, I wish
1:26:50
Speaker 1: this was not public information. Uh. But the Wall Street
1:26:53
Speaker 1: Journal ran a couple of stories, and the Quant Book
1:26:55
Speaker 1: talked about this. Uh. By the way, that Quant book
1:26:58
Speaker 1: is one of my favorite books. It's fent. I am
1:27:00
Speaker 1: my major beef for that book. Is they they tell
1:27:03
Speaker 1: him he reports I was a fat kid in high school.
1:27:05
Speaker 1: I'm a fat guy. Now it's not fair. I was
1:27:09
Speaker 1: not I was. This is why is he assuming that
1:27:14
Speaker 1: that's a tough yellow journalism. Um, but it's ill sea
1:27:18
Speaker 1: I had. I had several friends from high school called
1:27:20
Speaker 1: me up and go, yeah you chubby now? But you
1:27:22
Speaker 1: were okay about um? But um, you know to your point, Uh,
1:27:27
Speaker 1: this is a little embarrassing. But I might have punched
1:27:30
Speaker 1: my computer screen a couple of times and and had
1:27:32
Speaker 1: it visible and had the Walster Journal actually write about it.
1:27:35
Speaker 1: So you just smashed yeah, I and and either I
1:27:38
Speaker 1: throw a lousy punch or view sonic. Really I should
1:27:42
Speaker 1: have been a commercial for them, because I did it
1:27:43
Speaker 1: with my best cross and did no damage to it. Um.
1:27:46
Speaker 1: The journal, I'm sorry for the segue, but the journal
1:27:50
Speaker 1: I wrote a letter to the journal, thinking they would
1:27:52
Speaker 1: never publish it. I love the Walster Journal, but they're
1:27:55
Speaker 1: not known for their hilarity. And I wrote what I
1:27:57
Speaker 1: thought was a humorous letter doesn't translate into type area.
1:28:01
Speaker 1: But but I forgot that as the subject of a story,
1:28:04
Speaker 1: they they feel and they have great journalistic integrity. I
1:28:06
Speaker 1: think they feel a h that they should publish your response.
1:28:11
Speaker 1: But I wrote what I thought was a whimsical response
1:28:12
Speaker 1: that had no chance of being published. I wrote, your
1:28:15
Speaker 1: reporter wrote that on several occasions, on bad days, I
1:28:18
Speaker 1: punched the computer. Um. I can't disagree with the facts,
1:28:21
Speaker 1: but he left out a very important thing on each
1:28:24
Speaker 1: of those days. The monitor deserved it. The next day
1:28:27
Speaker 1: it's in the journal, and I swear to God didn't.
1:28:30
Speaker 1: What was the response to that, Because people like that, Um,
1:28:34
Speaker 1: human foibles are okay, but being able to laugh at
1:28:36
Speaker 1: yourself is okay too. And I do find myself ridiculous
1:28:38
Speaker 1: at times. Uh, you met my wife. There's no one
1:28:41
Speaker 1: more ridiculous than me. And all right, spouses and children
1:28:48
Speaker 1: have no respect for you, no matter who you are.
1:28:50
Speaker 1: That's why you get dogs dogs. You know. The line
1:28:53
Speaker 1: I love is we we hope to become the person
1:28:58
Speaker 1: dogs think, Oh that's awesome. Have a Winston Churchill line
1:29:01
Speaker 1: that he said he prefers pigs because as pets, because
1:29:04
Speaker 1: dogs look up to your cats look down to your
1:29:06
Speaker 1: pigs looking straight in the eye, which I don't know
1:29:08
Speaker 1: if that's actually true. It's a great. So let me
1:29:12
Speaker 1: bring this back. So you're you're running thirty billion dollars
1:29:16
Speaker 1: or so, the market is just we're getting Actually do
1:29:20
Speaker 1: you take that home at night? How do you keep
1:29:22
Speaker 1: faith with your quant world was miserable. One thing left
1:29:28
Speaker 1: out occasionally is this was after a bad month and
1:29:32
Speaker 1: before the GFC, but over the full painful period for quants,
1:29:35
Speaker 1: the SMP was actually up slightly. We didn't rattle the world.
1:29:39
Speaker 1: The world had nothing to do. We lost I like
1:29:41
Speaker 1: to say we lost money all on our own. Our
1:29:43
Speaker 1: shorts went up and our longs went down. But the
1:29:46
Speaker 1: market was it was it. We were about to have
1:29:48
Speaker 1: a bad neutral funds Well, I'd hate to try to
1:29:52
Speaker 1: sell us two clients at the time. Is no, it's
1:29:54
Speaker 1: okay because because we are market neutral. But I would
1:29:57
Speaker 1: prefer it to happening there then in a crash, because
1:29:59
Speaker 1: it does fit the spare it so emotionally, emotionally, are
1:30:02
Speaker 1: you taking this home at night? Other than the occasional
1:30:05
Speaker 1: view sonic, I'll try to be very introspective about this.
1:30:08
Speaker 1: I'm really bad at internalizing something. I preach that this
1:30:12
Speaker 1: is a statistical process in a fat tailed world. Stuff
1:30:15
Speaker 1: will happen, it will work long term. UM. I I
1:30:18
Speaker 1: do get emotional because I wanted to work. UM. And
1:30:21
Speaker 1: it's frustrated to stare at red long absolutely and you
1:30:25
Speaker 1: get something UM that that I and others have called
1:30:27
Speaker 1: time dilation. We're trying to be physicists here, but what
1:30:30
Speaker 1: feels like a like a very long time is actually not.
1:30:34
Speaker 1: You know, you stare at it for a whole day,
1:30:36
Speaker 1: which we're not supposed to do by the way you're
1:30:38
Speaker 1: supposed to Um. You know, it's a statistical thing you
1:30:41
Speaker 1: check a few times, but staring at it every day
1:30:43
Speaker 1: is not old day. It's not productive. You stare at
1:30:45
Speaker 1: something old day, you'll feel like you just went through
1:30:47
Speaker 1: every up and down since since since the flood. Um.
1:30:51
Speaker 1: But what I've actually and I and my firm are
1:30:54
Speaker 1: actually pretty good at is not letting our emotions affect
1:30:57
Speaker 1: our investment process. So so stuck with the models, even
1:31:01
Speaker 1: though it might have been really churning you and the Kishkas.
1:31:04
Speaker 1: It was really bothering. The Kishka's were a flame. But uh,
1:31:08
Speaker 1: we did a few things. Now, when something's not working
1:31:12
Speaker 1: I think of the time, the right thing to do
1:31:14
Speaker 1: is nothing. Maybe some risk control if the bets too big,
1:31:17
Speaker 1: but but almost nothing. Don't just do something, sit there exactly,
1:31:20
Speaker 1: and that's very often, uh, the right thing to do.
1:31:23
Speaker 1: Far more often. I think you want to keep your mind,
1:31:26
Speaker 1: if not open slightly, Ajar. You can take that a
1:31:29
Speaker 1: few different ways too. You know, has the world really changed?
1:31:32
Speaker 1: It almost never does when they say it does. But
1:31:34
Speaker 1: could happen? Is there some reason our process won't work
1:31:37
Speaker 1: going forward? Um? And we try every time there's a
1:31:41
Speaker 1: bad result, and that that was the ultimate example, but
1:31:43
Speaker 1: every time we've had you know, we've done this for
1:31:45
Speaker 1: almost twenty years, including Goldman Sax. Life's been net good
1:31:48
Speaker 1: to us. But if it's never we're not better than
1:31:50
Speaker 1: Warren Buffett, and he's had bad times. Um So so
1:31:53
Speaker 1: I sense that you're good and emotionally compartmentalizing. Yeah, people,
1:31:58
Speaker 1: the the volet aility and the you know, you're the
1:32:02
Speaker 1: natural human reaction to panic would get greedy. I'm I
1:32:05
Speaker 1: wish I were good at having it not get to
1:32:07
Speaker 1: me the same way I have. I and my firm
1:32:10
Speaker 1: have gotten very good and always have been pretty pretty
1:32:13
Speaker 1: darn good at not letting an effect what we actually do.
1:32:16
Speaker 1: If I like to put it this way, Um, we
1:32:19
Speaker 1: trade on what we think or at least partially, somehoat
1:32:22
Speaker 1: risk and we're still in the farmer camp partially but somewhat.
1:32:24
Speaker 1: Behavioral biases that desire to run away and take off
1:32:28
Speaker 1: a process you know is a good process is a
1:32:31
Speaker 1: behavioral bias. It's what is what is uh a run
1:32:35
Speaker 1: on the strategy where people too many people try to
1:32:37
Speaker 1: get out? That makes it better going forward. If you're
1:32:40
Speaker 1: a believer, you've been right all along. It was a
1:32:42
Speaker 1: crowded trade. Now it's and I'll tell you we took
1:32:45
Speaker 1: too much comfort. And I mentioned it earlier that it
1:32:47
Speaker 1: wasn't expensive in the beginning. It's about the fifties percentage
1:32:50
Speaker 1: by the NAT or by the low point. It wasentie
1:32:54
Speaker 1: attractive and you love that intellectually. Doesn't mean you're Kishkas
1:32:58
Speaker 1: are not battling each other. I I'm gonna say that
1:33:00
Speaker 1: more often. Um, but we've gotten very good at at
1:33:04
Speaker 1: sticking with with things while keeping that mind a jar
1:33:08
Speaker 1: sounds a little crazy. I'm gonna have to come up
1:33:09
Speaker 1: with a not quite open that mean that's too willy nilly,
1:33:13
Speaker 1: that's like, yeah, maybe we're wrong. You're you're open to
1:33:16
Speaker 1: the possibility that everything you're doing is wrong while knowing
1:33:20
Speaker 1: mathematically it's unlikely. So you should look at it an
1:33:23
Speaker 1: open mind, expecting not to find anything. But if you do,
1:33:27
Speaker 1: if you you know, for instance, it are certain strategy
1:33:29
Speaker 1: strategy that are about speed. Um. They're famous ones earning
1:33:33
Speaker 1: surprise when earnings announcements come out. Maybe that still has
1:33:36
Speaker 1: a little bit of efficacy, but nowhere near what it
1:33:38
Speaker 1: once had because the world has not much faster. Um.
1:33:41
Speaker 1: So if you see an actual reason why your strategy
1:33:43
Speaker 1: shouldn't work anymore. I'm not saying you should keep doing
1:33:45
Speaker 1: what you've done in the past forever. But if you
1:33:47
Speaker 1: see no reason why what you've done in the past,
1:33:51
Speaker 1: what you think has worked for a hundred years in
1:33:53
Speaker 1: fifty different places, should not continue to work. You should
1:33:56
Speaker 1: not let your term results dissuade you. And we have
1:33:59
Speaker 1: gotten very very good, and I think we've always I've
1:34:01
Speaker 1: always been good. But even better over time and learning
1:34:03
Speaker 1: that lesson. A few times I've ever violated that, and
1:34:05
Speaker 1: those stories I'll take to the grave. I have regretted
1:34:09
Speaker 1: anytime you've violated when you know the math backs up
1:34:12
Speaker 1: with you haven't haven't done it in more than a decade.
1:34:15
Speaker 1: But a few times, you know, you don't say I'm
1:34:17
Speaker 1: violating this. You convince yourself, Well, this is the exception
1:34:20
Speaker 1: to convince yourself it's the right thing. You get data
1:34:23
Speaker 1: on it, you change your model, you tweak it. The
1:34:26
Speaker 1: whole world is fa And by the way, this is
1:34:28
Speaker 1: not just quants. How many how many non quants have
1:34:30
Speaker 1: a strong investment thesis that will turn out to be right,
1:34:33
Speaker 1: but occasionally cave on it when it gets too painful.
1:34:35
Speaker 1: How how often do we hear about a fun blowing
1:34:38
Speaker 1: up and find out, gee, you know, if they weren't
1:34:42
Speaker 1: either using borrowed money or so leverage or what have you,
1:34:45
Speaker 1: if they just stuck it out another six months, the
1:34:48
Speaker 1: uh MF Global, even the thing that blew up MF
1:34:51
Speaker 1: global a year later, that would have been a great
1:34:53
Speaker 1: that's that's exactly right. Things like whether leverage is useful,
1:34:57
Speaker 1: how much leverage is safe, how much volatility you've and
1:35:00
Speaker 1: take getting those lines of credit lined up right if
1:35:02
Speaker 1: you if you do that, how much you can take.
1:35:05
Speaker 1: Are all about surviving the short term. At no point
1:35:08
Speaker 1: should you do a strategy. And this is art not science.
1:35:11
Speaker 1: I'm not claiming that I have a perfect quant model
1:35:13
Speaker 1: for this, but you should the perfect strategy that you
1:35:17
Speaker 1: can't stick with, whether for real reasons that your creditors
1:35:20
Speaker 1: say you're done or emotional reasons that that that we
1:35:23
Speaker 1: all have a breaking just can't just can't, can't take it.
1:35:26
Speaker 1: The great strategy you can stick with, and this is obvious,
1:35:29
Speaker 1: but I think really important. The great strategy you can't
1:35:31
Speaker 1: stick with is obviously vastly inferior to the very good
1:35:34
Speaker 1: strategy you can stick with. Suboptimal beats optimal if you
1:35:37
Speaker 1: can't run with options. This comes up with investment advisors
1:35:40
Speaker 1: a lot um and and and this is something you
1:35:43
Speaker 1: know much more about than I am, and I do um.
1:35:46
Speaker 1: I think they have a huge role in helping structure
1:35:48
Speaker 1: a portfolio, but I think the single most important thing
1:35:50
Speaker 1: they do is make sure people stick with their plan
1:35:52
Speaker 1: through the long term. We we talked about that in
1:35:53
Speaker 1: the office all the time, that we well like to
1:35:56
Speaker 1: pretend that we're investment managers, but were really behavioral counselors.
1:36:00
Speaker 1: And a lot of that is before anybody puts a
1:36:03
Speaker 1: dime to work. Hey, this is a great strategy. But
1:36:06
Speaker 1: when this is down, are you gonna? Uh? The old
1:36:10
Speaker 1: joke is you know your your portfolio is getting killed?
1:36:13
Speaker 1: How how are you sleeping? I sleep like a baby?
1:36:15
Speaker 1: Really you you're down? Well? I wake up every hour,
1:36:19
Speaker 1: went myself and cry for mommy? Is is that that
1:36:21
Speaker 1: that cliche has never worked for that exact reason? Baby
1:36:24
Speaker 1: sleep lousy? Yeah? No, it's a terrible But now innocent
1:36:27
Speaker 1: is what they're going for? Um, but go on? Sorry,
1:36:31
Speaker 1: so no, but it's that thing is is It's funny
1:36:34
Speaker 1: because the more I'm listening to you, the more I'm
1:36:36
Speaker 1: thinking you are precisely, precisely halfway between farm and which
1:36:43
Speaker 1: is not a bad place too. You know, if you're
1:36:46
Speaker 1: gonna pick an island to live on, that's a damn
1:36:48
Speaker 1: good I. Um, you know I'd signed for that in
1:36:51
Speaker 1: a heartbeat. Quality wise, I think philosophy wise, I really
1:36:54
Speaker 1: am and have been for a long long time. I
1:36:58
Speaker 1: And it all comes back to thinking markets are not perfect,
1:37:01
Speaker 1: but they're they're harder to beat than people think. There's
1:37:03
Speaker 1: the Schiller's the first part faum is the second part
1:37:06
Speaker 1: um taking risk and and again are mixed with science. Uh.
1:37:11
Speaker 1: Taking risk gets paid off over time. Taking too much
1:37:14
Speaker 1: risk and you won't live to see to see the
1:37:17
Speaker 1: ending and then the things. Uh. You know, it's very
1:37:20
Speaker 1: funny Fama Schiller. UH. Fama would probably believe in value
1:37:23
Speaker 1: more than momentum, but the firm he works with, d
1:37:25
Speaker 1: f A uses some momentum UH in their in their
1:37:28
Speaker 1: in their process. We would use more. I'm not saying
1:37:30
Speaker 1: there aren't differences. Momentum is more of an inefficient um
1:37:33
Speaker 1: market story, but they try to avoid shorting momentum. And
1:37:37
Speaker 1: I won't get into the the geeky side. So even there,
1:37:40
Speaker 1: we're a point on a spectrum difference. We're not a
1:37:42
Speaker 1: sea change. One joke I had, I did a little
1:37:46
Speaker 1: tv UM where I had Fama and Schiller come on
1:37:49
Speaker 1: after their Nobel Prize separately. I wasn't trying to create
1:37:52
Speaker 1: a steel cage match um, but uh, and I pointed
1:37:55
Speaker 1: out to both of them, and I think they both
1:37:57
Speaker 1: I think they both agreed with this, that they both
1:37:59
Speaker 1: put on pretty similar portfolios, mostly value tilts a momentum
1:38:03
Speaker 1: to what they do. Again, probably more momentum for Shiller
1:38:05
Speaker 1: than Fama. Uh more market timing for Siller than Fama,
1:38:10
Speaker 1: but they would interpret it as working for radically different reasons.
1:38:14
Speaker 1: So it's kind of like, remember, me and Rob are
1:38:15
Speaker 1: not um fundamental in the mixing wins. He thinks it's
1:38:18
Speaker 1: brand new. I think it's the value effect. But we
1:38:19
Speaker 1: get to the same place, just just take different routes. Cliff,
1:38:22
Speaker 1: You've been so generous with your time, and I know
1:38:25
Speaker 1: your your staff is looking to take you to your
1:38:28
Speaker 1: next appointment. I want to thank important. Um, well, you
1:38:33
Speaker 1: are important, You're You're Let me blow a little smoke
1:38:36
Speaker 1: your way. It's not just that fortune of Forbes or
1:38:40
Speaker 1: whoever it was called, your hedge fund very influential. You
1:38:43
Speaker 1: consistently publish deeply thoughtful white papers. Forget the fact that
1:38:48
Speaker 1: they regularly win awards Best Paper with you this, and
1:38:51
Speaker 1: that you are part of the philosophical and financial debate
1:38:57
Speaker 1: that is moving the world to finance forward. And the
1:39:01
Speaker 1: joke in physics we you and I talked about about
1:39:04
Speaker 1: science earlier, there's a famous joke I forgot who I'm
1:39:07
Speaker 1: I'm I'm stealing this from who said physics advances one
1:39:10
Speaker 1: funeral at a time. It turns out finance doesn't have
1:39:14
Speaker 1: to advance that way. It advances one white paper at
1:39:17
Speaker 1: a time, as long as it's written in an intelligent,
1:39:20
Speaker 1: coaching and persuasive way that enough people say, hey, this
1:39:24
Speaker 1: guy is really impacting finance, and um, that's a smoke
1:39:28
Speaker 1: fellow your way. Very nice to you to say, Um,
1:39:31
Speaker 1: I appreciate that. I also punched computers when we lose,
1:39:34
Speaker 1: and I have a face for radio, but I will
1:39:36
Speaker 1: take the compliment same same. Here I've been speaking with
1:39:39
Speaker 1: Clifford ass Neest. He is the founder and c i
1:39:42
Speaker 1: O of a q r UM. If you enjoy these
1:39:47
Speaker 1: sort of conversations, you can look up an Inch or
1:39:51
Speaker 1: down an Inch on iTunes and see all of the
1:39:53
Speaker 1: previous UH Masters in Business series. Cliff, thank you so
1:39:57
Speaker 1: much for spending so much time. Thank you was great.
1:39:59
Speaker 1: This is ay Rehults. You've been listening to Masters in
1:40:02
Speaker 1: Business on Bloomberg Radio.