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