Speaker 1: This is Master's in Business with Barry Ridholts on Boomberg Radio.
Speaker 1: This week on the podcast, I have an extra special guest.
Speaker 1: His name is Matthew Rothman and he is the director
Speaker 1: of quantitative Strategies at Credit Swiss. He is really a
Speaker 1: fairly legendary guy in the world of quant He very
Speaker 1: specifically warned Lehman Brothers when he was a relatively new
Speaker 1: higher there about some of the problems that they were
Speaker 1: looking at with their quant strategies and ask questions that
Speaker 1: they really kind of dismissed and laughed at, What do
Speaker 1: you mean we might go out of business? That's the
Speaker 1: dumbest thing we've ever heard. He's also been profiled in
Speaker 1: a number of places. If you read Scott Patterson's The Quants,
Speaker 1: you can find him referenced throughout there. Pretty much the
Speaker 1: first guy to figure out what happened during the quant
Speaker 1: quake of of two thousand and seven. We're just about
Speaker 1: a decade past that, and so, uh, he was the
Speaker 1: one of the first people who really figured out how
Speaker 1: this happened, why it happened, and what it might mean
Speaker 1: going forward to the future of of short term trading
Speaker 1: and markets and companies like Lehman Brothers. It's it's one
Speaker 1: of those stories of someone who was unfortunately, much to
Speaker 1: his chagrin, proven right. He was kind of hoping he
Speaker 1: wasn't going to be right, but hey, that's what happened,
Speaker 1: and we have all sense lived with the consequences. So,
Speaker 1: with no further ado, my conversation with Matthew Rothman. My
Speaker 1: special guest today is Matthew Rothman. He is currently the
Speaker 1: head of Global Quantitative Equity Research at Credit Swiss. He
Speaker 1: is also a senior lecturer in Finance at the m I. T.
Speaker 1: Sloan School of Management. Prior to joining Credit Swiss, Matthew
Speaker 1: was the director of Global Quantitative mat Growth Research at
Speaker 1: a Canadian asset management in Boston, which was running approximately
Speaker 1: seventy billion dollars in assets. Before that, Matthew was the
Speaker 1: global head of quant research at Lehman Brothers and then
Speaker 1: continued on at Barclay's Capital after that acquisition post bankruptcy.
Speaker 1: He is the author of Turbulent Times in quant Lands,
Speaker 1: which was a research note during the quant crash in
Speaker 1: the summer of two thousand and seven that became the
Speaker 1: most highly distributed research note in Lehman Brothers history. Matthew Rothman,
Speaker 1: Welcome to Bloomberg. Welcome, Thanks so much for having me,
Speaker 1: Barry Um. So, I've been looking forward to this for
Speaker 1: quite a while. I knew of you from Scott Patterson's
Speaker 1: book The Quantz, and I was vaguely familiar with UM,
Speaker 1: the research piece that you would put out turbulent times
Speaker 1: in quant land. Let's let's start at a very basic
Speaker 1: level for the lay person. Please explain what quants strategies
Speaker 1: focus on. You know, so much gets grouped under the
Speaker 1: kind of rubric of quant today that you really kind
Speaker 1: of have to start to decompose it a little bit.
Speaker 1: And there are a variety of different quants, UM, you
Speaker 1: should begin to think about them via asset classes. Uh So,
Speaker 1: derivatives based quants are very different than fixed income general
Speaker 1: fixed income quants versus uh kind of equity quants versus
Speaker 1: risk modeling quants, and each one will come with a
Speaker 1: different kind of skill set and a different kind of
Speaker 1: approach to modeling. If you take equity quants just for
Speaker 1: a second, they also kind of come at a variety
Speaker 1: of forecasting horizons, and so they'll look at different types
Speaker 1: of signals and different types of things. So you have
Speaker 1: people who are playing literally in the millisecond range doing
Speaker 1: kind frequency very high frequency trading market making. Uh it
Speaker 1: literally in you know, trading hundreds of times in the
Speaker 1: blink of an eye, um down to people are holding
Speaker 1: intra day strategies, to people holding several day strategies, to
Speaker 1: people holding strategies at last months uh and and so
Speaker 1: you know, you can think about them having very different
Speaker 1: types of signals and very different types of performance, but
Speaker 1: what they all have in common is that they're forecasting returns.
Speaker 1: And what separates a quant in my book, really from
Speaker 1: a fundamental manager is that fundamental managers really try to
Speaker 1: understand the drivers behind the company. They talk to management,
Speaker 1: they think about products, They forecast earnings at the end
Speaker 1: of the day, and they think about a company as
Speaker 1: an organic unit. Quants think about returns and what are
Speaker 1: the drivers of returns. What is going to make h
Speaker 1: two returns, two stocks take the same way or go
Speaker 1: the opposite way over a long period time or baskets
Speaker 1: of returns, and so we we think about what drives
Speaker 1: returns more than anything and really abstract away from the
Speaker 1: companies themselves. So so I oversimplified, as the fundamentalists are
Speaker 1: telling the story and the qual are crunching numbers. Is
Speaker 1: that a gross of simplification or does it work? I
Speaker 1: think everybody crunches numbers. I wouldn't want to say the
Speaker 1: fundamentalists don't tell a story. Um, they're certainly, you know,
Speaker 1: trying to forecast cash flows and understand, uh, you know,
Speaker 1: what are the drivers of earnings and revenues uh, and
Speaker 1: then finally relate that back to us a stock price
Speaker 1: and what they think they're appropriate stock price would be.
Speaker 1: Quants don't try to do any of those things necessarily.
Speaker 1: They try to just forecast returns directly, uh, and see
Speaker 1: what can be those drivers of those returns and overall,
Speaker 1: for the most part, think about large baskets of returns
Speaker 1: or of stocks and how those characteristics and how those
Speaker 1: stocks behave based upon their return based characteristics. So you
Speaker 1: studied under Gene Fama, you got your PhD from Chicago,
Speaker 1: The really the home of the efficient market hypothesis? Can
Speaker 1: you square E M H with quantitative analysis? Are they similar?
Speaker 1: Or really? When I think of quants, I think of
Speaker 1: using powerful computers in order to try and beat the market.
Speaker 1: Or again, oh, you oversimplify. So I think the E
Speaker 1: M H is probably one of the most misunderstood concepts
Speaker 1: UM in finance. And Gene Fama's genius was that he
Speaker 1: really taught us how to think in a very rigorous
Speaker 1: way about what it means to be an efficient market
Speaker 1: and what it means to beat the market. Before Fama
Speaker 1: came along, there were people publishing studies all the time
Speaker 1: that said they had a strategy to beat the market.
Speaker 1: I think that drove Fama a little crazy, um, because
Speaker 1: the work wasn't very well done and the phrase beat
Speaker 1: the market um was very loosely applied. And what Farma
Speaker 1: really kind of taught us was that you have to
Speaker 1: think about risk uh and say, on a risk adjusted basis,
Speaker 1: can I beat the market? And then academics have debated
Speaker 1: for years what is the appropriate measure of risk? Is
Speaker 1: that the capital asset pricing model is that the Fama
Speaker 1: French three factor model. Uh? Is there something else that
Speaker 1: we're missing this now car hearts factor on momentum uh
Speaker 1: that is put in there. But academics have then debated
Speaker 1: are those factors anomalies or their proxies for risks? And
Speaker 1: you know, we spent fifty years more plus and academic
Speaker 1: circles debating what it means to beat the market with
Speaker 1: a risk adjusted return in Wall Street. Um. You know,
Speaker 1: there's been a generation of Chicago students and other students
Speaker 1: who have come to Wall Street uh, cliff as nous
Speaker 1: and crowd and many of the people's at Goldman's access
Speaker 1: at Management and then fanned out across the street in
Speaker 1: in in in many ways. Um. And what we've all
Speaker 1: kind of been trained in these methods, not only Chicago
Speaker 1: but other schools as well. And what we've really brought
Speaker 1: to Bears is kind of very hardcore, rigorous academic quasi
Speaker 1: academic background to how we think about can you make
Speaker 1: money as a quant and what does that mean? Uh?
Speaker 1: And and so you know, Wheel spend less time arguing
Speaker 1: about is something risk or is it a miss pricing?
Speaker 1: Is it an anomaly? Um? Doesn't that's that mean the
Speaker 1: market is efficient or less efficient? But you know, we
Speaker 1: we bring that same kind of sensibility that Fama taught us, UM,
Speaker 1: but we'll get less involved in the academic, you know,
Speaker 1: debate about risk versus miss pricing. So let's talk a
Speaker 1: little bit about building a quant team. You're hired at
Speaker 1: Credit Swiss to help put a team together what goes
Speaker 1: into that? How do you first begin to assemble a
Speaker 1: QUANT team. I think the first thing that you need
Speaker 1: if a quant, if you're going to be a quant,
Speaker 1: is a combination of data and technology. So you need
Speaker 1: to kind of go out and figure out what are
Speaker 1: the big databases that you need where you're gonna get
Speaker 1: your information and what is you're diversified information set going
Speaker 1: to be what you think you're edges and go about
Speaker 1: procuring that. So you're you're building hardware and software, You're
Speaker 1: hiring programmers, You're hiring programmers, you've got you have to
Speaker 1: hire data scientists and people who are going to really
Speaker 1: an overuse term, but people are going to really understand
Speaker 1: how to manage and curate and store your data. And
Speaker 1: then you have to find researchers who know what to
Speaker 1: really do with that data and where to find and
Speaker 1: where to find those hidden gems of signals and come
Speaker 1: up with ideas. And then you actually need people who
Speaker 1: can communicate it. So so this isn't anything that gets
Speaker 1: put together very quickly. This is a long processes, this
Speaker 1: is a long process. When when Credit Swiss comes to
Speaker 1: you and says, hey, Matthew, we want to build a
Speaker 1: quant team. Do you say, all right, it's gonna take
Speaker 1: five years, two years, How how do you put them
Speaker 1: into the proper mindset for this? I say, you've probably
Speaker 1: got to give me twelve to eighteen months and think
Speaker 1: that I'm going to be in a dark cave, um
Speaker 1: and you're gonna see nothing from me. And I'm gonna
Speaker 1: be asking you for big checks uh and and and
Speaker 1: hiring people and kind of layout a business plan very
Speaker 1: carefully uh and can detail the costs and exactly what
Speaker 1: I need. Um. And you got to make sure that
Speaker 1: they're in it and get and get the ask because
Speaker 1: it's a heavy ask, but what you can get out
Speaker 1: of it is pretty cool at the end of the day.
Speaker 1: So the competition for the really skilled fill in the
Speaker 1: blank programmers, researchers, data scientists. I think about the just
Speaker 1: a giant collection of PhD s at Renaissance Technologies, long
Speaker 1: before the rest of Wall Street started thinking in those
Speaker 1: terms that that's gotta be you said, big checks, that's
Speaker 1: gotta be a serious commitment made by the firm to
Speaker 1: to build something like this out. It is definitely a
Speaker 1: serious commitment by Credit sweet um uh. And they understand
Speaker 1: that much of the world is really moving this way.
Speaker 1: And from the firm's perspective, what I believe they understand
Speaker 1: is that we need to be able to deliver content
Speaker 1: um to those firms that you're mentioning. Uh, that is
Speaker 1: interesting to them. Uh. The way we deliver fundament to
Speaker 1: research to the biggest asset managers in the world out there,
Speaker 1: we need to deliver quantitative research along those same domains
Speaker 1: and so yes, it's a big ask if you're going
Speaker 1: to be additive to those people's process. Uh, and you know,
Speaker 1: play with them in the sandbox. Is it is it
Speaker 1: that competitive to hire people? I was joking a little bit,
Speaker 1: but I'm assuming that these folks are really in demand
Speaker 1: and there is no you know, you can't really do
Speaker 1: this on the cheap. I don't think that you can
Speaker 1: do this on the cheap. But you know, you need
Speaker 1: you need a relatively well sized staff. But you know,
Speaker 1: we're not going to be rentech. We don't think about that.
Speaker 1: You know, we don't need that size staff. No, no, no, no, no, no,
Speaker 1: no no no. That I you know, I think you
Speaker 1: need a staff of probably five to seven good researchers
Speaker 1: to be able to produce something interesting. Uh. You need
Speaker 1: a technology team of three to four people. Uh, you
Speaker 1: need a data team of probably another two to three people,
Speaker 1: um to really four people to really kind of begin
Speaker 1: to curate what you're doing. So it's you know, not
Speaker 1: crazy um in any sense, but you can be very
Speaker 1: productive and produce really interesting research on the cell side
Speaker 1: with that size team. So in one of your notes
Speaker 1: you mentioned quant one point oh um, referring to the
Speaker 1: quant quake of of the summer of two thousand and seven.
Speaker 1: What does quant two point oh and quant three point
Speaker 1: oh look like? What are the changes that that are
Speaker 1: taking place and will take place? So Quant one point
Speaker 1: I really ended I think in the summer of August
Speaker 1: two thousand and seven, where there were rather simplistic strategies
Speaker 1: that a lot of people were using, and we turned
Speaker 1: on the light in the room and saw everyone else
Speaker 1: who was there and realized that we needed to do
Speaker 1: things to diversify ourselves from each other. Um. And so
Speaker 1: we've seen that really over the past eight to nine years,
Speaker 1: where people really started to think in different ways, not
Speaker 1: even so much about uh forecasting returns, because I don't
Speaker 1: think we were all that similar there, but really about
Speaker 1: how we access liquidity in the market, how we optimize
Speaker 1: our portfolios, how we thought about risk, how we put
Speaker 1: you know, factors together. Could we time factors? Could we
Speaker 1: not time factors? How you incorporate macro information into your forecast?
Speaker 1: And so people really started to break the paradigm in
Speaker 1: a lot of ways. Uh, still within relatively traditional framework,
Speaker 1: but begin to really push that envelope, you know, kind
Speaker 1: of doing simple screening was no longer enough. So some
Speaker 1: of the criticism, and I'm pulling a line, this is
Speaker 1: actually an academic white paper, our our quansole fishing in
Speaker 1: the same small pond with the same tackle box, implying, hey,
Speaker 1: these were all crowded trades. Everyone was more or less
Speaker 1: using the same tools and pursuing the same goals. Was
Speaker 1: that true back then? And is it still true today?
Speaker 1: You know, it's one of the criticisms that get leveled
Speaker 1: at quant that infuriates me the most. Um. You never
Speaker 1: hear people say that to fundament to analysts. Right, you're
Speaker 1: all listening to the same press conference, You're are reading
Speaker 1: the same earnings report, you're all talking to the same
Speaker 1: investor relations person, so therefore you must all be the same.
Speaker 1: So I think it's one of those great misunderstandings about
Speaker 1: quant is that just because you look at the same
Speaker 1: data or studied under gene Fama, Uh, you must all
Speaker 1: be the same. Um. And let me kind of give
Speaker 1: you an example of how even quants can be different,
Speaker 1: even though on the outside they may look the same.
Speaker 1: So quants not surprisingly like to buy cheap things, um,
Speaker 1: and the hope that they'll go up in value. I
Speaker 1: really don't know any investor who likes to buy expensive
Speaker 1: things and think that it's going to go down in
Speaker 1: value high but sell high. But I don't know anyoneho
Speaker 1: wants to buy high and sell low. No, right, not
Speaker 1: a great strategy to make money. But when you're a
Speaker 1: quant and you say that you want to buy something
Speaker 1: that's cheap, well, you're programming that, and so you have
Speaker 1: to all of a sudden be really really precise ice
Speaker 1: on what you mean by cheap. Do you mean it's
Speaker 1: cheap on a pe ratio? Do you mean it's cheap
Speaker 1: on a book to price ratio? Do you mean it's
Speaker 1: cheap because of sales to price? What metric are you
Speaker 1: even using to define cheapness? Right, you've got to program
Speaker 1: that into the computer, and then you've got to say,
Speaker 1: do you mean it's cheap relative to its own history?
Speaker 1: Do you mean it's cheap on a sales surprice ratio
Speaker 1: compared to every other stock in the market. Do you
Speaker 1: want to do its sector relative? Do you want to
Speaker 1: do it country relative? What do you mean? Um? And
Speaker 1: God is in the details of a lot of these things. UM.
Speaker 1: If I'm gonna look at a book to price ratio,
Speaker 1: do I just book values for differences in gap standards
Speaker 1: in different industries or not? Do I? How do I
Speaker 1: correct for book value under i f R S accounting
Speaker 1: International accounting standards versus gap standards? Do? How do I
Speaker 1: handle all these things? So even a look like you're
Speaker 1: just doing the same thing, Oh, I'm using book to price,
Speaker 1: there can be a tons of details. Let's talk a
Speaker 1: little bit about that period of of oh eight oh nine,
Speaker 1: because you know it's almost ten years ago to the
Speaker 1: day when that weekend that shook the entire financial firmament
Speaker 1: took took place. I've read a couple of your older
Speaker 1: research notes, and I have to ask you the question
Speaker 1: what actually caused the financial crisis and market crash? You know,
Speaker 1: I think the great place um to start, as Andrew
Speaker 1: Ross Sorkin's book Too Big to Fail. If you're really
Speaker 1: interested in kind of the inner workings of Lehman. Uh
Speaker 1: during that time, he nailed it. Um, it's a great read.
Speaker 1: I couldn't put it down. My wife kept, you know,
Speaker 1: like nudging me, like put the book down. You've lived this,
Speaker 1: like why do you have to read this? And I
Speaker 1: was like, oh no, he's got details in here that
Speaker 1: like some of us were trying to find out. UM,
Speaker 1: research one into that you could see in the suffis
Speaker 1: he got access to and got people talking. That's really
Speaker 1: quite remarkable. Um. I think that there was definitely some
Speaker 1: level of mismanagement at the top, as as he documents, uh,
Speaker 1: not just Lehman Brothers, but across the board, but across
Speaker 1: the board, a misunderstanding of risk. Uh. And it's very
Speaker 1: hard to know when the music is going to stop,
Speaker 1: as it were, when successful businesses have run their course. Um.
Speaker 1: If you remember nine months back, Lehman Brothers was putting
Speaker 1: up record earnings. Uh, And so how do you know
Speaker 1: that it's time to get out of that business. It's
Speaker 1: a really hard call to make. So so let's talk
Speaker 1: about six or nine months back. I read and I
Speaker 1: don't think this was Sorkin's book. I think it was Patterson's.
Speaker 1: The Quants. You had submitted some memos to senior management,
Speaker 1: sort of saying, hey, guys, you gotta wake up. Is
Speaker 1: a ton of risk here, and it seemed like you
Speaker 1: had a sense there were problems coming long before much
Speaker 1: of the street figured it out. I think you're being
Speaker 1: overly generous to me on that one. I think that
Speaker 1: there were definitely things that concerned me. Did you ask someone,
Speaker 1: and again maybe this is Patterson's book, didn't you say, well,
Speaker 1: what what are the contingencies in case Leman goes bankrupt?
Speaker 1: And people laughed at you, They looked at you like
Speaker 1: you were crazy. Um, there were times that there were
Speaker 1: things going on that disturbed me. I'll give you I'll
Speaker 1: give you a little anecdote. There's a great paper by
Speaker 1: a professor um Owen Lamont at the Harvard Business School
Speaker 1: and used to be at the University of Chicago, and
Speaker 1: he did a study that found that firms who get
Speaker 1: into fights with their short sellers, like the time those
Speaker 1: firms end up going bankrupt. They're in trouble, nothing else
Speaker 1: to do but with the shorts like like stop right,
Speaker 1: you know, Um, and and he documents some of these
Speaker 1: and they're great anecdotes in there. And if you remember,
Speaker 1: towards the end of Lean and Brothers, UM Management got
Speaker 1: into a fight with one of our with David Einhorn.
Speaker 1: I believe UM, And when I yes, it was, it
Speaker 1: was contested. And I did send UM that paper along
Speaker 1: to senior people. And picture, wait, this guy sending me
Speaker 1: a Harvard Business School white paper and arguing with shorts.
Speaker 1: Doesn't he realize our very foundation is under assault. I
Speaker 1: could just picture the c suite response to that. UM
Speaker 1: was this head sending me a white paper? What is
Speaker 1: this except except saying, these people do this, You're scaring me. UM.
Speaker 1: I was lucky that my boss was a PhD from
Speaker 1: the University of Chicago as well. He appreciated his kind
Speaker 1: of things. Uh, he got it. Uh. And and I
Speaker 1: think we actually, you know, I don't want to say
Speaker 1: we stopped fighting with him, but we did stop fighting
Speaker 1: with him, and I think we did start to content
Speaker 1: rate on different things. We had some very talented, you're
Speaker 1: bright people there. You're literally on the way to London
Speaker 1: to a conference when you get a phone call. All
Speaker 1: you're in JFK. You get a phone call on the
Speaker 1: other side of security. Hey, tap out, you gotta come back.
Speaker 1: You go home to New Jersey. You take not your
Speaker 1: little car but your wife's station wagon with the presence
Speaker 1: of mind too. I gotta go clear up my office.
Speaker 1: And you're described as this lucid rational reason like you weren't.
Speaker 1: Oh what a shock, what a surprise. This seemed to
Speaker 1: be something that you apparently had thought out before, where
Speaker 1: most people more or less seem to be shocked or
Speaker 1: panicked or both. How do you and is that again,
Speaker 1: am I oversimplifying this? Or um? Well, I was certainly emotional. Um,
Speaker 1: I don't want to say that that wasn't a very
Speaker 1: emotional night for me. Um. You know, one of the
Speaker 1: things that I think, you know, behavioral economists and other
Speaker 1: people tell you, is that the closer you are to
Speaker 1: a situation, the harder it is for you to kind
Speaker 1: of take that step back rationally and see what's going on.
Speaker 1: A lot of Lehman management lived through ninety four and
Speaker 1: had lived through other crises, and really we're very, very,
Speaker 1: very close. I was relatively new at Lehman and so
Speaker 1: kind of had a little different perspective more objectivity than
Speaker 1: then they did. About the situation. I think that was
Speaker 1: part of part of the difference. Um and just being
Speaker 1: kind of a little just more unsentimental. Let's talk a
Speaker 1: little bit about the quant crash of two thousand and seven.
Speaker 1: I love the story about you and Austriel Levin figuring
Speaker 1: out what actually had happened long before anybody else. Uh.
Speaker 1: Was this over sushi or Chinese food in San fran sushi? Okay,
Speaker 1: it was a sushi dinner. Um. I've always felt badly
Speaker 1: that um ozreal Levine to his friends as known as susy.
Speaker 1: You know, he really should have been the co author
Speaker 1: with me on that paper and deserves every bit of credit. Um.
Speaker 1: What was he working at the time? What was the
Speaker 1: place called Menta Capital? He's still there. Uh. And he
Speaker 1: used to run b g I's hedge fund um main um,
Speaker 1: you know main hedge fund over there, and he had
Speaker 1: started on his own. Uh. And you know I'd have
Speaker 1: been out that day seeing clients and watching the blow
Speaker 1: up happening, and like we both were just sitting there
Speaker 1: over sushi, um, and like just kind of piecing together
Speaker 1: what would have caused everyone to unwind? Um? And it
Speaker 1: was literally just over sushi dinner. Just arguing it back
Speaker 1: and forth and kind of putting together what the story
Speaker 1: had to have been. So tell that story, because it's
Speaker 1: fascinating how you guys deduce leverage multi strat. So the
Speaker 1: story that we that that that that we kind of
Speaker 1: came up with um and still holds up to this day.
Speaker 1: No one's you know, we can't prove it, um, but
Speaker 1: no one has a better story UM. And you know,
Speaker 1: it's kind of become accepted wisdom is that there were
Speaker 1: a number of multi strat quantitative hedge funds that held
Speaker 1: positions in UH sub prime mortgage UH and fixed income
Speaker 1: mortgages of low credit that we're taking losses. This was
Speaker 1: in the summer of two thousand and seven where you
Speaker 1: had the managers at bear Stearns who were running those
Speaker 1: fixed income portfolio and trying to remember I don't remember
Speaker 1: the names, but that that was June that kind of
Speaker 1: wabble that that that started wobbling right and by in
Speaker 1: mid July you saw a number of other quant um
Speaker 1: You saw the fixed income credit distress. Credit market was
Speaker 1: in distress um and it was a liquid and people
Speaker 1: were beginning to receive margin calls on those on those books.
Speaker 1: They were, they were highly levered, and um Man and
Speaker 1: and and prime brokers and others were coming to people
Speaker 1: who who held those assets and said, we need more
Speaker 1: collateral so support those books. So highly levered and a
Speaker 1: liquid not a great combination and not a great combination.
Speaker 1: And the last thing you want to do if you're
Speaker 1: holding that portfolio is actually liquidate those assets because the
Speaker 1: marks aren't probably really at market. There at some discount
Speaker 1: discount to market. But when you try to move that,
Speaker 1: the mark is going to get set lower. As you
Speaker 1: try to sell in a liquid asset, right for that,
Speaker 1: you know it's going to be marked lower than the
Speaker 1: whole portfolio gets marked lower. You're gonna need to raise
Speaker 1: more collateral for the discount of the underlying asset. Sounds
Speaker 1: like portfolio, It sounds like exactly. And so the last
Speaker 1: if so, if you're smart and you realize this, you're
Speaker 1: not gonna if you to meet the margin call, you're
Speaker 1: not going to sell that asset. You're gonna go sell
Speaker 1: a very highly liquid asset because you're taking a much
Speaker 1: smaller haircut on that if any at all. Right, it's
Speaker 1: a liquid portfolio. Now, what is the most liquid assets
Speaker 1: in the world, Probably US large cap equities. So if
Speaker 1: you're a multi strat firm, where are you gonna go
Speaker 1: raise that equity? You're gonna go liquidate, And many of
Speaker 1: these were quants. You're gonna go liquidate your quant portfolio.
Speaker 1: And we saw that if you go back and look
Speaker 1: at the data, that a lot of the quants were
Speaker 1: losing money throughout most of July. A well known quant
Speaker 1: manager has come out and said, like, we lost money
Speaker 1: twenty one out of the twenty two days in July.
Speaker 1: But it wasn't just a kind of steady trickle, like
Speaker 1: it wasn't really bad. But then it really started to
Speaker 1: pick up momentum as it were in um August, and
Speaker 1: people started and we really think it's because the liquidations
Speaker 1: and the margin calls became much more severe, and other
Speaker 1: people were noticing that their being their portfolios were misbehaving,
Speaker 1: and so they started to take down who didn't have
Speaker 1: any exposure necessary to these subprime assets. They saw their
Speaker 1: quant portfolios not behaving that the way they wanted, and
Speaker 1: so they started to take down risk because their models
Speaker 1: were misbehaving, which is because it just shows you how
Speaker 1: inter related everything is. That's right, we don't have any
Speaker 1: subprime exposure doesn't matter. People who do a liquidating things
Speaker 1: that you have exposed to. And so that's how can
Speaker 1: that is a definition of contagion, right where something that
Speaker 1: you're not actually exposed to begins to affect another part
Speaker 1: of the market. And you and Oozy are putting this
Speaker 1: together pretty much in real time in early August. We're
Speaker 1: putting this literally together over a three to four hour
Speaker 1: dinner of sushi in a restaurant in California with some saki. UM,
Speaker 1: and you closed to join up there until they kicked
Speaker 1: this out, and we kind of you know, you know,
Speaker 1: we didn't exactly have the story. We couldn't prove it,
Speaker 1: but it all made sense, UM, and kind of got
Speaker 1: this story. And then I went back to my hotel room, uh,
Speaker 1: and realized that the rest of the trip that I
Speaker 1: was planning in California was out the window. What I
Speaker 1: needed to do was right this all up. And so
Speaker 1: that next morning I called and told all my sales people,
Speaker 1: cancel my trip, UM, cancel all my client meetings. UM.
Speaker 1: This is you know, I'm going into the San Francisco
Speaker 1: office and we're writing this note as our quant world
Speaker 1: is melting down. UM and you know, stayed up until literally,
Speaker 1: I mean I got there at you know, eight o'clock
Speaker 1: in the morning and published that note. Um walk. I
Speaker 1: remember walking back from the San Francisco office after I
Speaker 1: hit the send button on that note and knowing that
Speaker 1: I had done it was almost like the Jerry McGuire moment,
Speaker 1: like when you put that out there, kind of saying like,
Speaker 1: oh my god, what if I just hit the send
Speaker 1: button on um and woke up to the most read
Speaker 1: note in really the history of wurbulent times in quant Land.
Speaker 1: Really just the timing was perfect, and you guys figured out,
Speaker 1: if not the best explanation, certainly no one's come along
Speaker 1: with a better explanation, since I think people it is
Speaker 1: pretty much I think received as the explanation. So at
Speaker 1: this point, there's a line I'm not sure if this
Speaker 1: is from that or another one of your writings. Events
Speaker 1: that model only predicted what happen once in ten thousand years,
Speaker 1: happened every day for three days. So, in other words,
Speaker 1: loudly improbable things are happening way too frequently, right, I
Speaker 1: think that's gonna be on my tombstones, um um. And
Speaker 1: you know, some people have actually criticized me as not
Speaker 1: understanding that returns are not normally distributed. For that statement,
Speaker 1: of course, what I meant was that things were misbehaving
Speaker 1: on our models, and our models were misspecified and wrong.
Speaker 1: UM and obviously we did not have their appropriate distribution
Speaker 1: of returns. If that's what our models were saying. UM,
Speaker 1: I clearly understand statistics, and clearly it was it was.
Speaker 1: It was a pithy way of trying to say, our
Speaker 1: models are absolutely wrong. If that's what we're predicting and
Speaker 1: we're seeing them three days in a row, we don't
Speaker 1: understand what's going on. Our models are wrong. So so
Speaker 1: I love the expression all models are wrong, but some
Speaker 1: are useful. Um. And your models had previously proven to
Speaker 1: be useful. What was wrong with all of the quant models?
Speaker 1: Some people were blaming Gaussian Coppola's and other people were saying, no,
Speaker 1: this is strictly a subprime derivative c d O UM contagion.
Speaker 1: Where did the models? Where were the models off? I
Speaker 1: think where the models were off is in understanding liquidity
Speaker 1: UM wasn't appropriately kind of factored into that and notions
Speaker 1: of crowding. We're very very We're just not in the
Speaker 1: models to be honest with you, we didn't know how
Speaker 1: to think about that. We didn't know how to think
Speaker 1: about crowding risk, We didn't know really how to think
Speaker 1: about liquidity the way we do today. We held more
Speaker 1: concentrated positions at that time. While we might have only
Speaker 1: hold a fraction of average daily volume a d V
Speaker 1: and traded those carefully, we let those positions build up
Speaker 1: too much as a portion of our book um. We
Speaker 1: didn't spread the bets out enough across enough different stocks,
Speaker 1: and we ran with this way too much leverage. So
Speaker 1: so there's no doubt. Leverage is always a giant problem
Speaker 1: whenever there's headache. But you had done some subsequent research
Speaker 1: that found, hey, the correlations were much lower than everybody believed.
Speaker 1: Everybody that was talking about crowded trades assumed people were all,
Speaker 1: if not in the exact same investments, in such similar
Speaker 1: asset um holdings that it didn't make a difference. But
Speaker 1: you found the correlation was something around. What I tried
Speaker 1: to do was decompose why we were into crowded trades.
Speaker 1: So I don't think we're denying that quants were holding
Speaker 1: the same portfolios. The received wisdom was that it was
Speaker 1: because our return prediction models are alpha models were all
Speaker 1: the same that we were looking in this We were
Speaker 1: fishing for alpha in the same pond. And what I
Speaker 1: actually managed to do was convince the biggest quant firms
Speaker 1: out there that they should actually give me the outputs
Speaker 1: of their models for a period of a year. And
Speaker 1: and they did. And that's a lot of trust for
Speaker 1: I want to say, all right, here's the crown jewels.
Speaker 1: Try not to let anybody else get home. That was
Speaker 1: the relationships that I had with my clients was that
Speaker 1: they actually gave me the outputs to their models because
Speaker 1: we all thought this was a really important problem to
Speaker 1: figure out of what drove us into these same trades.
Speaker 1: And what I saw was that the actual outputs of
Speaker 1: the models weren't all that correlated. It wasn't an alpha
Speaker 1: modeling problem. People, as because we talked about before, have
Speaker 1: different ways of predicting returns. If you and I were
Speaker 1: to say, what is the stock that's gonna have the
Speaker 1: highest return over the next you know, six months, or
Speaker 1: ask your listeners, right, there'll be a lot of people
Speaker 1: would have very different opinions on a stock like Netflix
Speaker 1: or tesla Um or Amazon Um, any of the fang stocks,
Speaker 1: any of those kind of things. We might all have
Speaker 1: very different forecasts, but if we were to ask what
Speaker 1: are the most risky stocks, could probably list a lot
Speaker 1: of those names. The dispersion and outcome. We don't know
Speaker 1: which way it's gonna go, but we can agree it's risky.
Speaker 1: We have been speaking with Matthew Rothman. He is currently
Speaker 1: the head of Quantitative equity Strategies at Credit Swiss. We
Speaker 1: love your comments, feedback and suggestions right to us at
Speaker 1: m IB podcast at Bloomberg dot net. You can check
Speaker 1: out my daily column on Bloomberg View dot com or
Speaker 1: follow me on Twitter at rit Halts. I'm Barry Hults.
Speaker 1: You're listening to Masters in Business on Bloomberg Radio. Welcome
Speaker 1: to the podcast, Matthew. Thank you for being so generous
Speaker 1: with your time. I find this stuff fascinating. I was
Speaker 1: about to ask you, Um, you mentioned Andrew Ross Sorkin's
Speaker 1: book Too Big to Fail. Um, do you read Patterson's
Speaker 1: book The Klantz? I did, oh uh not. I found
Speaker 1: it fascinating because I love the characters. It's it's all
Speaker 1: my favorite Asses and Sam, Jim Simons of Renaissance and
Speaker 1: Ed Thorpe. You're mentioned in it. A number of people
Speaker 1: are in it. I found that to be a really
Speaker 1: fascinating tale. What what was your take on that? I'm
Speaker 1: to apologize for a no, no, no, it's fine. Um
Speaker 1: my frustrations with the book or that I found it
Speaker 1: a little overridden and a little over sensationalized. So here's
Speaker 1: what I have to I have to throw your own
Speaker 1: words back at you. Are you closer to that narrative
Speaker 1: than you are to the Sorcan narrative? Maybe I read
Speaker 1: I you know, I know some of the characters in there,
Speaker 1: and um, you know some of them do have tempers.
Speaker 1: But like you know, I read the poker game that starts.
Speaker 1: It's when I was looking at them, like what is
Speaker 1: this about? Out? It was yes, and you know we
Speaker 1: those of us who are close to it, and you see,
Speaker 1: I mean there's a detail in the book, just for
Speaker 1: as a small example that drives me crazy, where Scott
Speaker 1: has me coming off a red eye flight from New
Speaker 1: York to San Francisco. No, it's the other way around.
Speaker 1: There are no red eye flasks. It's San Francisco to
Speaker 1: New York, and it's it's barely a red eye because
Speaker 1: it's five and a half hours, right, and it's like
Speaker 1: you're going to London and it's eight hours, you can exactly,
Speaker 1: and so and so. It's just little things like that
Speaker 1: which you just pick up and you're like, he's got
Speaker 1: the details rock. And when someone starts getting the details
Speaker 1: wrong on little things that are so obvious, you start
Speaker 1: distrusting some of the other stuff where it's harder, where
Speaker 1: it's harder to see you. You're bursting my balloon. I
Speaker 1: just love that book so much, but I can understand
Speaker 1: it's someone who was so close to the story and
Speaker 1: so close to the characters that you see the embellishments. Sure,
Speaker 1: so and and if you know some of these people,
Speaker 1: if you know so, how I know Cliff Astness today
Speaker 1: versus that book. There are two very different characters. Like
Speaker 1: the ass. This character in the book is a little
Speaker 1: harder and a little like I know him as this
Speaker 1: this mischievous guy with a wicked wit. I mean, he's
Speaker 1: just outright hilarious. He's a funny guy. He's he has
Speaker 1: a great sense of humor, great personality. Doesn't come across
Speaker 1: that way in the book, you know, he he is
Speaker 1: um little hard ass in the book. You know, I'm
Speaker 1: not saying that he's look at the company he's built
Speaker 1: and all of those things. I'm sure he drives people
Speaker 1: to produce results. I would expect nothing else of a
Speaker 1: multi billionaire, uh, you know who had the vision to
Speaker 1: build the you know, the incredible company that he and
Speaker 1: his partners have built. Um. But you're right, the charming
Speaker 1: side the Cliff does not come through that. The witty,
Speaker 1: the you know, hilarious UM, charismatic um, you know, part
Speaker 1: that makes Cliff the legend that he is unshine through
Speaker 1: in that book. And so like those are the types
Speaker 1: of things that that that that bother me um about it.
Speaker 1: But it's what's fascinating about is to me is is
Speaker 1: this thread that runs through the throughout that whole book.
Speaker 1: And we'll get two books a little later. How quant
Speaker 1: was sort of disdained in the people like almost I
Speaker 1: don't want to say laughed at, but kind of like
Speaker 1: you put the numbers geeks in the basement. We're actually
Speaker 1: running a real firm here. It almost starts like that
Speaker 1: and ends up in oh Klan is taking overall the
Speaker 1: Wall Street and you people who didn't understand or appreciate it, well,
Speaker 1: you missed the bus and here's the next big thing.
Speaker 1: But that thread is fascinating and I think that's even
Speaker 1: more true today, um than it ever has been. Um.
Speaker 1: You know today you shouldn't be putting EXCEL on your resume. Uh.
Speaker 1: You know you know that you know just a word, right,
Speaker 1: you know that's a given. Like you know today, if
Speaker 1: you want to stand out, you know, you better be
Speaker 1: talking about how you can program and Python in our uh.
Speaker 1: And you know, you know, you know all those sets
Speaker 1: of skills that you that you have, you know, if
Speaker 1: you really want to be successful. So I think on
Speaker 1: Wall Street today and kind of going forward. So before
Speaker 1: I get to the standard questions, there are a couple
Speaker 1: of things I missed that I want to come back to. UM,
Speaker 1: and I have to stop saying, um, halt h O
Speaker 1: l T is a pretty substantial product at Credit Swiss.
Speaker 1: Can can you explain exactly what that is? Because when
Speaker 1: I started researching and I'm like, wow, how have I
Speaker 1: never heard of this? This is Uh it's a great product. Yeah, no,
Speaker 1: it's a great product. Um, you know it's not part
Speaker 1: of my domain. UM, there's a there's a team there
Speaker 1: that has been doing and that's been together for close
Speaker 1: to twenty five years, maybe more than that. Pretty successful,
Speaker 1: very successful uh. And what they've really done is collected
Speaker 1: accounting data for companies over that plus a year period
Speaker 1: and figured out how to normalize it uh and really
Speaker 1: begin to look at companies across different industries and different
Speaker 1: countries and put them all on an equal footing um
Speaker 1: uh and then really look at what a take those
Speaker 1: cash flows and look what really is the return on
Speaker 1: capital um for these companies and the implied growth rates
Speaker 1: for them and kind of come back and then look
Speaker 1: at what is then being implied for what the appropriate
Speaker 1: stock valuation should be. And so it's a wonderful tool
Speaker 1: that people that's very interactive UM and that people can
Speaker 1: really kind of compare companies all across the globe really
Speaker 1: on an apples to apples basis and look at it
Speaker 1: from a fundamental accounting perspective. It's very very powerful and
Speaker 1: has a wide following across the investor basis, and I
Speaker 1: assume a lot of people just are unfamiliar with it.
Speaker 1: I was looking at it saying, how have I not
Speaker 1: seen anything mentioned of this in the media. It was
Speaker 1: pretty uh. Up with a trial fe like, yeah, we'll
Speaker 1: set you up with the trial. Anytime you like I
Speaker 1: could get lost in that, I'll have my head of
Speaker 1: restart something like that. So since the quant crash and
Speaker 1: oh seven, we've seen two really interesting changes in the market.
Speaker 1: One has been, I don't want to call it the
Speaker 1: rise of indexing because that's been going on for forty years,
Speaker 1: but certainly a broader mom and pop imbraceive indexing. And
Speaker 1: then second, at the same time, really volatility has fallen
Speaker 1: off the off the cliff. How have those two factors
Speaker 1: impacted the ability for quants to make money in the market. Yeah,
Speaker 1: I mean I the way I have really kind of
Speaker 1: understood the rise of indexing and then probably not unique
Speaker 1: in my insight here is that in two thousand and eight,
Speaker 1: what you would really investors in two thousand and nine
Speaker 1: really had hoped for was managers that were going to
Speaker 1: be able to give them some level of insurance and
Speaker 1: and protect them in those moments in time, and that
Speaker 1: just didn't happen. UM. And so I think people have
Speaker 1: been driven by lower fees UH, and I think the fiduciaries,
Speaker 1: the planned sponsors who are managing UH many of the
Speaker 1: retirement accounts UH and pension funds have been disappointed by
Speaker 1: that facts as well. And so you've seen this move
Speaker 1: towards lower fee uh types of investing that can deliver
Speaker 1: you, you you know, what seems like to be the same
Speaker 1: kind of outcome for for for a lesser price. UH.
Speaker 1: And and so investors have definitely flocked that. And you've
Speaker 1: seen even this past year, the funds that have actually
Speaker 1: attracted the greatest inflows have not only been passive, but
Speaker 1: the absolute lowest priced passive UM funds. So even within
Speaker 1: low fee, it's been the absolute lowest fee that have
Speaker 1: attracted inflows. I remember some years ago morning Star did
Speaker 1: the study. Now their bread and butter is the Star
Speaker 1: rating system. They do this this study that said, if
Speaker 1: you can only know one thing about a fund, what
Speaker 1: should it be? And the answer was fee. If you
Speaker 1: just forget everything else, just by the lowest fees net
Speaker 1: net on average, you're gonna end up with the best performance.
Speaker 1: And and warning Stars credit Not only did they publish it,
Speaker 1: it's still up on the website. You go see it.
Speaker 1: It sort of argues ignore the stars, just look at fees.
Speaker 1: But this is an academic there's a whole bunch of
Speaker 1: academic research out there UM that has been absolutely making
Speaker 1: making that point. Um as well for a number of years,
Speaker 1: you know, Uh, Professor Gruber down An n y U
Speaker 1: has published some of the Gruber Gruber um some of
Speaker 1: the seminal studies on that as well, which and and
Speaker 1: and and others have. Um, he's not alone in that.
Speaker 1: But um, that really kind of made the point that
Speaker 1: fees low fees are one of the biggest predictors of
Speaker 1: future outperformance. Wow. That that's that's pretty Uh, that's pretty significant. Um.
Speaker 1: There's a line you haven't in some of your no,
Speaker 1: and I just love this. I have to share this. Um,
Speaker 1: you must have the right dictionary. If a trader in
Speaker 1: an instant message rights this is a dog with fleas,
Speaker 1: you need to know what they're really saying. UM. Much
Speaker 1: less if they're saying I'm doing market research, that just
Speaker 1: means they're watching YouTube videos. So so what is this
Speaker 1: is a dog fleas means I don't want to touch this,
Speaker 1: I want nothing to do with it. Yeah, it means
Speaker 1: this is a bad stock. Don't don't don't don't own it? Right,
Speaker 1: Um you know? Um? Or yeah, it is trade or
Speaker 1: speak for you know what are you doing market research?
Speaker 1: You're watching YouTube? Um? Where this comes up um is
Speaker 1: that there's a whole new field in in finance, and
Speaker 1: not that new um um, but it's really taken um
Speaker 1: getting a lot of momentum over the last five seven
Speaker 1: years of trying to understand text UH and parsing text
Speaker 1: and trying to understand the meaning within and what people
Speaker 1: are saying. So, whether it's reading earnings transcripts or reading
Speaker 1: annual reports UM, or reading news in general, or from
Speaker 1: a compliance perspective, if you're just trying to read instant
Speaker 1: messages and so the question is how do you begin
Speaker 1: to understand context and what people are really saying. And
Speaker 1: so if you're reading trader speak, your dictionary of words
Speaker 1: to try to understand what our trader is saying is
Speaker 1: different than if you're reading a novel. So so let's
Speaker 1: talk a little bit about that. Because one of the
Speaker 1: questions I didn't get to how to do with machine
Speaker 1: learning and artificial intelligence, which and then throwing big data.
Speaker 1: These are burgeoning areas for research, not just for quant trading,
Speaker 1: but for everything. Big data is now devouring the whole world.
Speaker 1: How do you interact with artificial intelligence and machine learning
Speaker 1: when it comes to figuring out what is out in
Speaker 1: the world and translating it to an expressible investment theme. So,
Speaker 1: you know, I think this is a really exciting time
Speaker 1: to be a quant because the word world is becoming
Speaker 1: more and more and more and more digitalized every day,
Speaker 1: and we are able to get our hands on data
Speaker 1: sets as quantitative investors that we could only dream of, um,
Speaker 1: you know, five seven years ago. And so the real
Speaker 1: question is that you have these huge data sets, how
Speaker 1: do you begin to process them? Uh and look for
Speaker 1: signal within them? Uh? And so you've really seen To
Speaker 1: make that happen, you've had to have two other revolutions
Speaker 1: that have had to come along at the same time.
Speaker 1: One is that computing power and in general hardware and
Speaker 1: software has had to just kind of go through a revolution. Uh.
Speaker 1: And we've seen that um exponential increases in computing. At
Speaker 1: the same time, the price just fell off the cliff
Speaker 1: for storage. Right, So we can go to the web
Speaker 1: on you know, a WS, Amazon Web Services or Zure
Speaker 1: or other places our there Google um uh, and you
Speaker 1: can buy you know, terabytes, tens and tens and hundreds
Speaker 1: of terabytes a storage extraordinarily cheaply rent it when you're
Speaker 1: done just kind of you know that's it. That's all
Speaker 1: you need um uh you can have There's that we've
Speaker 1: moved now from processing uh power from processing on CPUs
Speaker 1: UM in computers where everything had to be done in
Speaker 1: a hierarchical structure, but we've now rewritten the code so
Speaker 1: that everything can run in parallel UM and use GPUs
Speaker 1: graphical processing much faster in tandem cheaper UM. And so
Speaker 1: you've seen exponential growth um uh in computing power that
Speaker 1: is really really hard to overstate UH. And it's just
Speaker 1: been accelerating even that over the past you know, eighteen months.
Speaker 1: So we're we're just beginning to understand and unlock the
Speaker 1: horizon here. And so this has allowed you to just
Speaker 1: process amount ounce of data UM that is hard to imagine.
Speaker 1: An example, UM, there is a company out there that
Speaker 1: is now literally taking pictures of the entire globe every
Speaker 1: day at three meter resolution and storing that data for you.
Speaker 1: You could say, see the tiniest changes anywhere, right, Well,
Speaker 1: three m resolution is what the law allows, right, so
Speaker 1: a car, so but you can tell whether that's a
Speaker 1: car or a bus, right, UM. We can't see people,
Speaker 1: um UM. But the storage on that is a Yoda bite.
Speaker 1: How big is a Yoda bite, It's a trillion terra bites. Right,
Speaker 1: So how do you can now now process that data? Right?
Speaker 1: That requires a lot of computing power and a lot
Speaker 1: of storage capabilities. That's now economical to do, where five
Speaker 1: years ago that was a pipe dream. That's amazing, you know.
Speaker 1: I just I'm familiar with Ways, which was bought by Google,
Speaker 1: which uses Android phones to to look at traffic. I
Speaker 1: was just reading about a company that uses cell phone
Speaker 1: signals to identify actual foot traffic in malls and they
Speaker 1: identified way early that retail was in trouble before it
Speaker 1: was front page news. Amazon's doing this and this company's
Speaker 1: series is in trouble with that. They had figured it
Speaker 1: out and it costs hundreds of thousands of dollars a
Speaker 1: year told the services sometimes right, but you know, if
Speaker 1: you know two years in advance, hey, by the way,
Speaker 1: retail is about to fall off a cliff, it will
Speaker 1: pay for itself in Uh yeah, I mean the data
Speaker 1: sets that, like I said, that are available are really
Speaker 1: quite remarkable. Um that people have in terms of literally
Speaker 1: where you your your foot traffic, UM, your credit card
Speaker 1: spending data, UM, reading email receipts UM. You know, that
Speaker 1: that are available. As you said, traffic data. UM. It's
Speaker 1: there's really if you're if you if there's a data
Speaker 1: set that you want, UM and you don't think that
Speaker 1: you can get it, you're not looking hard enough. Uh.
Speaker 1: At this point, we can track literally every bill of
Speaker 1: lading for every cargo container that is coming into the
Speaker 1: United States. Really at T plus one tomorrow time, I
Speaker 1: I can, I can. For planes are crazy that you
Speaker 1: could see every plane in the sky. You can see
Speaker 1: where they're coming from, where they're going, what type of
Speaker 1: plane it is. You can ask Syria on your phone,
Speaker 1: tell me the planes that are above my head and
Speaker 1: she'll tell you. Really, I'm gonna have to try that.
Speaker 1: You know. You literally just asked Siri what plane is
Speaker 1: above me and she'll tell you the planes that literally
Speaker 1: are right about I mean. So, so all this data
Speaker 1: UM is you know what people are beginning to look at,
Speaker 1: and it's a bit of an arms race UM for
Speaker 1: everybody to try to keep up with this UM and
Speaker 1: to try to understand what's out there and how you
Speaker 1: process it because there's probably no one data set it's
Speaker 1: going to give you the you know, the holy grail
Speaker 1: of everything. It's really about how you take all these
Speaker 1: disparate data sets combine them h in a thoughtful manner
Speaker 1: that's really going to give you your answers uh to
Speaker 1: do that? Interesting? Alright, So I only have you for
Speaker 1: another ten or fifteen minutes. Let me get to my
Speaker 1: favorite questions. Well, I was going to ask you what's
Speaker 1: the most important thing people don't know about your background?
Speaker 1: But I suspect you've already answered that. Actually I don't
Speaker 1: think I have. So it's not Springsteen. It's not Springsteen.
Speaker 1: Springsteen is very important to my background. Um, But people
Speaker 1: know who know you know that, people who know me
Speaker 1: know that. And actually Springsteen lyrics are always pretty much
Speaker 1: hidden somewhere in my notes. Like if you're a Springsteen
Speaker 1: fan and you read my notes, you'll capture you'll find
Speaker 1: another you'll find a hidden Springsteen reference in there. Um,
Speaker 1: And sometimes they're not so subtle. UM. But what people
Speaker 1: probably don't unders know about me is that I was
Speaker 1: very lucky, um to be born to two academics who
Speaker 1: teach at Columbia UM. And my father was a professor
Speaker 1: I Guess is professor of American social history and really
Speaker 1: kind of founded the field um of American social history
Speaker 1: UM and taught me UM at a very young age
Speaker 1: UH to be questioning and dubious of your sources UM.
Speaker 1: And so when I was ten, he dedicated a book
Speaker 1: to me UM and it's called The Sources of American
Speaker 1: Social History UH. And it says to Matthew to to
Speaker 1: understand that American history is more than the study of
Speaker 1: great people UM. And it's a book of unconventional sources
Speaker 1: that try to study how institutions work. UM. And study
Speaker 1: history as a study of institutions UM, not as acts
Speaker 1: of Congress or acts of war or what great people
Speaker 1: are doing. But study the church, UM, study the prison,
Speaker 1: study the hospital and the experience of people within those
Speaker 1: setting UM. And understand the biases of these sources and
Speaker 1: look for unconventional sources. And so I like to think
Speaker 1: that that kind of training about data UH was embedded
Speaker 1: into me at a very very very early age. And
Speaker 1: looking for things UM and biases and things and being
Speaker 1: skeptical of the wisdom you're receiving of what you're being told, UM,
Speaker 1: how markets actually work, the players in them. All of
Speaker 1: that was really instilled at me from age ten to eleven.
Speaker 1: So you're an M. I. T. Darren Asamoglu is there
Speaker 1: and he talks about the role of institutions in the
Speaker 1: economy and people shouldn't be looking at these big events
Speaker 1: or these fed chiefs, should be looking at these societal institutions.
Speaker 1: They have a much greater impact on things like economic
Speaker 1: inequality and recessions and cycles than does anyone person or
Speaker 1: anyone sort of event. Very much along your dad's along
Speaker 1: those lines, um, And I think you know, I think
Speaker 1: you know, I know. One of your questions to me,
Speaker 1: I don't mean to jump the gun on any of
Speaker 1: your questions is favorite books. So let's let's jump the gun.
Speaker 1: Let's well, before we do that, let's because I'm anticipating
Speaker 1: the answer to who were your mentors? I have to
Speaker 1: assume your father was a mentor of yours, of course,
Speaker 1: of course, I mean, um, that's a little cliche to
Speaker 1: say that your dad was a mentor. Well, but you know,
Speaker 1: someone dedicates a book and it it obviously, of course. Um.
Speaker 1: And my father was absolutely influential in my life and
Speaker 1: my thinking and teaching me how to write and just
Speaker 1: taking a red pen to my writing and just um,
Speaker 1: you know, arguing with me about ideas. And um, I
Speaker 1: had a professor at college who was a huge mentor
Speaker 1: of media to me. I went to Brown advisor. UM.
Speaker 1: I did a lot of independent studies with him, who
Speaker 1: just grilled me on ethics and rigor of thought and
Speaker 1: uh the law uh and you know what are what
Speaker 1: are rights versus nonconstitutional rights and just took it in
Speaker 1: his wing and really shaped my thinking, uh in in
Speaker 1: in many many hard ways. UM. I think some of
Speaker 1: my other um you know mentors, uh you know had
Speaker 1: to have been a guy by the name of Sid Brown,
Speaker 1: um who's who was a professor at Columbia who saw
Speaker 1: something in me. When I was a master student there,
Speaker 1: I wandered into uh his graduate class, graduate students class
Speaker 1: and stochastic calculus uh you know, and uh you know
Speaker 1: it was filled with PhD students and somehow I got
Speaker 1: the high grade. I'm still to this day not sure
Speaker 1: how I did that. And he took me under his wing,
Speaker 1: um and taught me a lot. And now he's a
Speaker 1: friend and colleague and just been entrusted um kind of
Speaker 1: mentor and advisor throughout my career. UM. And then there
Speaker 1: was a gentleman at Lehman Brothers UM, where I was
Speaker 1: much too young in many ways to get the position
Speaker 1: I did as head of quant. Uh. Um I had
Speaker 1: been uh and I had been out of my PhD
Speaker 1: program all of five years. Oh really, so you're a
Speaker 1: little green. I was a little green. And he threw
Speaker 1: me into as a managing director ahead of all the
Speaker 1: quantitative equity research at Lehman five years out of my
Speaker 1: PhD program. Um, and so I was green. Um. I
Speaker 1: didn't quite you know, know how to behave with other
Speaker 1: senior managing directors and how that whole world worked. Um.
Speaker 1: And Robbie was absolutely you know, harsh um uh in
Speaker 1: brutal and uh to me, but in the loving way.
Speaker 1: I was one of Robbie's children, as the way I
Speaker 1: described it in um. Uh you know, he taught me
Speaker 1: how to grow up um and how to behave in
Speaker 1: a major, world class institution and what was expected of
Speaker 1: of me not so much. I mean he helped me
Speaker 1: on the quant but really helped me mature as a
Speaker 1: manager um and as a person. Uh and how one
Speaker 1: carries oneself uh in a role. And I remember kind
Speaker 1: of telling my team every day like I don't know
Speaker 1: what I did wrong um this week before my weekly
Speaker 1: meeting with Robber. But but I'm about to go find
Speaker 1: out and UM, I don't know when I found out. UM.
Speaker 1: Uh and he and it was painful at times, UM,
Speaker 1: but I absolutely love him for it. UM. And he
Speaker 1: made me such a better UM workplace person, uh you
Speaker 1: know every day and uh you know, uh it was
Speaker 1: it was hard at the time, but I I adore
Speaker 1: him for taking that time and attention. UM everybody. You
Speaker 1: you know, if he's listening to this, you know everyone
Speaker 1: needs them two you know. So let's talk you mentioned books.
Speaker 1: Let's talk about some of your favorite books, fiction, non fiction.
Speaker 1: What do you read for fun? What do you read
Speaker 1: for informational purposes? So? Uh, I love documentary photography. I
Speaker 1: am a huge fan of that, and I collect um
Speaker 1: documentary photography books. I have a pretty extensive collection, UM.
Speaker 1: And I am always on the hunt for that new
Speaker 1: great documentary photography book UM or and collecting the masters.
Speaker 1: Uh give us the name of a book for someone
Speaker 1: who has no experience with documentary photography but wants to
Speaker 1: have splore the space. So you have to start post
Speaker 1: World documentary Photography with Robert Frank Um the Americans. UM.
Speaker 1: That was an absolutely revolutionary book. UM. Along with Cardi
Speaker 1: A Brasson the decisive moment UM. And but but but
Speaker 1: Frank changed photography UM forever UM. And then there comes
Speaker 1: a whole series of lesser known masters UM, but utter
Speaker 1: masters UM from you know, Eugene Richard's work out there
Speaker 1: documentary UM poverty in America. That is one has to
Speaker 1: be aware of and see um uh to the work
Speaker 1: like people of ron have viv that is just legendary. UM.
Speaker 1: His photographs were actually submitted into the War Crimes Tribunal
Speaker 1: UH in the Hague UM documentary the atrocities um uh
Speaker 1: you know that happened in the UM you know, and
Speaker 1: in what was Yugoslavia, UM. You know, just the some
Speaker 1: of the most important work. There's work by people like
Speaker 1: Don McCullen, the legendary British war photographer, documenting the atrocities
Speaker 1: in Biafra and Vietnam and around the world UM, heroic people. UM.
Speaker 1: Tim Heatherington's doing work that unfortunately he died UM, covering
Speaker 1: UH in Africa and Libya. And these are just um,
Speaker 1: you know, moving work. Of course, the work by Robert Kappa,
Speaker 1: the legendary war photographer who was in the first wave
Speaker 1: of the boats UM coming off of d day. Um.
Speaker 1: So just um, very very variable, powerful work. And there
Speaker 1: are people who are still doing this work today, um
Speaker 1: out there that just don't get any love uh and attention.
Speaker 1: People for like Alex Webb at Magnum, Ed Kashi um
Speaker 1: at you know seven. Uh there's I don't I'm just
Speaker 1: missing people. James Knockway. Uh, you know, truly legendary. Listen.
Speaker 1: I'll make sure it gets included with just on this.
Speaker 1: I could go on. I have hundreds and hundreds of
Speaker 1: about something, a little something, a little lighter than than
Speaker 1: what else do you read? What do you read for enjoy,
Speaker 1: for just pure relaxation. Um. You know you're talking to
Speaker 1: a guy who just loves quant and loves getting quant finance. Um.
Speaker 1: I'm really into the work right now by a guy
Speaker 1: by name of Jarren Lanier. Um. He's the philosopher in
Speaker 1: chief um at Microsoft. Okay I knew yeah. And he's
Speaker 1: written this great book called You Are Not a Gadget.
Speaker 1: Um Uh and he's really thinking very hard about the
Speaker 1: role of human beings and artificial intelligence uh in this
Speaker 1: big data world. Um. There's just great stuff uh to
Speaker 1: read out there. Um. There's other work U, A great
Speaker 1: book called Behave by Richard Seboski. Um. So it's it's
Speaker 1: a big book. It's a big book somebody else recommended
Speaker 1: and I actually picked it up not too long, you know,
Speaker 1: just really I mean it's not light stuff. Um. But
Speaker 1: it's really beginning to explain how what is you know,
Speaker 1: getting too these arguments of what is nature versus what
Speaker 1: is nurture? And how do we learn? And how do
Speaker 1: human beings change behavior? Um? And you know how inbred
Speaker 1: is things like violence into our societies or not, and
Speaker 1: studies examples coming from um baboons and how baboon's learned
Speaker 1: and you think baboons are very um have this ingrained,
Speaker 1: but they reached these tipping points where societies really changed.
Speaker 1: And so um, really getting uh at at at this work. Um,
Speaker 1: that's that's just fascinating. Any any fiction or is it strange? Oh? No,
Speaker 1: I love fiction. I'm a huge fan of Paul Auster.
Speaker 1: Um what's the book. Uh, he's written this great series
Speaker 1: of books and starting books out there called the New
Speaker 1: York Trilogy. But he's written a great book called Invisible
Speaker 1: and he kind of again it's kind of postmodernist fiction, um,
Speaker 1: where you always kind of have to figure out what's
Speaker 1: the story. He writes them as detective stories, but they're
Speaker 1: much deeper than that. I love the stories by Raymond Carver.
Speaker 1: I wish I could ever write like Raymond Carver short
Speaker 1: stories or Richard Ford. Um, the poetry of Marie How Um,
Speaker 1: you know, it's what the living do is amazing. Um
Speaker 1: you know there's uh so, there's just a lot of these,
Speaker 1: uh just wonderful books uh out there you're you're on
Speaker 1: a lot of planes, I am, um, but you just
Speaker 1: find time to read. Well if if you if you
Speaker 1: want to read a book, you have to carve out
Speaker 1: a specific time. Otherwise it's just not going to happen.
Speaker 1: There are too many of the distractions. Yeah, you know
Speaker 1: the TV. You know, just keep the TV off. Don't
Speaker 1: get addicted to the Law and Order SVU. Actually, this
Speaker 1: is a good list. I think people are gonna have
Speaker 1: some good feedback about this. Um So, we've talked about
Speaker 1: the arc of quant and how it's changed. Um So,
Speaker 1: I don't know if I have to ask you what's
Speaker 1: changed since you joined the industry, But what might be
Speaker 1: a little more insightful for listeners is what changes do
Speaker 1: you see just beginning to happen. Now, what are the
Speaker 1: next major shifts that are already underway and we're just
Speaker 1: not aware of them. Um. I think it's really this
Speaker 1: quant three point oh um as as I call it,
Speaker 1: which is really beginning to understand these disparate data sources
Speaker 1: that are out there and how we begin to use
Speaker 1: them and incorporate them into an investment process. Uh. And
Speaker 1: you know where, you know where that data is useful
Speaker 1: and where that data isn't useful. And I think a
Speaker 1: little bit of what's been happening in the industry has
Speaker 1: been putting the horse before the cart, where there is
Speaker 1: this explosion of data as we were talking about, and
Speaker 1: at this point it's just so cool, like you can
Speaker 1: literally if you can imagine it, you can get the
Speaker 1: data for it. Um. But we also need to slow
Speaker 1: down and start thinking about it from an investor's perspective
Speaker 1: of what is the data that I need that's going
Speaker 1: to help me with solve my investment controversy um and
Speaker 1: kind of turn things back on its head um. And
Speaker 1: that's what I'm hoping the industry will start to do
Speaker 1: and not just be data for data's sake and just
Speaker 1: consuming these vast amounts of it UM and ware housing it,
Speaker 1: but really think what's the what's what's the crux of
Speaker 1: the question, what's the controversy? See? And then how do
Speaker 1: I go and get that data? UM? And and that
Speaker 1: can be in so many different forms of How do
Speaker 1: I read text that I want to figure out what
Speaker 1: are people talking about? UM? How do I read UM?
Speaker 1: How do I understand body language from reading the text
Speaker 1: of an earnings transcript? How do I infer even like
Speaker 1: the big questions and natural language processing is, how do
Speaker 1: I look at double negatives or triple negatives? How do
Speaker 1: I begin to infer what you mean versus what you
Speaker 1: actually said? Like we can do that as humans. Sometimes
Speaker 1: sometimes a written word like on not that twitter is
Speaker 1: has anything to do with the real world, but I
Speaker 1: noticed that sarcasm or snark very often gets lost in
Speaker 1: the written word from from some intentions it does, UM.
Speaker 1: And that's what makes email dangerous as a form of communication,
Speaker 1: and why sometimes it's better to pick up your instead
Speaker 1: of writing an emails, or if you're getting upset at
Speaker 1: an email, to actually pick up the phone and ask
Speaker 1: your colleague what do you mean? Let's let's have a
Speaker 1: quick conversation before you hit the send snarky reply back
Speaker 1: to them because they may have meant something quarterably different
Speaker 1: and you're not getting it. Like, so people have difficulty
Speaker 1: interpreting actual written words. Can machines do what humans in
Speaker 1: this case can't do with human not today, not today.
Speaker 1: But that's the frontier of where we're moving, right, and
Speaker 1: that's what we have to do. We have machines today
Speaker 1: have trouble with just double negatives or triple negatives. It
Speaker 1: wasn't a great quarter, but it was okay, and it
Speaker 1: exceeded our expectations, which were fairly modest. Get a machine
Speaker 1: to parse that, right, It sounds like all those clauses
Speaker 1: sort of contradict each one before, right, and getting to
Speaker 1: like you know what I'm trying to say, Get the
Speaker 1: machine to parse the full context of that sentence. See
Speaker 1: I don't own that company. Maybe maybe it depends upon
Speaker 1: what you want to do or not, right, you know,
Speaker 1: get a machine to know that when inflation comes in
Speaker 1: higher than expectations in Japan, Right, that's a good thing
Speaker 1: as well in the present deflation environment. Sure, Right, So
Speaker 1: you have to teach machines context, You have to teach
Speaker 1: them nuance, you have to teach them the ability to
Speaker 1: understand when this is bad here but not bad there.
Speaker 1: It's the same, that's right. That's that's the frontier, um.
Speaker 1: And and that's all done through models, teaching model models
Speaker 1: for this to work within that's right now. I'm not
Speaker 1: saying that we're there by any stretch of the imagination,
Speaker 1: Please don't miss hear me on that. But I'm saying
Speaker 1: that's where we're headed, right, um. And that's the frontier.
Speaker 1: And it's question. The question is how quickly with this
Speaker 1: explosion um in you know, kind of processing power and
Speaker 1: all the texts and all these things that are being digitalized,
Speaker 1: will we get there? Interesting? I kind of feel like
Speaker 1: I asked you this question. Tell me about a time
Speaker 1: you failed, because you described the so so succinctly the
Speaker 1: quant crash and oh seven. But is that a fair question.
Speaker 1: Tell us about a time you've failed and what you
Speaker 1: learned from it, you know, Um, if you go back
Speaker 1: and actually read my third grade report card, yeah, um,
Speaker 1: it's amazingly talked about someone who hasn't really learned a
Speaker 1: lot from third grade um or changed their behavior. My
Speaker 1: third grade teacher said, Matthew always turns in his homework
Speaker 1: assignments late, but when he does it forest far surpasses
Speaker 1: our expectations. Do you still have a problem with tardiness?
Speaker 1: Is that an ongoing UF? I've got it much better. Uh.
Speaker 1: But you know what I've had to learn over time is,
Speaker 1: as one of my bosses has put it, is not
Speaker 1: let the better be the you know, the enemy of
Speaker 1: the perfect. Right, you're the perfect me the enemy the better?
Speaker 1: Excuse me? Um? And so you know, uh, you know,
Speaker 1: get out version one, get out version two, get out
Speaker 1: version three. What was your the great technology line? Is? Um?
Speaker 1: Good programmers ship? Is that the yes? So? So they
Speaker 1: I see people are coming into the studio before we
Speaker 1: get thrown out of here. Let me ask my two
Speaker 1: favorite questions. If a millennial or some recent college graduate
Speaker 1: where to come up to you and say, Hey, I'm
Speaker 1: thinking about going into quantitative research in finance, what sort
Speaker 1: of advice would you give them? Program? Program, program program
Speaker 1: much more than statistics, applied mathematics, calculus. So so the
Speaker 1: so the thing that I want if I'm looking for
Speaker 1: my ideal candidate, they need to know how to program.
Speaker 1: They need to know how to to do statistics and econometrics. Um. Uh,
Speaker 1: they need to know finance right, and they need to
Speaker 1: be curious. UM. I can't teach you how to program right.
Speaker 1: I've tried to take people who have those other characteristics
Speaker 1: and teach a program utter failure um um. But you
Speaker 1: know how you also get people who are curious and
Speaker 1: skeptical and skeptical of our own work. I think that's
Speaker 1: the biggest thing, Like realize that you're going to make
Speaker 1: mistakes and find the errors in your work before I
Speaker 1: find them. That's really interesting. And our final question, what
Speaker 1: is it that you know about quantitative investing today that
Speaker 1: you wish you knew fifteen or twenty years ago? You know,
Speaker 1: I guess I have a you know my my. My
Speaker 1: short answer to that is people need a much healthier
Speaker 1: respect for that effect that it's a model and it's
Speaker 1: going to be wrong. Um. And understand that even the
Speaker 1: best models that are actually true UM or have very
Speaker 1: good uh performance are going to go through periods of
Speaker 1: of big underperformance. And that doesn't mean you get rid
Speaker 1: of the model. UM. You know, how do you diversify
Speaker 1: across those models? Uh? And kind of you know, work
Speaker 1: through those periods. I think is the biggest thing that
Speaker 1: everyone who's trying to invest in quant and really kind
Speaker 1: of has appreciation for quite fast nating. We have been
Speaker 1: speaking with Matthew Ruthman. He is the head of quantitative
Speaker 1: equity Strategies at Credit Swiss. If you enjoy this conversation,
Speaker 1: be Shuan looked up an inchro down an inch on
Speaker 1: Apple iTunes and you could see any of the hundred
Speaker 1: and fifty or so such conversations that we have had previously.
Speaker 1: We love your comments, feedback and suggestions right to us
Speaker 1: at m IB podcast at Bloomberg dot net. I would
Speaker 1: be remiss if I did not thank Taylor Riggs for
Speaker 1: helping to produce the show and set up these interviews.
Speaker 1: Michael bat Nick is our head of research. Medina Parwana
Speaker 1: is our audio engineer, who is our recording engineer. Today,
Speaker 1: Caroline O'Brien, Thank you Caroline for filling in last minute.
Speaker 1: I'm Barry Ritults. You've been listening to Masters in Business
Speaker 1: on Bloomberg Radio p