Matthew Rothman

of quantitative Strategies at Credit Swiss. He is really a fairly legendary guy in the world of quant He very specifically warned Lehman Brothers when he was a relatively new higher there about some of the problems that they were looking at with their quant strategies and ask questions that they really kind of dismissed and laughed at, What do you mean we might go out of business? That's the dumbest thing we've ever heard. He's also been profiled in

a number of places. If you read Scott Patterson's The Quants, you can find him referenced throughout there. Pretty much the first guy to figure out what happened during the quant quake of of two thousand and seven. We're just about a decade past that, and so, uh, he was the one of the first people who really figured out how this happened, why it happened, and what it might mean going forward to the future of of short term trading

and markets and companies like Lehman Brothers. It's it's one of those stories of someone who was unfortunately, much to his chagrin, proven right. He was kind of hoping he wasn't going to be right, but hey, that's what happened, and we have all sense lived with the consequences. So, with no further ado, my conversation with Matthew Rothman. My special guest today is Matthew Rothman. He is currently the

head of Global Quantitative Equity Research at Credit Swiss. He is also a senior lecturer in Finance at the m I. T. Sloan School of Management. Prior to joining Credit Swiss, Matthew was the director of Global Quantitative mat Growth Research at a Canadian asset management in Boston, which was running approximately

seventy billion dollars in assets. Before that, Matthew was the global head of quant research at Lehman Brothers and then continued on at Barclay's Capital after that acquisition post bankruptcy. He is the author of Turbulent Times in quant Lands, which was a research note during the quant crash in the summer of two thousand and seven that became the most highly distributed research note in Lehman Brothers history. Matthew Rothman, Welcome to Bloomberg. Welcome, Thanks so much for having me,

Barry Um. So, I've been looking forward to this for quite a while. I knew of you from Scott Patterson's book The Quantz, and I was vaguely familiar with UM, the research piece that you would put out turbulent times in quant land. Let's let's start at a very basic level for the lay person. Please explain what quants strategies focus on. You know, so much gets grouped under the kind of rubric of quant today that you really kind of have to start to decompose it a little bit.

And there are a variety of different quants, UM, you should begin to think about them via asset classes. Uh So, derivatives based quants are very different than fixed income general fixed income quants versus uh kind of equity quants versus risk modeling quants, and each one will come with a different kind of skill set and a different kind of

approach to modeling. If you take equity quants just for a second, they also kind of come at a variety of forecasting horizons, and so they'll look at different types of signals and different types of things. So you have people who are playing literally in the millisecond range doing

kind frequency very high frequency trading market making. Uh it literally in you know, trading hundreds of times in the blink of an eye, um down to people are holding intra day strategies, to people holding several day strategies, to people holding strategies at last months uh and and so you know, you can think about them having very different types of signals and very different types of performance, but what they all have in common is that they're forecasting returns.

And what separates a quant in my book, really from a fundamental manager is that fundamental managers really try to understand the drivers behind the company. They talk to management, they think about products, They forecast earnings at the end of the day, and they think about a company as an organic unit. Quants think about returns and what are

the drivers of returns. What is going to make h two returns, two stocks take the same way or go the opposite way over a long period time or baskets of returns, and so we we think about what drives returns more than anything and really abstract away from the companies themselves. So so I oversimplified, as the fundamentalists are telling the story and the qual are crunching numbers. Is that a gross of simplification or does it work? I think everybody crunches numbers. I wouldn't want to say the

fundamentalists don't tell a story. Um, they're certainly, you know, trying to forecast cash flows and understand, uh, you know, what are the drivers of earnings and revenues uh, and then finally relate that back to us a stock price and what they think they're appropriate stock price would be. Quants don't try to do any of those things necessarily.

They try to just forecast returns directly, uh, and see what can be those drivers of those returns and overall, for the most part, think about large baskets of returns or of stocks and how those characteristics and how those stocks behave based upon their return based characteristics. So you studied under Gene Fama, you got your PhD from Chicago, The really the home of the efficient market hypothesis? Can you square E M H with quantitative analysis? Are they similar?

Or really? When I think of quants, I think of using powerful computers in order to try and beat the market. Or again, oh, you oversimplify. So I think the E M H is probably one of the most misunderstood concepts UM in finance. And Gene Fama's genius was that he really taught us how to think in a very rigorous way about what it means to be an efficient market

and what it means to beat the market. Before Fama came along, there were people publishing studies all the time that said they had a strategy to beat the market. I think that drove Fama a little crazy, um, because the work wasn't very well done and the phrase beat the market um was very loosely applied. And what Farma really kind of taught us was that you have to think about risk uh and say, on a risk adjusted basis, can I beat the market? And then academics have debated

for years what is the appropriate measure of risk? Is that the capital asset pricing model is that the Fama French three factor model. Uh? Is there something else that we're missing this now car hearts factor on momentum uh that is put in there. But academics have then debated are those factors anomalies or their proxies for risks? And you know, we spent fifty years more plus and academic circles debating what it means to beat the market with

a risk adjusted return in Wall Street. Um. You know, there's been a generation of Chicago students and other students who have come to Wall Street uh, cliff as nous and crowd and many of the people's at Goldman's access at Management and then fanned out across the street in in in in many ways. Um. And what we've all kind of been trained in these methods, not only Chicago

but other schools as well. And what we've really brought to Bears is kind of very hardcore, rigorous academic quasi academic background to how we think about can you make money as a quant and what does that mean? Uh? And and so you know, Wheel spend less time arguing about is something risk or is it a miss pricing? Is it an anomaly? Um? Doesn't that's that mean the

market is efficient or less efficient? But you know, we we bring that same kind of sensibility that Fama taught us, UM, but we'll get less involved in the academic, you know, debate about risk versus miss pricing. So let's talk a little bit about building a quant team. You're hired at Credit Swiss to help put a team together what goes into that? How do you first begin to assemble a QUANT team. I think the first thing that you need if a quant, if you're going to be a quant,

is a combination of data and technology. So you need to kind of go out and figure out what are the big databases that you need where you're gonna get your information and what is you're diversified information set going to be what you think you're edges and go about

procuring that. So you're you're building hardware and software, You're hiring programmers, You're hiring programmers, you've got you have to hire data scientists and people who are going to really an overuse term, but people are going to really understand how to manage and curate and store your data. And then you have to find researchers who know what to really do with that data and where to find and where to find those hidden gems of signals and come

up with ideas. And then you actually need people who can communicate it. So so this isn't anything that gets put together very quickly. This is a long processes, this is a long process. When when Credit Swiss comes to you and says, hey, Matthew, we want to build a quant team. Do you say, all right, it's gonna take five years, two years, How how do you put them

into the proper mindset for this? I say, you've probably got to give me twelve to eighteen months and think that I'm going to be in a dark cave, um and you're gonna see nothing from me. And I'm gonna be asking you for big checks uh and and and hiring people and kind of layout a business plan very carefully uh and can detail the costs and exactly what

I need. Um. And you got to make sure that they're in it and get and get the ask because it's a heavy ask, but what you can get out of it is pretty cool at the end of the day. So the competition for the really skilled fill in the

blank programmers, researchers, data scientists. I think about the just a giant collection of PhD s at Renaissance Technologies, long before the rest of Wall Street started thinking in those terms that that's gotta be you said, big checks, that's gotta be a serious commitment made by the firm to to build something like this out. It is definitely a serious commitment by Credit sweet um uh. And they understand that much of the world is really moving this way.

And from the firm's perspective, what I believe they understand is that we need to be able to deliver content um to those firms that you're mentioning. Uh, that is interesting to them. Uh. The way we deliver fundament to research to the biggest asset managers in the world out there, we need to deliver quantitative research along those same domains and so yes, it's a big ask if you're going to be additive to those people's process. Uh, and you know, play with them in the sandbox. Is it is it

that competitive to hire people? I was joking a little bit, but I'm assuming that these folks are really in demand and there is no you know, you can't really do this on the cheap. I don't think that you can do this on the cheap. But you know, you need you need a relatively well sized staff. But you know, we're not going to be rentech. We don't think about that.

You know, we don't need that size staff. No, no, no, no, no, no, no no no. That I you know, I think you need a staff of probably five to seven good researchers to be able to produce something interesting. Uh. You need a technology team of three to four people. Uh, you need a data team of probably another two to three people, um to really four people to really kind of begin

to curate what you're doing. So it's you know, not crazy um in any sense, but you can be very productive and produce really interesting research on the cell side with that size team. So in one of your notes you mentioned quant one point oh um, referring to the quant quake of of the summer of two thousand and seven. What does quant two point oh and quant three point oh look like? What are the changes that that are

taking place and will take place? So Quant one point I really ended I think in the summer of August two thousand and seven, where there were rather simplistic strategies that a lot of people were using, and we turned on the light in the room and saw everyone else who was there and realized that we needed to do

things to diversify ourselves from each other. Um. And so we've seen that really over the past eight to nine years, where people really started to think in different ways, not even so much about uh forecasting returns, because I don't think we were all that similar there, but really about how we access liquidity in the market, how we optimize our portfolios, how we thought about risk, how we put you know, factors together. Could we time factors? Could we

not time factors? How you incorporate macro information into your forecast? And so people really started to break the paradigm in a lot of ways. Uh, still within relatively traditional framework, but begin to really push that envelope, you know, kind of doing simple screening was no longer enough. So some of the criticism, and I'm pulling a line, this is actually an academic white paper, our our quansole fishing in the same small pond with the same tackle box, implying, hey,

these were all crowded trades. Everyone was more or less using the same tools and pursuing the same goals. Was that true back then? And is it still true today? You know, it's one of the criticisms that get leveled at quant that infuriates me the most. Um. You never

hear people say that to fundament to analysts. Right, you're all listening to the same press conference, You're are reading the same earnings report, you're all talking to the same investor relations person, so therefore you must all be the same. So I think it's one of those great misunderstandings about quant is that just because you look at the same data or studied under gene Fama, Uh, you must all

be the same. Um. And let me kind of give you an example of how even quants can be different, even though on the outside they may look the same. So quants not surprisingly like to buy cheap things, um, and the hope that they'll go up in value. I really don't know any investor who likes to buy expensive things and think that it's going to go down in value high but sell high. But I don't know anyoneho wants to buy high and sell low. No, right, not

a great strategy to make money. But when you're a quant and you say that you want to buy something that's cheap, well, you're programming that, and so you have to all of a sudden be really really precise ice on what you mean by cheap. Do you mean it's cheap on a pe ratio? Do you mean it's cheap on a book to price ratio? Do you mean it's cheap because of sales to price? What metric are you

even using to define cheapness? Right, you've got to program that into the computer, and then you've got to say, do you mean it's cheap relative to its own history? Do you mean it's cheap on a sales surprice ratio compared to every other stock in the market. Do you want to do its sector relative? Do you want to do it country relative? What do you mean? Um? And

God is in the details of a lot of these things. UM. If I'm gonna look at a book to price ratio, do I just book values for differences in gap standards in different industries or not? Do I? How do I correct for book value under i f R S accounting International accounting standards versus gap standards? Do? How do I handle all these things? So even a look like you're just doing the same thing, Oh, I'm using book to price,

there can be a tons of details. Let's talk a little bit about that period of of oh eight oh nine, because you know it's almost ten years ago to the day when that weekend that shook the entire financial firmament took took place. I've read a couple of your older research notes, and I have to ask you the question what actually caused the financial crisis and market crash? You know, I think the great place um to start, as Andrew Ross Sorkin's book Too Big to Fail. If you're really

interested in kind of the inner workings of Lehman. Uh during that time, he nailed it. Um, it's a great read. I couldn't put it down. My wife kept, you know, like nudging me, like put the book down. You've lived this, like why do you have to read this? And I was like, oh no, he's got details in here that like some of us were trying to find out. UM, research one into that you could see in the suffis he got access to and got people talking. That's really

quite remarkable. Um. I think that there was definitely some level of mismanagement at the top, as as he documents, uh, not just Lehman Brothers, but across the board, but across the board, a misunderstanding of risk. Uh. And it's very hard to know when the music is going to stop, as it were, when successful businesses have run their course. Um. If you remember nine months back, Lehman Brothers was putting up record earnings. Uh, And so how do you know

that it's time to get out of that business. It's a really hard call to make. So so let's talk about six or nine months back. I read and I don't think this was Sorkin's book. I think it was Patterson's. The Quants. You had submitted some memos to senior management, sort of saying, hey, guys, you gotta wake up. Is a ton of risk here, and it seemed like you had a sense there were problems coming long before much of the street figured it out. I think you're being

overly generous to me on that one. I think that there were definitely things that concerned me. Did you ask someone, and again maybe this is Patterson's book, didn't you say, well, what what are the contingencies in case Leman goes bankrupt? And people laughed at you, They looked at you like you were crazy. Um, there were times that there were things going on that disturbed me. I'll give you I'll

give you a little anecdote. There's a great paper by a professor um Owen Lamont at the Harvard Business School and used to be at the University of Chicago, and he did a study that found that firms who get into fights with their short sellers, like the time those firms end up going bankrupt. They're in trouble, nothing else to do but with the shorts like like stop right, you know, Um, and and he documents some of these

and they're great anecdotes in there. And if you remember, towards the end of Lean and Brothers, UM Management got into a fight with one of our with David Einhorn. I believe UM, And when I yes, it was, it was contested. And I did send UM that paper along to senior people. And picture, wait, this guy sending me a Harvard Business School white paper and arguing with shorts. Doesn't he realize our very foundation is under assault. I could just picture the c suite response to that. UM

was this head sending me a white paper? What is this except except saying, these people do this, You're scaring me. UM. I was lucky that my boss was a PhD from the University of Chicago as well. He appreciated his kind of things. Uh, he got it. Uh. And and I think we actually, you know, I don't want to say we stopped fighting with him, but we did stop fighting with him, and I think we did start to content rate on different things. We had some very talented, you're

bright people there. You're literally on the way to London to a conference when you get a phone call. All you're in JFK. You get a phone call on the other side of security. Hey, tap out, you gotta come back. You go home to New Jersey. You take not your little car but your wife's station wagon with the presence of mind too. I gotta go clear up my office. And you're described as this lucid rational reason like you weren't.

Oh what a shock, what a surprise. This seemed to be something that you apparently had thought out before, where most people more or less seem to be shocked or panicked or both. How do you and is that again, am I oversimplifying this? Or um? Well, I was certainly emotional. Um, I don't want to say that that wasn't a very

emotional night for me. Um. You know, one of the things that I think, you know, behavioral economists and other people tell you, is that the closer you are to a situation, the harder it is for you to kind of take that step back rationally and see what's going on. A lot of Lehman management lived through ninety four and had lived through other crises, and really we're very, very, very close. I was relatively new at Lehman and so kind of had a little different perspective more objectivity than

then they did. About the situation. I think that was part of part of the difference. Um and just being kind of a little just more unsentimental. Let's talk a little bit about the quant crash of two thousand and seven. I love the story about you and Austriel Levin figuring out what actually had happened long before anybody else. Uh. Was this over sushi or Chinese food in San fran sushi? Okay, it was a sushi dinner. Um. I've always felt badly that um ozreal Levine to his friends as known as susy.

You know, he really should have been the co author with me on that paper and deserves every bit of credit. Um. What was he working at the time? What was the place called Menta Capital? He's still there. Uh. And he used to run b g I's hedge fund um main um, you know main hedge fund over there, and he had

started on his own. Uh. And you know I'd have been out that day seeing clients and watching the blow up happening, and like we both were just sitting there over sushi, um, and like just kind of piecing together what would have caused everyone to unwind? Um? And it was literally just over sushi dinner. Just arguing it back and forth and kind of putting together what the story had to have been. So tell that story, because it's

fascinating how you guys deduce leverage multi strat. So the story that we that that that that we kind of came up with um and still holds up to this day. No one's you know, we can't prove it, um, but no one has a better story UM. And you know, it's kind of become accepted wisdom is that there were a number of multi strat quantitative hedge funds that held positions in UH sub prime mortgage UH and fixed income

mortgages of low credit that we're taking losses. This was in the summer of two thousand and seven where you had the managers at bear Stearns who were running those fixed income portfolio and trying to remember I don't remember the names, but that that was June that kind of wabble that that that started wobbling right and by in mid July you saw a number of other quant um

You saw the fixed income credit distress. Credit market was in distress um and it was a liquid and people were beginning to receive margin calls on those on those books. They were, they were highly levered, and um Man and and and prime brokers and others were coming to people who who held those assets and said, we need more collateral so support those books. So highly levered and a liquid not a great combination and not a great combination.

And the last thing you want to do if you're holding that portfolio is actually liquidate those assets because the marks aren't probably really at market. There at some discount discount to market. But when you try to move that, the mark is going to get set lower. As you try to sell in a liquid asset, right for that, you know it's going to be marked lower than the whole portfolio gets marked lower. You're gonna need to raise more collateral for the discount of the underlying asset. Sounds

like portfolio, It sounds like exactly. And so the last if so, if you're smart and you realize this, you're not gonna if you to meet the margin call, you're not going to sell that asset. You're gonna go sell a very highly liquid asset because you're taking a much smaller haircut on that if any at all. Right, it's a liquid portfolio. Now, what is the most liquid assets in the world, Probably US large cap equities. So if you're a multi strat firm, where are you gonna go

raise that equity? You're gonna go liquidate, And many of these were quants. You're gonna go liquidate your quant portfolio. And we saw that if you go back and look at the data, that a lot of the quants were losing money throughout most of July. A well known quant manager has come out and said, like, we lost money twenty one out of the twenty two days in July. But it wasn't just a kind of steady trickle, like

it wasn't really bad. But then it really started to pick up momentum as it were in um August, and people started and we really think it's because the liquidations and the margin calls became much more severe, and other people were noticing that their being their portfolios were misbehaving, and so they started to take down who didn't have

any exposure necessary to these subprime assets. They saw their quant portfolios not behaving that the way they wanted, and so they started to take down risk because their models were misbehaving, which is because it just shows you how inter related everything is. That's right, we don't have any subprime exposure doesn't matter. People who do a liquidating things

that you have exposed to. And so that's how can that is a definition of contagion, right where something that you're not actually exposed to begins to affect another part of the market. And you and Oozy are putting this together pretty much in real time in early August. We're putting this literally together over a three to four hour dinner of sushi in a restaurant in California with some saki. UM, and you closed to join up there until they kicked this out, and we kind of you know, you know,

we didn't exactly have the story. We couldn't prove it, but it all made sense, UM, and kind of got this story. And then I went back to my hotel room, uh, and realized that the rest of the trip that I was planning in California was out the window. What I needed to do was right this all up. And so that next morning I called and told all my sales people,

cancel my trip, UM, cancel all my client meetings. UM. This is you know, I'm going into the San Francisco office and we're writing this note as our quant world is melting down. UM and you know, stayed up until literally, I mean I got there at you know, eight o'clock

in the morning and published that note. Um walk. I remember walking back from the San Francisco office after I hit the send button on that note and knowing that I had done it was almost like the Jerry McGuire moment, like when you put that out there, kind of saying like, oh my god, what if I just hit the send button on um and woke up to the most read note in really the history of wurbulent times in quant Land.

Really just the timing was perfect, and you guys figured out, if not the best explanation, certainly no one's come along with a better explanation, since I think people it is pretty much I think received as the explanation. So at this point, there's a line I'm not sure if this is from that or another one of your writings. Events that model only predicted what happen once in ten thousand years,

happened every day for three days. So, in other words, loudly improbable things are happening way too frequently, right, I think that's gonna be on my tombstones, um um. And you know, some people have actually criticized me as not understanding that returns are not normally distributed. For that statement, of course, what I meant was that things were misbehaving on our models, and our models were misspecified and wrong. UM and obviously we did not have their appropriate distribution

of returns. If that's what our models were saying. UM, I clearly understand statistics, and clearly it was it was. It was a pithy way of trying to say, our models are absolutely wrong. If that's what we're predicting and we're seeing them three days in a row, we don't understand what's going on. Our models are wrong. So so I love the expression all models are wrong, but some are useful. Um. And your models had previously proven to be useful. What was wrong with all of the quant models?

Some people were blaming Gaussian Coppola's and other people were saying, no, this is strictly a subprime derivative c d O UM contagion. Where did the models? Where were the models off? I think where the models were off is in understanding liquidity UM wasn't appropriately kind of factored into that and notions of crowding. We're very very We're just not in the models to be honest with you, we didn't know how

to think about that. We didn't know how to think about crowding risk, We didn't know really how to think about liquidity the way we do today. We held more concentrated positions at that time. While we might have only hold a fraction of average daily volume a d V and traded those carefully, we let those positions build up too much as a portion of our book um. We didn't spread the bets out enough across enough different stocks, and we ran with this way too much leverage. So

so there's no doubt. Leverage is always a giant problem whenever there's headache. But you had done some subsequent research that found, hey, the correlations were much lower than everybody believed. Everybody that was talking about crowded trades assumed people were all, if not in the exact same investments, in such similar asset um holdings that it didn't make a difference. But you found the correlation was something around. What I tried to do was decompose why we were into crowded trades.

So I don't think we're denying that quants were holding the same portfolios. The received wisdom was that it was because our return prediction models are alpha models were all the same that we were looking in this We were fishing for alpha in the same pond. And what I actually managed to do was convince the biggest quant firms out there that they should actually give me the outputs of their models for a period of a year. And

and they did. And that's a lot of trust for I want to say, all right, here's the crown jewels. Try not to let anybody else get home. That was the relationships that I had with my clients was that they actually gave me the outputs to their models because we all thought this was a really important problem to figure out of what drove us into these same trades. And what I saw was that the actual outputs of the models weren't all that correlated. It wasn't an alpha

modeling problem. People, as because we talked about before, have different ways of predicting returns. If you and I were to say, what is the stock that's gonna have the highest return over the next you know, six months, or ask your listeners, right, there'll be a lot of people would have very different opinions on a stock like Netflix or tesla Um or Amazon Um, any of the fang stocks,

any of those kind of things. We might all have very different forecasts, but if we were to ask what are the most risky stocks, could probably list a lot of those names. The dispersion and outcome. We don't know which way it's gonna go, but we can agree it's risky. We have been speaking with Matthew Rothman. He is currently the head of Quantitative equity Strategies at Credit Swiss. We love your comments, feedback and suggestions right to us at

m IB podcast at Bloomberg dot net. You can check out my daily column on Bloomberg View dot com or follow me on Twitter at rit Halts. I'm Barry Hults. You're listening to Masters in Business on Bloomberg Radio. Welcome to the podcast, Matthew. Thank you for being so generous with your time. I find this stuff fascinating. I was about to ask you, Um, you mentioned Andrew Ross Sorkin's book Too Big to Fail. Um, do you read Patterson's book The Klantz? I did, oh uh not. I found

it fascinating because I love the characters. It's it's all my favorite Asses and Sam, Jim Simons of Renaissance and Ed Thorpe. You're mentioned in it. A number of people are in it. I found that to be a really fascinating tale. What what was your take on that? I'm to apologize for a no, no, no, it's fine. Um my frustrations with the book or that I found it a little overridden and a little over sensationalized. So here's what I have to I have to throw your own

words back at you. Are you closer to that narrative than you are to the Sorcan narrative? Maybe I read I you know, I know some of the characters in there, and um, you know some of them do have tempers. But like you know, I read the poker game that starts. It's when I was looking at them, like what is

this about? Out? It was yes, and you know we those of us who are close to it, and you see, I mean there's a detail in the book, just for as a small example that drives me crazy, where Scott has me coming off a red eye flight from New York to San Francisco. No, it's the other way around. There are no red eye flasks. It's San Francisco to New York, and it's it's barely a red eye because it's five and a half hours, right, and it's like you're going to London and it's eight hours, you can exactly,

and so and so. It's just little things like that which you just pick up and you're like, he's got the details rock. And when someone starts getting the details wrong on little things that are so obvious, you start distrusting some of the other stuff where it's harder, where it's harder to see you. You're bursting my balloon. I just love that book so much, but I can understand it's someone who was so close to the story and

so close to the characters that you see the embellishments. Sure, so and and if you know some of these people, if you know so, how I know Cliff Astness today versus that book. There are two very different characters. Like the ass. This character in the book is a little harder and a little like I know him as this this mischievous guy with a wicked wit. I mean, he's just outright hilarious. He's a funny guy. He's he has

a great sense of humor, great personality. Doesn't come across that way in the book, you know, he he is um little hard ass in the book. You know, I'm not saying that he's look at the company he's built and all of those things. I'm sure he drives people to produce results. I would expect nothing else of a multi billionaire, uh, you know who had the vision to build the you know, the incredible company that he and his partners have built. Um. But you're right, the charming

side the Cliff does not come through that. The witty, the you know, hilarious UM, charismatic um, you know, part that makes Cliff the legend that he is unshine through in that book. And so like those are the types of things that that that that bother me um about it. But it's what's fascinating about is to me is is this thread that runs through the throughout that whole book.

And we'll get two books a little later. How quant was sort of disdained in the people like almost I don't want to say laughed at, but kind of like you put the numbers geeks in the basement. We're actually running a real firm here. It almost starts like that and ends up in oh Klan is taking overall the Wall Street and you people who didn't understand or appreciate it, well, you missed the bus and here's the next big thing. But that thread is fascinating and I think that's even

more true today, um than it ever has been. Um. You know today you shouldn't be putting EXCEL on your resume. Uh. You know you know that you know just a word, right, you know that's a given. Like you know today, if you want to stand out, you know, you better be talking about how you can program and Python in our uh. And you know, you know, you know all those sets of skills that you that you have, you know, if you really want to be successful. So I think on

Wall Street today and kind of going forward. So before I get to the standard questions, there are a couple of things I missed that I want to come back to. UM, and I have to stop saying, um, halt h O l T is a pretty substantial product at Credit Swiss. Can can you explain exactly what that is? Because when I started researching and I'm like, wow, how have I never heard of this? This is Uh it's a great product. Yeah, no, it's a great product. Um, you know it's not part

of my domain. UM, there's a there's a team there that has been doing and that's been together for close to twenty five years, maybe more than that. Pretty successful,

very successful uh. And what they've really done is collected accounting data for companies over that plus a year period and figured out how to normalize it uh and really begin to look at companies across different industries and different countries and put them all on an equal footing um uh and then really look at what a take those cash flows and look what really is the return on capital um for these companies and the implied growth rates for them and kind of come back and then look

at what is then being implied for what the appropriate stock valuation should be. And so it's a wonderful tool that people that's very interactive UM and that people can really kind of compare companies all across the globe really on an apples to apples basis and look at it from a fundamental accounting perspective. It's very very powerful and has a wide following across the investor basis, and I assume a lot of people just are unfamiliar with it. I was looking at it saying, how have I not

seen anything mentioned of this in the media. It was pretty uh. Up with a trial fe like, yeah, we'll set you up with the trial. Anytime you like I could get lost in that, I'll have my head of restart something like that. So since the quant crash and oh seven, we've seen two really interesting changes in the market. One has been, I don't want to call it the rise of indexing because that's been going on for forty years,

but certainly a broader mom and pop imbraceive indexing. And then second, at the same time, really volatility has fallen off the off the cliff. How have those two factors

impacted the ability for quants to make money in the market. Yeah, I mean I the way I have really kind of understood the rise of indexing and then probably not unique in my insight here is that in two thousand and eight, what you would really investors in two thousand and nine really had hoped for was managers that were going to be able to give them some level of insurance and and protect them in those moments in time, and that

just didn't happen. UM. And so I think people have been driven by lower fees UH, and I think the fiduciaries, the planned sponsors who are managing UH many of the retirement accounts UH and pension funds have been disappointed by that facts as well. And so you've seen this move towards lower fee uh types of investing that can deliver you, you you know, what seems like to be the same kind of outcome for for for a lesser price. UH.

And and so investors have definitely flocked that. And you've seen even this past year, the funds that have actually attracted the greatest inflows have not only been passive, but the absolute lowest priced passive UM funds. So even within low fee, it's been the absolute lowest fee that have attracted inflows. I remember some years ago morning Star did the study. Now their bread and butter is the Star

rating system. They do this this study that said, if you can only know one thing about a fund, what should it be? And the answer was fee. If you just forget everything else, just by the lowest fees net net on average, you're gonna end up with the best performance. And and warning Stars credit Not only did they publish it, it's still up on the website. You go see it. It sort of argues ignore the stars, just look at fees.

But this is an academic there's a whole bunch of academic research out there UM that has been absolutely making making that point. Um as well for a number of years, you know, Uh, Professor Gruber down An n y U has published some of the Gruber Gruber um some of the seminal studies on that as well, which and and and and others have. Um, he's not alone in that. But um, that really kind of made the point that fees low fees are one of the biggest predictors of

future outperformance. Wow. That that's that's pretty Uh, that's pretty significant. Um. There's a line you haven't in some of your no, and I just love this. I have to share this. Um, you must have the right dictionary. If a trader in an instant message rights this is a dog with fleas, you need to know what they're really saying. UM. Much less if they're saying I'm doing market research, that just

means they're watching YouTube videos. So so what is this is a dog fleas means I don't want to touch this, I want nothing to do with it. Yeah, it means this is a bad stock. Don't don't don't don't own it? Right, Um you know? Um? Or yeah, it is trade or speak for you know what are you doing market research?

You're watching YouTube? Um? Where this comes up um is that there's a whole new field in in finance, and not that new um um, but it's really taken um getting a lot of momentum over the last five seven years of trying to understand text UH and parsing text and trying to understand the meaning within and what people

are saying. So, whether it's reading earnings transcripts or reading annual reports UM, or reading news in general, or from a compliance perspective, if you're just trying to read instant messages and so the question is how do you begin to understand context and what people are really saying. And so if you're reading trader speak, your dictionary of words to try to understand what our trader is saying is different than if you're reading a novel. So so let's

talk a little bit about that. Because one of the questions I didn't get to how to do with machine learning and artificial intelligence, which and then throwing big data. These are burgeoning areas for research, not just for quant trading, but for everything. Big data is now devouring the whole world. How do you interact with artificial intelligence and machine learning when it comes to figuring out what is out in

the world and translating it to an expressible investment theme. So, you know, I think this is a really exciting time to be a quant because the word world is becoming more and more and more and more digitalized every day, and we are able to get our hands on data sets as quantitative investors that we could only dream of, um, you know, five seven years ago. And so the real question is that you have these huge data sets, how do you begin to process them? Uh and look for

signal within them? Uh? And so you've really seen To make that happen, you've had to have two other revolutions that have had to come along at the same time. One is that computing power and in general hardware and software has had to just kind of go through a revolution. Uh. And we've seen that um exponential increases in computing. At the same time, the price just fell off the cliff

for storage. Right, So we can go to the web on you know, a WS, Amazon Web Services or Zure or other places our there Google um uh, and you can buy you know, terabytes, tens and tens and hundreds of terabytes a storage extraordinarily cheaply rent it when you're

done just kind of you know that's it. That's all you need um uh you can have There's that we've moved now from processing uh power from processing on CPUs UM in computers where everything had to be done in a hierarchical structure, but we've now rewritten the code so that everything can run in parallel UM and use GPUs graphical processing much faster in tandem cheaper UM. And so you've seen exponential growth um uh in computing power that

is really really hard to overstate UH. And it's just been accelerating even that over the past you know, eighteen months. So we're we're just beginning to understand and unlock the horizon here. And so this has allowed you to just process amount ounce of data UM that is hard to imagine. An example, UM, there is a company out there that is now literally taking pictures of the entire globe every day at three meter resolution and storing that data for you.

You could say, see the tiniest changes anywhere, right, Well, three m resolution is what the law allows, right, so a car, so but you can tell whether that's a car or a bus, right, UM. We can't see people, um UM. But the storage on that is a Yoda bite. How big is a Yoda bite, It's a trillion terra bites. Right, So how do you can now now process that data? Right? That requires a lot of computing power and a lot of storage capabilities. That's now economical to do, where five

years ago that was a pipe dream. That's amazing, you know. I just I'm familiar with Ways, which was bought by Google, which uses Android phones to to look at traffic. I was just reading about a company that uses cell phone signals to identify actual foot traffic in malls and they identified way early that retail was in trouble before it was front page news. Amazon's doing this and this company's

series is in trouble with that. They had figured it out and it costs hundreds of thousands of dollars a year told the services sometimes right, but you know, if you know two years in advance, hey, by the way, retail is about to fall off a cliff, it will pay for itself in Uh yeah, I mean the data sets that, like I said, that are available are really quite remarkable. Um that people have in terms of literally where you your your foot traffic, UM, your credit card

spending data, UM, reading email receipts UM. You know, that that are available. As you said, traffic data. UM. It's there's really if you're if you if there's a data set that you want, UM and you don't think that you can get it, you're not looking hard enough. Uh. At this point, we can track literally every bill of lading for every cargo container that is coming into the United States. Really at T plus one tomorrow time, I I can, I can. For planes are crazy that you

could see every plane in the sky. You can see where they're coming from, where they're going, what type of plane it is. You can ask Syria on your phone, tell me the planes that are above my head and she'll tell you. Really, I'm gonna have to try that. You know. You literally just asked Siri what plane is above me and she'll tell you the planes that literally

are right about I mean. So, so all this data UM is you know what people are beginning to look at, and it's a bit of an arms race UM for everybody to try to keep up with this UM and to try to understand what's out there and how you process it because there's probably no one data set it's going to give you the you know, the holy grail of everything. It's really about how you take all these disparate data sets combine them h in a thoughtful manner that's really going to give you your answers uh to

do that? Interesting? Alright, So I only have you for another ten or fifteen minutes. Let me get to my favorite questions. Well, I was going to ask you what's the most important thing people don't know about your background? But I suspect you've already answered that. Actually I don't think I have. So it's not Springsteen. It's not Springsteen. Springsteen is very important to my background. Um, But people know who know you know that, people who know me

know that. And actually Springsteen lyrics are always pretty much hidden somewhere in my notes. Like if you're a Springsteen fan and you read my notes, you'll capture you'll find another you'll find a hidden Springsteen reference in there. Um, And sometimes they're not so subtle. UM. But what people probably don't unders know about me is that I was very lucky, um to be born to two academics who

teach at Columbia UM. And my father was a professor I Guess is professor of American social history and really kind of founded the field um of American social history UM and taught me UM at a very young age UH to be questioning and dubious of your sources UM. And so when I was ten, he dedicated a book to me UM and it's called The Sources of American Social History UH. And it says to Matthew to to understand that American history is more than the study of

great people UM. And it's a book of unconventional sources that try to study how institutions work. UM. And study history as a study of institutions UM, not as acts of Congress or acts of war or what great people are doing. But study the church, UM, study the prison, study the hospital and the experience of people within those setting UM. And understand the biases of these sources and look for unconventional sources. And so I like to think that that kind of training about data UH was embedded

into me at a very very very early age. And looking for things UM and biases and things and being skeptical of the wisdom you're receiving of what you're being told, UM, how markets actually work, the players in them. All of that was really instilled at me from age ten to eleven.

So you're an M. I. T. Darren Asamoglu is there and he talks about the role of institutions in the economy and people shouldn't be looking at these big events or these fed chiefs, should be looking at these societal institutions. They have a much greater impact on things like economic inequality and recessions and cycles than does anyone person or anyone sort of event. Very much along your dad's along those lines, um, And I think you know, I think

you know, I know. One of your questions to me, I don't mean to jump the gun on any of your questions is favorite books. So let's let's jump the gun. Let's well, before we do that, let's because I'm anticipating the answer to who were your mentors? I have to assume your father was a mentor of yours, of course, of course, I mean, um, that's a little cliche to say that your dad was a mentor. Well, but you know,

someone dedicates a book and it it obviously, of course. Um. And my father was absolutely influential in my life and my thinking and teaching me how to write and just taking a red pen to my writing and just um, you know, arguing with me about ideas. And um, I had a professor at college who was a huge mentor

of media to me. I went to Brown advisor. UM. I did a lot of independent studies with him, who just grilled me on ethics and rigor of thought and uh the law uh and you know what are what are rights versus nonconstitutional rights and just took it in his wing and really shaped my thinking, uh in in in many many hard ways. UM. I think some of my other um you know mentors, uh you know had to have been a guy by the name of Sid Brown, um who's who was a professor at Columbia who saw

something in me. When I was a master student there, I wandered into uh his graduate class, graduate students class and stochastic calculus uh you know, and uh you know it was filled with PhD students and somehow I got the high grade. I'm still to this day not sure how I did that. And he took me under his wing, um and taught me a lot. And now he's a friend and colleague and just been entrusted um kind of

mentor and advisor throughout my career. UM. And then there was a gentleman at Lehman Brothers UM, where I was much too young in many ways to get the position I did as head of quant. Uh. Um I had been uh and I had been out of my PhD program all of five years. Oh really, so you're a little green. I was a little green. And he threw me into as a managing director ahead of all the quantitative equity research at Lehman five years out of my

PhD program. Um, and so I was green. Um. I didn't quite you know, know how to behave with other senior managing directors and how that whole world worked. Um. And Robbie was absolutely you know, harsh um uh in brutal and uh to me, but in the loving way. I was one of Robbie's children, as the way I described it in um. Uh you know, he taught me how to grow up um and how to behave in a major, world class institution and what was expected of

of me not so much. I mean he helped me on the quant but really helped me mature as a manager um and as a person. Uh and how one carries oneself uh in a role. And I remember kind of telling my team every day like I don't know what I did wrong um this week before my weekly meeting with Robber. But but I'm about to go find out and UM, I don't know when I found out. UM. Uh and he and it was painful at times, UM,

but I absolutely love him for it. UM. And he made me such a better UM workplace person, uh you know every day and uh you know, uh it was it was hard at the time, but I I adore him for taking that time and attention. UM everybody. You you know, if he's listening to this, you know everyone needs them two you know. So let's talk you mentioned books. Let's talk about some of your favorite books, fiction, non fiction. What do you read for fun? What do you read

for informational purposes? So? Uh, I love documentary photography. I am a huge fan of that, and I collect um documentary photography books. I have a pretty extensive collection, UM. And I am always on the hunt for that new great documentary photography book UM or and collecting the masters. Uh give us the name of a book for someone who has no experience with documentary photography but wants to have splore the space. So you have to start post

World documentary Photography with Robert Frank Um the Americans. UM. That was an absolutely revolutionary book. UM. Along with Cardi A Brasson the decisive moment UM. And but but but Frank changed photography UM forever UM. And then there comes a whole series of lesser known masters UM, but utter masters UM from you know, Eugene Richard's work out there documentary UM poverty in America. That is one has to be aware of and see um uh to the work

like people of ron have viv that is just legendary. UM. His photographs were actually submitted into the War Crimes Tribunal UH in the Hague UM documentary the atrocities um uh you know that happened in the UM you know, and in what was Yugoslavia, UM. You know, just the some of the most important work. There's work by people like Don McCullen, the legendary British war photographer, documenting the atrocities

in Biafra and Vietnam and around the world UM, heroic people. UM. Tim Heatherington's doing work that unfortunately he died UM, covering UH in Africa and Libya. And these are just um, you know, moving work. Of course, the work by Robert Kappa, the legendary war photographer who was in the first wave of the boats UM coming off of d day. Um.

So just um, very very variable, powerful work. And there are people who are still doing this work today, um out there that just don't get any love uh and attention. People for like Alex Webb at Magnum, Ed Kashi um at you know seven. Uh there's I don't I'm just missing people. James Knockway. Uh, you know, truly legendary. Listen. I'll make sure it gets included with just on this. I could go on. I have hundreds and hundreds of about something, a little something, a little lighter than than

what else do you read? What do you read for enjoy, for just pure relaxation. Um. You know you're talking to a guy who just loves quant and loves getting quant finance. Um. I'm really into the work right now by a guy by name of Jarren Lanier. Um. He's the philosopher in chief um at Microsoft. Okay I knew yeah. And he's written this great book called You Are Not a Gadget. Um Uh and he's really thinking very hard about the role of human beings and artificial intelligence uh in this

big data world. Um. There's just great stuff uh to read out there. Um. There's other work U, A great book called Behave by Richard Seboski. Um. So it's it's a big book. It's a big book somebody else recommended and I actually picked it up not too long, you know, just really I mean it's not light stuff. Um. But it's really beginning to explain how what is you know, getting too these arguments of what is nature versus what is nurture? And how do we learn? And how do

human beings change behavior? Um? And you know how inbred is things like violence into our societies or not, and studies examples coming from um baboons and how baboon's learned and you think baboons are very um have this ingrained, but they reached these tipping points where societies really changed. And so um, really getting uh at at at this work. Um, that's that's just fascinating. Any any fiction or is it strange? Oh? No, I love fiction. I'm a huge fan of Paul Auster.

Um what's the book. Uh, he's written this great series of books and starting books out there called the New York Trilogy. But he's written a great book called Invisible and he kind of again it's kind of postmodernist fiction, um, where you always kind of have to figure out what's the story. He writes them as detective stories, but they're much deeper than that. I love the stories by Raymond Carver. I wish I could ever write like Raymond Carver short

stories or Richard Ford. Um, the poetry of Marie How Um, you know, it's what the living do is amazing. Um you know there's uh so, there's just a lot of these, uh just wonderful books uh out there you're you're on a lot of planes, I am, um, but you just find time to read. Well if if you if you want to read a book, you have to carve out a specific time. Otherwise it's just not going to happen. There are too many of the distractions. Yeah, you know the TV. You know, just keep the TV off. Don't

get addicted to the Law and Order SVU. Actually, this is a good list. I think people are gonna have some good feedback about this. Um So, we've talked about the arc of quant and how it's changed. Um So, I don't know if I have to ask you what's changed since you joined the industry, But what might be a little more insightful for listeners is what changes do you see just beginning to happen. Now, what are the next major shifts that are already underway and we're just

not aware of them. Um. I think it's really this quant three point oh um as as I call it, which is really beginning to understand these disparate data sources that are out there and how we begin to use them and incorporate them into an investment process. Uh. And you know where, you know where that data is useful

and where that data isn't useful. And I think a little bit of what's been happening in the industry has been putting the horse before the cart, where there is this explosion of data as we were talking about, and at this point it's just so cool, like you can literally if you can imagine it, you can get the

data for it. Um. But we also need to slow down and start thinking about it from an investor's perspective of what is the data that I need that's going to help me with solve my investment controversy um and kind of turn things back on its head um. And that's what I'm hoping the industry will start to do and not just be data for data's sake and just consuming these vast amounts of it UM and ware housing it, but really think what's the what's what's the crux of

the question, what's the controversy? See? And then how do I go and get that data? UM? And and that can be in so many different forms of How do I read text that I want to figure out what are people talking about? UM? How do I read UM? How do I understand body language from reading the text of an earnings transcript? How do I infer even like the big questions and natural language processing is, how do

I look at double negatives or triple negatives? How do I begin to infer what you mean versus what you actually said? Like we can do that as humans. Sometimes sometimes a written word like on not that twitter is has anything to do with the real world, but I noticed that sarcasm or snark very often gets lost in the written word from from some intentions it does, UM.

And that's what makes email dangerous as a form of communication, and why sometimes it's better to pick up your instead of writing an emails, or if you're getting upset at an email, to actually pick up the phone and ask your colleague what do you mean? Let's let's have a quick conversation before you hit the send snarky reply back to them because they may have meant something quarterably different and you're not getting it. Like, so people have difficulty

interpreting actual written words. Can machines do what humans in this case can't do with human not today, not today. But that's the frontier of where we're moving, right, and that's what we have to do. We have machines today have trouble with just double negatives or triple negatives. It wasn't a great quarter, but it was okay, and it

exceeded our expectations, which were fairly modest. Get a machine to parse that, right, It sounds like all those clauses sort of contradict each one before, right, and getting to like you know what I'm trying to say, Get the machine to parse the full context of that sentence. See I don't own that company. Maybe maybe it depends upon what you want to do or not, right, you know, get a machine to know that when inflation comes in higher than expectations in Japan, Right, that's a good thing

as well in the present deflation environment. Sure, Right, So you have to teach machines context, You have to teach them nuance, you have to teach them the ability to understand when this is bad here but not bad there. It's the same, that's right. That's that's the frontier, um. And and that's all done through models, teaching model models for this to work within that's right now. I'm not saying that we're there by any stretch of the imagination, Please don't miss hear me on that. But I'm saying

that's where we're headed, right, um. And that's the frontier. And it's question. The question is how quickly with this explosion um in you know, kind of processing power and all the texts and all these things that are being digitalized, will we get there? Interesting? I kind of feel like I asked you this question. Tell me about a time you failed, because you described the so so succinctly the quant crash and oh seven. But is that a fair question.

Tell us about a time you've failed and what you learned from it, you know, Um, if you go back and actually read my third grade report card, yeah, um, it's amazingly talked about someone who hasn't really learned a lot from third grade um or changed their behavior. My third grade teacher said, Matthew always turns in his homework assignments late, but when he does it forest far surpasses our expectations. Do you still have a problem with tardiness?

Is that an ongoing UF? I've got it much better. Uh. But you know what I've had to learn over time is, as one of my bosses has put it, is not let the better be the you know, the enemy of the perfect. Right, you're the perfect me the enemy the better? Excuse me? Um? And so you know, uh, you know, get out version one, get out version two, get out version three. What was your the great technology line? Is? Um?

Good programmers ship? Is that the yes? So? So they I see people are coming into the studio before we get thrown out of here. Let me ask my two favorite questions. If a millennial or some recent college graduate where to come up to you and say, Hey, I'm thinking about going into quantitative research in finance, what sort of advice would you give them? Program? Program, program program

much more than statistics, applied mathematics, calculus. So so the so the thing that I want if I'm looking for my ideal candidate, they need to know how to program. They need to know how to to do statistics and econometrics. Um. Uh, they need to know finance right, and they need to be curious. UM. I can't teach you how to program right. I've tried to take people who have those other characteristics and teach a program utter failure um um. But you know how you also get people who are curious and

skeptical and skeptical of our own work. I think that's the biggest thing, Like realize that you're going to make mistakes and find the errors in your work before I find them. That's really interesting. And our final question, what is it that you know about quantitative investing today that you wish you knew fifteen or twenty years ago? You know, I guess I have a you know my my. My short answer to that is people need a much healthier respect for that effect that it's a model and it's

going to be wrong. Um. And understand that even the best models that are actually true UM or have very good uh performance are going to go through periods of of big underperformance. And that doesn't mean you get rid of the model. UM. You know, how do you diversify across those models? Uh? And kind of you know, work through those periods. I think is the biggest thing that everyone who's trying to invest in quant and really kind of has appreciation for quite fast nating. We have been

speaking with Matthew Ruthman. He is the head of quantitative equity Strategies at Credit Swiss. If you enjoy this conversation, be Shuan looked up an inchro down an inch on Apple iTunes and you could see any of the hundred and fifty or so such conversations that we have had previously. We love your comments, feedback and suggestions right to us at m IB podcast at Bloomberg dot net. I would be remiss if I did not thank Taylor Riggs for helping to produce the show and set up these interviews.

Michael bat Nick is our head of research. Medina Parwana is our audio engineer, who is our recording engineer. Today, Caroline O'Brien, Thank you Caroline for filling in last minute. I'm Barry Ritults. You've been listening to Masters in Business on Bloomberg Radio p