WEBVTT - Matthew Rothman 0:00:02.200 --> 0:00:06.800 This is Master's in Business with Barry Ridholts on Boomberg Radio. 0:00:08.720 --> 0:00:11.440 This week on the podcast, I have an extra special guest. 0:00:11.840 --> 0:00:14.960 His name is Matthew Rothman and he is the director 0:00:15.440 --> 0:00:21.400 of quantitative Strategies at Credit Swiss. He is really a 0:00:21.520 --> 0:00:25.040 fairly legendary guy in the world of quant He very 0:00:25.160 --> 0:00:30.040 specifically warned Lehman Brothers when he was a relatively new 0:00:30.120 --> 0:00:32.959 higher there about some of the problems that they were 0:00:33.000 --> 0:00:37.280 looking at with their quant strategies and ask questions that 0:00:37.360 --> 0:00:40.559 they really kind of dismissed and laughed at, What do 0:00:40.560 --> 0:00:42.559 you mean we might go out of business? That's the 0:00:42.640 --> 0:00:46.760 dumbest thing we've ever heard. He's also been profiled in 0:00:46.800 --> 0:00:51.240 a number of places. If you read Scott Patterson's The Quants, 0:00:51.320 --> 0:00:54.640 you can find him referenced throughout there. Pretty much the 0:00:54.720 --> 0:00:58.600 first guy to figure out what happened during the quant 0:00:58.680 --> 0:01:01.720 quake of of two thousand and seven. We're just about 0:01:01.760 --> 0:01:05.560 a decade past that, and so, uh, he was the 0:01:05.640 --> 0:01:08.800 one of the first people who really figured out how 0:01:08.840 --> 0:01:11.800 this happened, why it happened, and what it might mean 0:01:11.840 --> 0:01:15.720 going forward to the future of of short term trading 0:01:15.880 --> 0:01:20.679 and markets and companies like Lehman Brothers. It's it's one 0:01:20.680 --> 0:01:24.920 of those stories of someone who was unfortunately, much to 0:01:25.000 --> 0:01:27.560 his chagrin, proven right. He was kind of hoping he 0:01:27.640 --> 0:01:30.480 wasn't going to be right, but hey, that's what happened, 0:01:30.520 --> 0:01:33.759 and we have all sense lived with the consequences. So, 0:01:33.800 --> 0:01:41.120 with no further ado, my conversation with Matthew Rothman. My 0:01:41.200 --> 0:01:44.160 special guest today is Matthew Rothman. He is currently the 0:01:44.160 --> 0:01:48.360 head of Global Quantitative Equity Research at Credit Swiss. He 0:01:48.560 --> 0:01:52.560 is also a senior lecturer in Finance at the m I. T. 0:01:52.760 --> 0:01:57.520 Sloan School of Management. Prior to joining Credit Swiss, Matthew 0:01:57.600 --> 0:02:01.400 was the director of Global Quantitative mat Growth Research at 0:02:01.400 --> 0:02:05.280 a Canadian asset management in Boston, which was running approximately 0:02:05.400 --> 0:02:09.640 seventy billion dollars in assets. Before that, Matthew was the 0:02:09.680 --> 0:02:13.280 global head of quant research at Lehman Brothers and then 0:02:13.320 --> 0:02:18.440 continued on at Barclay's Capital after that acquisition post bankruptcy. 0:02:18.560 --> 0:02:22.799 He is the author of Turbulent Times in quant Lands, 0:02:23.200 --> 0:02:26.680 which was a research note during the quant crash in 0:02:26.720 --> 0:02:30.120 the summer of two thousand and seven that became the 0:02:30.160 --> 0:02:35.320 most highly distributed research note in Lehman Brothers history. Matthew Rothman, 0:02:35.560 --> 0:02:38.080 Welcome to Bloomberg. Welcome, Thanks so much for having me, 0:02:38.120 --> 0:02:40.920 Barry Um. So, I've been looking forward to this for 0:02:41.280 --> 0:02:45.240 quite a while. I knew of you from Scott Patterson's 0:02:45.240 --> 0:02:50.400 book The Quantz, and I was vaguely familiar with UM, 0:02:50.440 --> 0:02:53.560 the research piece that you would put out turbulent times 0:02:53.560 --> 0:02:57.120 in quant land. Let's let's start at a very basic 0:02:57.240 --> 0:03:02.040 level for the lay person. Please explain what quants strategies 0:03:02.200 --> 0:03:05.760 focus on. You know, so much gets grouped under the 0:03:06.040 --> 0:03:08.639 kind of rubric of quant today that you really kind 0:03:08.639 --> 0:03:10.640 of have to start to decompose it a little bit. 0:03:11.000 --> 0:03:14.079 And there are a variety of different quants, UM, you 0:03:14.240 --> 0:03:17.440 should begin to think about them via asset classes. Uh So, 0:03:17.560 --> 0:03:21.440 derivatives based quants are very different than fixed income general 0:03:21.440 --> 0:03:26.880 fixed income quants versus uh kind of equity quants versus 0:03:26.960 --> 0:03:29.760 risk modeling quants, and each one will come with a 0:03:29.800 --> 0:03:31.640 different kind of skill set and a different kind of 0:03:31.680 --> 0:03:36.480 approach to modeling. If you take equity quants just for 0:03:36.520 --> 0:03:39.120 a second, they also kind of come at a variety 0:03:39.360 --> 0:03:42.800 of forecasting horizons, and so they'll look at different types 0:03:42.800 --> 0:03:45.200 of signals and different types of things. So you have 0:03:45.280 --> 0:03:49.480 people who are playing literally in the millisecond range doing 0:03:49.560 --> 0:03:54.160 kind frequency very high frequency trading market making. Uh it 0:03:54.360 --> 0:03:57.280 literally in you know, trading hundreds of times in the 0:03:57.320 --> 0:04:00.440 blink of an eye, um down to people are holding 0:04:00.440 --> 0:04:03.960 intra day strategies, to people holding several day strategies, to 0:04:04.080 --> 0:04:07.640 people holding strategies at last months uh and and so 0:04:08.760 --> 0:04:10.840 you know, you can think about them having very different 0:04:10.840 --> 0:04:13.520 types of signals and very different types of performance, but 0:04:13.600 --> 0:04:17.440 what they all have in common is that they're forecasting returns. 0:04:17.480 --> 0:04:20.640 And what separates a quant in my book, really from 0:04:20.680 --> 0:04:23.919 a fundamental manager is that fundamental managers really try to 0:04:24.000 --> 0:04:28.440 understand the drivers behind the company. They talk to management, 0:04:28.440 --> 0:04:31.240 they think about products, They forecast earnings at the end 0:04:31.279 --> 0:04:34.000 of the day, and they think about a company as 0:04:34.000 --> 0:04:37.480 an organic unit. Quants think about returns and what are 0:04:37.520 --> 0:04:40.880 the drivers of returns. What is going to make h 0:04:41.000 --> 0:04:43.520 two returns, two stocks take the same way or go 0:04:43.600 --> 0:04:46.320 the opposite way over a long period time or baskets 0:04:46.320 --> 0:04:49.880 of returns, and so we we think about what drives 0:04:49.960 --> 0:04:52.720 returns more than anything and really abstract away from the 0:04:52.760 --> 0:04:57.840 companies themselves. So so I oversimplified, as the fundamentalists are 0:04:57.880 --> 0:05:01.800 telling the story and the qual are crunching numbers. Is 0:05:01.839 --> 0:05:04.880 that a gross of simplification or does it work? I 0:05:04.920 --> 0:05:07.000 think everybody crunches numbers. I wouldn't want to say the 0:05:07.000 --> 0:05:10.720 fundamentalists don't tell a story. Um, they're certainly, you know, 0:05:10.880 --> 0:05:14.440 trying to forecast cash flows and understand, uh, you know, 0:05:14.440 --> 0:05:17.520 what are the drivers of earnings and revenues uh, and 0:05:17.560 --> 0:05:21.600 then finally relate that back to us a stock price 0:05:21.720 --> 0:05:24.279 and what they think they're appropriate stock price would be. 0:05:25.160 --> 0:05:28.800 Quants don't try to do any of those things necessarily. 0:05:29.240 --> 0:05:33.559 They try to just forecast returns directly, uh, and see 0:05:33.839 --> 0:05:37.720 what can be those drivers of those returns and overall, 0:05:37.960 --> 0:05:41.880 for the most part, think about large baskets of returns 0:05:42.520 --> 0:05:46.320 or of stocks and how those characteristics and how those 0:05:46.320 --> 0:05:51.440 stocks behave based upon their return based characteristics. So you 0:05:51.600 --> 0:05:55.560 studied under Gene Fama, you got your PhD from Chicago, 0:05:56.160 --> 0:06:00.120 The really the home of the efficient market hypothesis? Can 0:06:00.160 --> 0:06:05.039 you square E M H with quantitative analysis? Are they similar? 0:06:05.279 --> 0:06:08.120 Or really? When I think of quants, I think of 0:06:09.920 --> 0:06:13.880 using powerful computers in order to try and beat the market. 0:06:14.040 --> 0:06:17.960 Or again, oh, you oversimplify. So I think the E 0:06:18.120 --> 0:06:21.240 M H is probably one of the most misunderstood concepts 0:06:21.720 --> 0:06:25.760 UM in finance. And Gene Fama's genius was that he 0:06:25.880 --> 0:06:29.960 really taught us how to think in a very rigorous 0:06:30.000 --> 0:06:32.480 way about what it means to be an efficient market 0:06:32.800 --> 0:06:35.760 and what it means to beat the market. Before Fama 0:06:35.839 --> 0:06:39.039 came along, there were people publishing studies all the time 0:06:39.440 --> 0:06:41.719 that said they had a strategy to beat the market. 0:06:42.080 --> 0:06:46.000 I think that drove Fama a little crazy, um, because 0:06:46.480 --> 0:06:50.000 the work wasn't very well done and the phrase beat 0:06:50.080 --> 0:06:55.440 the market um was very loosely applied. And what Farma 0:06:55.520 --> 0:06:57.080 really kind of taught us was that you have to 0:06:57.120 --> 0:07:01.320 think about risk uh and say, on a risk adjusted basis, 0:07:01.760 --> 0:07:05.120 can I beat the market? And then academics have debated 0:07:05.160 --> 0:07:08.919 for years what is the appropriate measure of risk? Is 0:07:08.960 --> 0:07:11.760 that the capital asset pricing model is that the Fama 0:07:11.840 --> 0:07:14.880 French three factor model. Uh? Is there something else that 0:07:14.920 --> 0:07:19.239 we're missing this now car hearts factor on momentum uh 0:07:19.320 --> 0:07:21.960 that is put in there. But academics have then debated 0:07:21.960 --> 0:07:25.200 are those factors anomalies or their proxies for risks? And 0:07:25.560 --> 0:07:28.120 you know, we spent fifty years more plus and academic 0:07:28.160 --> 0:07:31.560 circles debating what it means to beat the market with 0:07:31.640 --> 0:07:35.600 a risk adjusted return in Wall Street. Um. You know, 0:07:35.840 --> 0:07:38.880 there's been a generation of Chicago students and other students 0:07:38.920 --> 0:07:42.000 who have come to Wall Street uh, cliff as nous 0:07:42.000 --> 0:07:44.280 and crowd and many of the people's at Goldman's access 0:07:44.320 --> 0:07:48.520 at Management and then fanned out across the street in 0:07:48.520 --> 0:07:51.480 in in in many ways. Um. And what we've all 0:07:51.560 --> 0:07:54.600 kind of been trained in these methods, not only Chicago 0:07:54.640 --> 0:07:56.880 but other schools as well. And what we've really brought 0:07:56.920 --> 0:08:01.520 to Bears is kind of very hardcore, rigorous academic quasi 0:08:01.600 --> 0:08:05.280 academic background to how we think about can you make 0:08:05.320 --> 0:08:08.920 money as a quant and what does that mean? Uh? 0:08:08.960 --> 0:08:12.000 And and so you know, Wheel spend less time arguing 0:08:12.000 --> 0:08:14.280 about is something risk or is it a miss pricing? 0:08:14.640 --> 0:08:17.040 Is it an anomaly? Um? Doesn't that's that mean the 0:08:17.040 --> 0:08:20.280 market is efficient or less efficient? But you know, we 0:08:20.280 --> 0:08:24.200 we bring that same kind of sensibility that Fama taught us, UM, 0:08:24.240 --> 0:08:27.480 but we'll get less involved in the academic, you know, 0:08:27.720 --> 0:08:31.200 debate about risk versus miss pricing. So let's talk a 0:08:31.200 --> 0:08:34.520 little bit about building a quant team. You're hired at 0:08:34.520 --> 0:08:38.480 Credit Swiss to help put a team together what goes 0:08:38.520 --> 0:08:42.520 into that? How do you first begin to assemble a 0:08:42.600 --> 0:08:45.120 QUANT team. I think the first thing that you need 0:08:45.120 --> 0:08:46.680 if a quant, if you're going to be a quant, 0:08:47.160 --> 0:08:51.280 is a combination of data and technology. So you need 0:08:51.360 --> 0:08:53.040 to kind of go out and figure out what are 0:08:53.040 --> 0:08:55.079 the big databases that you need where you're gonna get 0:08:55.080 --> 0:08:58.959 your information and what is you're diversified information set going 0:08:59.000 --> 0:09:01.360 to be what you think you're edges and go about 0:09:01.480 --> 0:09:05.559 procuring that. So you're you're building hardware and software, You're 0:09:05.600 --> 0:09:08.599 hiring programmers, You're hiring programmers, you've got you have to 0:09:08.679 --> 0:09:10.880 hire data scientists and people who are going to really 0:09:11.080 --> 0:09:13.520 an overuse term, but people are going to really understand 0:09:13.520 --> 0:09:17.600 how to manage and curate and store your data. And 0:09:17.600 --> 0:09:19.680 then you have to find researchers who know what to 0:09:19.720 --> 0:09:21.680 really do with that data and where to find and 0:09:21.679 --> 0:09:24.440 where to find those hidden gems of signals and come 0:09:24.520 --> 0:09:26.800 up with ideas. And then you actually need people who 0:09:26.800 --> 0:09:29.440 can communicate it. So so this isn't anything that gets 0:09:29.480 --> 0:09:32.840 put together very quickly. This is a long processes, this 0:09:32.960 --> 0:09:35.600 is a long process. When when Credit Swiss comes to 0:09:35.600 --> 0:09:37.880 you and says, hey, Matthew, we want to build a 0:09:38.000 --> 0:09:39.959 quant team. Do you say, all right, it's gonna take 0:09:40.160 --> 0:09:43.600 five years, two years, How how do you put them 0:09:44.400 --> 0:09:47.280 into the proper mindset for this? I say, you've probably 0:09:47.280 --> 0:09:49.679 got to give me twelve to eighteen months and think 0:09:49.720 --> 0:09:51.600 that I'm going to be in a dark cave, um 0:09:51.600 --> 0:09:53.160 and you're gonna see nothing from me. And I'm gonna 0:09:53.200 --> 0:09:56.400 be asking you for big checks uh and and and 0:09:56.600 --> 0:09:59.520 hiring people and kind of layout a business plan very 0:09:59.559 --> 0:10:02.640 carefully uh and can detail the costs and exactly what 0:10:02.679 --> 0:10:04.880 I need. Um. And you got to make sure that 0:10:04.920 --> 0:10:07.760 they're in it and get and get the ask because 0:10:07.760 --> 0:10:09.760 it's a heavy ask, but what you can get out 0:10:09.760 --> 0:10:11.240 of it is pretty cool at the end of the day. 0:10:11.440 --> 0:10:16.720 So the competition for the really skilled fill in the 0:10:16.760 --> 0:10:23.520 blank programmers, researchers, data scientists. I think about the just 0:10:23.600 --> 0:10:28.120 a giant collection of PhD s at Renaissance Technologies, long 0:10:28.160 --> 0:10:30.400 before the rest of Wall Street started thinking in those 0:10:30.520 --> 0:10:33.679 terms that that's gotta be you said, big checks, that's 0:10:33.720 --> 0:10:37.160 gotta be a serious commitment made by the firm to 0:10:37.160 --> 0:10:39.840 to build something like this out. It is definitely a 0:10:39.880 --> 0:10:44.160 serious commitment by Credit sweet um uh. And they understand 0:10:44.200 --> 0:10:46.840 that much of the world is really moving this way. 0:10:46.920 --> 0:10:51.040 And from the firm's perspective, what I believe they understand 0:10:51.120 --> 0:10:53.160 is that we need to be able to deliver content 0:10:53.760 --> 0:10:57.280 um to those firms that you're mentioning. Uh, that is 0:10:57.360 --> 0:11:00.079 interesting to them. Uh. The way we deliver fundament to 0:11:00.240 --> 0:11:03.199 research to the biggest asset managers in the world out there, 0:11:03.240 --> 0:11:07.000 we need to deliver quantitative research along those same domains 0:11:07.320 --> 0:11:09.400 and so yes, it's a big ask if you're going 0:11:09.440 --> 0:11:12.800 to be additive to those people's process. Uh, and you know, 0:11:12.840 --> 0:11:14.800 play with them in the sandbox. Is it is it 0:11:14.960 --> 0:11:18.280 that competitive to hire people? I was joking a little bit, 0:11:18.640 --> 0:11:21.480 but I'm assuming that these folks are really in demand 0:11:21.640 --> 0:11:24.080 and there is no you know, you can't really do 0:11:24.120 --> 0:11:26.400 this on the cheap. I don't think that you can 0:11:26.480 --> 0:11:29.880 do this on the cheap. But you know, you need 0:11:30.440 --> 0:11:33.200 you need a relatively well sized staff. But you know, 0:11:33.320 --> 0:11:36.040 we're not going to be rentech. We don't think about that. 0:11:36.120 --> 0:11:40.760 You know, we don't need that size staff. No, no, no, no, no, no, 0:11:40.760 --> 0:11:42.760 no no no. That I you know, I think you 0:11:42.840 --> 0:11:46.040 need a staff of probably five to seven good researchers 0:11:46.080 --> 0:11:48.640 to be able to produce something interesting. Uh. You need 0:11:48.880 --> 0:11:51.840 a technology team of three to four people. Uh, you 0:11:51.880 --> 0:11:54.720 need a data team of probably another two to three people, 0:11:55.120 --> 0:11:57.320 um to really four people to really kind of begin 0:11:57.360 --> 0:11:59.760 to curate what you're doing. So it's you know, not 0:12:00.040 --> 0:12:02.400 crazy um in any sense, but you can be very 0:12:02.440 --> 0:12:05.440 productive and produce really interesting research on the cell side 0:12:05.440 --> 0:12:07.760 with that size team. So in one of your notes 0:12:07.800 --> 0:12:11.880 you mentioned quant one point oh um, referring to the 0:12:12.080 --> 0:12:15.080 quant quake of of the summer of two thousand and seven. 0:12:15.960 --> 0:12:18.560 What does quant two point oh and quant three point 0:12:18.559 --> 0:12:21.160 oh look like? What are the changes that that are 0:12:21.320 --> 0:12:24.520 taking place and will take place? So Quant one point 0:12:24.520 --> 0:12:27.120 I really ended I think in the summer of August 0:12:27.120 --> 0:12:31.439 two thousand and seven, where there were rather simplistic strategies 0:12:31.480 --> 0:12:34.720 that a lot of people were using, and we turned 0:12:34.720 --> 0:12:37.320 on the light in the room and saw everyone else 0:12:37.320 --> 0:12:40.360 who was there and realized that we needed to do 0:12:40.440 --> 0:12:44.480 things to diversify ourselves from each other. Um. And so 0:12:45.000 --> 0:12:48.240 we've seen that really over the past eight to nine years, 0:12:48.600 --> 0:12:51.720 where people really started to think in different ways, not 0:12:51.800 --> 0:12:54.920 even so much about uh forecasting returns, because I don't 0:12:54.920 --> 0:12:57.760 think we were all that similar there, but really about 0:12:57.800 --> 0:13:01.719 how we access liquidity in the market, how we optimize 0:13:01.720 --> 0:13:06.040 our portfolios, how we thought about risk, how we put 0:13:06.320 --> 0:13:09.440 you know, factors together. Could we time factors? Could we 0:13:09.520 --> 0:13:14.439 not time factors? How you incorporate macro information into your forecast? 0:13:14.800 --> 0:13:18.480 And so people really started to break the paradigm in 0:13:18.720 --> 0:13:24.079 a lot of ways. Uh, still within relatively traditional framework, 0:13:24.360 --> 0:13:27.880 but begin to really push that envelope, you know, kind 0:13:27.880 --> 0:13:31.280 of doing simple screening was no longer enough. So some 0:13:31.360 --> 0:13:34.240 of the criticism, and I'm pulling a line, this is 0:13:34.280 --> 0:13:38.880 actually an academic white paper, our our quansole fishing in 0:13:38.920 --> 0:13:42.640 the same small pond with the same tackle box, implying, hey, 0:13:42.679 --> 0:13:45.520 these were all crowded trades. Everyone was more or less 0:13:45.559 --> 0:13:49.320 using the same tools and pursuing the same goals. Was 0:13:49.360 --> 0:13:53.079 that true back then? And is it still true today? 0:13:53.240 --> 0:13:55.160 You know, it's one of the criticisms that get leveled 0:13:55.160 --> 0:13:58.520 at quant that infuriates me the most. Um. You never 0:13:58.559 --> 0:14:01.560 hear people say that to fundament to analysts. Right, you're 0:14:01.559 --> 0:14:04.400 all listening to the same press conference, You're are reading 0:14:04.400 --> 0:14:06.600 the same earnings report, you're all talking to the same 0:14:06.640 --> 0:14:11.760 investor relations person, so therefore you must all be the same. 0:14:12.280 --> 0:14:14.720 So I think it's one of those great misunderstandings about 0:14:14.760 --> 0:14:16.719 quant is that just because you look at the same 0:14:16.840 --> 0:14:20.000 data or studied under gene Fama, Uh, you must all 0:14:20.000 --> 0:14:23.240 be the same. Um. And let me kind of give 0:14:23.240 --> 0:14:27.200 you an example of how even quants can be different, 0:14:27.520 --> 0:14:29.880 even though on the outside they may look the same. 0:14:30.400 --> 0:14:34.400 So quants not surprisingly like to buy cheap things, um, 0:14:34.440 --> 0:14:36.240 and the hope that they'll go up in value. I 0:14:36.280 --> 0:14:38.600 really don't know any investor who likes to buy expensive 0:14:38.640 --> 0:14:40.480 things and think that it's going to go down in 0:14:40.560 --> 0:14:45.600 value high but sell high. But I don't know anyoneho 0:14:45.600 --> 0:14:47.600 wants to buy high and sell low. No, right, not 0:14:47.920 --> 0:14:50.560 a great strategy to make money. But when you're a 0:14:50.600 --> 0:14:53.800 quant and you say that you want to buy something 0:14:53.840 --> 0:14:56.720 that's cheap, well, you're programming that, and so you have 0:14:56.800 --> 0:15:00.240 to all of a sudden be really really precise ice 0:15:00.560 --> 0:15:04.440 on what you mean by cheap. Do you mean it's 0:15:04.560 --> 0:15:07.320 cheap on a pe ratio? Do you mean it's cheap 0:15:07.360 --> 0:15:09.360 on a book to price ratio? Do you mean it's 0:15:09.400 --> 0:15:12.320 cheap because of sales to price? What metric are you 0:15:12.400 --> 0:15:16.800 even using to define cheapness? Right, you've got to program 0:15:16.880 --> 0:15:19.520 that into the computer, and then you've got to say, 0:15:19.600 --> 0:15:22.000 do you mean it's cheap relative to its own history? 0:15:22.360 --> 0:15:24.840 Do you mean it's cheap on a sales surprice ratio 0:15:25.120 --> 0:15:27.760 compared to every other stock in the market. Do you 0:15:27.800 --> 0:15:30.280 want to do its sector relative? Do you want to 0:15:30.440 --> 0:15:35.120 do it country relative? What do you mean? Um? And 0:15:35.360 --> 0:15:39.440 God is in the details of a lot of these things. UM. 0:15:39.560 --> 0:15:41.560 If I'm gonna look at a book to price ratio, 0:15:41.840 --> 0:15:46.120 do I just book values for differences in gap standards 0:15:46.160 --> 0:15:49.840 in different industries or not? Do I? How do I 0:15:49.880 --> 0:15:53.360 correct for book value under i f R S accounting 0:15:53.360 --> 0:15:56.960 International accounting standards versus gap standards? Do? How do I 0:15:57.360 --> 0:16:00.600 handle all these things? So even a look like you're 0:16:00.600 --> 0:16:02.960 just doing the same thing, Oh, I'm using book to price, 0:16:03.240 --> 0:16:06.840 there can be a tons of details. Let's talk a 0:16:06.840 --> 0:16:09.320 little bit about that period of of oh eight oh nine, 0:16:09.320 --> 0:16:12.640 because you know it's almost ten years ago to the 0:16:12.720 --> 0:16:18.400 day when that weekend that shook the entire financial firmament 0:16:18.880 --> 0:16:22.200 took took place. I've read a couple of your older 0:16:22.880 --> 0:16:25.440 research notes, and I have to ask you the question 0:16:26.120 --> 0:16:32.360 what actually caused the financial crisis and market crash? You know, 0:16:32.400 --> 0:16:35.080 I think the great place um to start, as Andrew 0:16:35.160 --> 0:16:38.320 Ross Sorkin's book Too Big to Fail. If you're really 0:16:38.360 --> 0:16:41.200 interested in kind of the inner workings of Lehman. Uh 0:16:41.320 --> 0:16:45.240 during that time, he nailed it. Um, it's a great read. 0:16:45.440 --> 0:16:47.960 I couldn't put it down. My wife kept, you know, 0:16:48.000 --> 0:16:50.120 like nudging me, like put the book down. You've lived this, 0:16:50.200 --> 0:16:51.480 like why do you have to read this? And I 0:16:51.560 --> 0:16:53.520 was like, oh no, he's got details in here that 0:16:53.600 --> 0:16:56.880 like some of us were trying to find out. UM, 0:16:57.480 --> 0:17:00.480 research one into that you could see in the suffis 0:17:00.600 --> 0:17:03.720 he got access to and got people talking. That's really 0:17:03.840 --> 0:17:07.640 quite remarkable. Um. I think that there was definitely some 0:17:07.720 --> 0:17:12.160 level of mismanagement at the top, as as he documents, uh, 0:17:12.359 --> 0:17:14.720 not just Lehman Brothers, but across the board, but across 0:17:14.760 --> 0:17:19.560 the board, a misunderstanding of risk. Uh. And it's very 0:17:19.600 --> 0:17:23.320 hard to know when the music is going to stop, 0:17:23.440 --> 0:17:27.640 as it were, when successful businesses have run their course. Um. 0:17:27.680 --> 0:17:30.919 If you remember nine months back, Lehman Brothers was putting 0:17:30.960 --> 0:17:35.040 up record earnings. Uh, And so how do you know 0:17:35.320 --> 0:17:37.880 that it's time to get out of that business. It's 0:17:37.880 --> 0:17:40.760 a really hard call to make. So so let's talk 0:17:40.800 --> 0:17:44.159 about six or nine months back. I read and I 0:17:44.160 --> 0:17:47.199 don't think this was Sorkin's book. I think it was Patterson's. 0:17:47.280 --> 0:17:51.639 The Quants. You had submitted some memos to senior management, 0:17:52.040 --> 0:17:53.920 sort of saying, hey, guys, you gotta wake up. Is 0:17:53.960 --> 0:17:57.159 a ton of risk here, and it seemed like you 0:17:57.320 --> 0:18:01.000 had a sense there were problems coming long before much 0:18:01.040 --> 0:18:03.080 of the street figured it out. I think you're being 0:18:03.119 --> 0:18:06.840 overly generous to me on that one. I think that 0:18:06.880 --> 0:18:10.600 there were definitely things that concerned me. Did you ask someone, 0:18:11.040 --> 0:18:14.360 and again maybe this is Patterson's book, didn't you say, well, 0:18:14.359 --> 0:18:17.360 what what are the contingencies in case Leman goes bankrupt? 0:18:17.600 --> 0:18:19.399 And people laughed at you, They looked at you like 0:18:19.440 --> 0:18:23.200 you were crazy. Um, there were times that there were 0:18:23.240 --> 0:18:26.199 things going on that disturbed me. I'll give you I'll 0:18:26.240 --> 0:18:29.600 give you a little anecdote. There's a great paper by 0:18:29.640 --> 0:18:33.800 a professor um Owen Lamont at the Harvard Business School 0:18:33.800 --> 0:18:35.959 and used to be at the University of Chicago, and 0:18:36.040 --> 0:18:40.080 he did a study that found that firms who get 0:18:40.119 --> 0:18:45.840 into fights with their short sellers, like the time those 0:18:45.880 --> 0:18:50.360 firms end up going bankrupt. They're in trouble, nothing else 0:18:50.400 --> 0:18:54.120 to do but with the shorts like like stop right, 0:18:54.240 --> 0:18:56.440 you know, Um, and and he documents some of these 0:18:56.480 --> 0:18:58.920 and they're great anecdotes in there. And if you remember, 0:18:59.000 --> 0:19:02.240 towards the end of Lean and Brothers, UM Management got 0:19:02.280 --> 0:19:05.639 into a fight with one of our with David Einhorn. 0:19:05.800 --> 0:19:10.199 I believe UM, And when I yes, it was, it 0:19:10.280 --> 0:19:14.280 was contested. And I did send UM that paper along 0:19:14.880 --> 0:19:20.480 to senior people. And picture, wait, this guy sending me 0:19:20.520 --> 0:19:23.679 a Harvard Business School white paper and arguing with shorts. 0:19:23.920 --> 0:19:27.280 Doesn't he realize our very foundation is under assault. I 0:19:27.320 --> 0:19:30.520 could just picture the c suite response to that. UM 0:19:30.760 --> 0:19:33.080 was this head sending me a white paper? What is 0:19:33.119 --> 0:19:38.200 this except except saying, these people do this, You're scaring me. UM. 0:19:38.680 --> 0:19:41.080 I was lucky that my boss was a PhD from 0:19:41.080 --> 0:19:44.320 the University of Chicago as well. He appreciated his kind 0:19:44.320 --> 0:19:47.320 of things. Uh, he got it. Uh. And and I 0:19:47.359 --> 0:19:48.760 think we actually, you know, I don't want to say 0:19:48.760 --> 0:19:50.520 we stopped fighting with him, but we did stop fighting 0:19:50.520 --> 0:19:52.000 with him, and I think we did start to content 0:19:52.119 --> 0:19:55.240 rate on different things. We had some very talented, you're 0:19:55.280 --> 0:19:57.760 bright people there. You're literally on the way to London 0:19:57.840 --> 0:20:00.359 to a conference when you get a phone call. All 0:20:00.560 --> 0:20:03.199 you're in JFK. You get a phone call on the 0:20:03.200 --> 0:20:06.720 other side of security. Hey, tap out, you gotta come back. 0:20:07.160 --> 0:20:10.520 You go home to New Jersey. You take not your 0:20:10.640 --> 0:20:14.720 little car but your wife's station wagon with the presence 0:20:14.720 --> 0:20:16.840 of mind too. I gotta go clear up my office. 0:20:17.119 --> 0:20:22.359 And you're described as this lucid rational reason like you weren't. 0:20:22.440 --> 0:20:25.119 Oh what a shock, what a surprise. This seemed to 0:20:25.119 --> 0:20:29.160 be something that you apparently had thought out before, where 0:20:29.200 --> 0:20:31.919 most people more or less seem to be shocked or 0:20:31.960 --> 0:20:35.080 panicked or both. How do you and is that again, 0:20:35.080 --> 0:20:39.520 am I oversimplifying this? Or um? Well, I was certainly emotional. Um, 0:20:39.640 --> 0:20:41.360 I don't want to say that that wasn't a very 0:20:41.359 --> 0:20:45.159 emotional night for me. Um. You know, one of the 0:20:45.160 --> 0:20:48.760 things that I think, you know, behavioral economists and other 0:20:48.760 --> 0:20:50.679 people tell you, is that the closer you are to 0:20:50.720 --> 0:20:53.760 a situation, the harder it is for you to kind 0:20:53.800 --> 0:20:58.000 of take that step back rationally and see what's going on. 0:20:58.680 --> 0:21:01.520 A lot of Lehman management lived through ninety four and 0:21:01.640 --> 0:21:05.840 had lived through other crises, and really we're very, very, 0:21:05.960 --> 0:21:10.280 very close. I was relatively new at Lehman and so 0:21:10.760 --> 0:21:14.680 kind of had a little different perspective more objectivity than 0:21:14.720 --> 0:21:18.080 then they did. About the situation. I think that was 0:21:18.240 --> 0:21:21.640 part of part of the difference. Um and just being 0:21:21.720 --> 0:21:25.000 kind of a little just more unsentimental. Let's talk a 0:21:25.040 --> 0:21:28.240 little bit about the quant crash of two thousand and seven. 0:21:28.800 --> 0:21:33.440 I love the story about you and Austriel Levin figuring 0:21:33.480 --> 0:21:38.720 out what actually had happened long before anybody else. Uh. 0:21:38.920 --> 0:21:42.480 Was this over sushi or Chinese food in San fran sushi? Okay, 0:21:42.560 --> 0:21:45.080 it was a sushi dinner. Um. I've always felt badly 0:21:45.160 --> 0:21:48.200 that um ozreal Levine to his friends as known as susy. 0:21:48.680 --> 0:21:50.600 You know, he really should have been the co author 0:21:50.640 --> 0:21:53.600 with me on that paper and deserves every bit of credit. Um. 0:21:53.840 --> 0:21:55.520 What was he working at the time? What was the 0:21:55.560 --> 0:21:59.200 place called Menta Capital? He's still there. Uh. And he 0:21:59.840 --> 0:22:03.600 used to run b g I's hedge fund um main um, 0:22:03.640 --> 0:22:05.960 you know main hedge fund over there, and he had 0:22:06.000 --> 0:22:08.240 started on his own. Uh. And you know I'd have 0:22:08.240 --> 0:22:10.720 been out that day seeing clients and watching the blow 0:22:10.840 --> 0:22:14.200 up happening, and like we both were just sitting there 0:22:14.280 --> 0:22:18.240 over sushi, um, and like just kind of piecing together 0:22:18.400 --> 0:22:23.160 what would have caused everyone to unwind? Um? And it 0:22:23.200 --> 0:22:25.920 was literally just over sushi dinner. Just arguing it back 0:22:25.960 --> 0:22:29.400 and forth and kind of putting together what the story 0:22:29.920 --> 0:22:32.160 had to have been. So tell that story, because it's 0:22:32.160 --> 0:22:37.480 fascinating how you guys deduce leverage multi strat. So the 0:22:37.480 --> 0:22:39.919 story that we that that that that we kind of 0:22:39.960 --> 0:22:42.680 came up with um and still holds up to this day. 0:22:42.760 --> 0:22:45.120 No one's you know, we can't prove it, um, but 0:22:45.400 --> 0:22:47.679 no one has a better story UM. And you know, 0:22:47.720 --> 0:22:51.440 it's kind of become accepted wisdom is that there were 0:22:51.440 --> 0:22:56.520