Interview With Emanuel Derman: Masters in Business (Audio)

how things have developed today. Professor Emmanuel Derman is the head of Financial Engineering UH coursework in the master's program at Columbia University. His background is really quite amazing, and I go into a lot of detail UH in the program. Suffice it to say at Goldman Sachs, where he UH

eventually became head of the renowned quantitative Strategies group. Gives you an insight of of what sort of UH mathematical and programming background he has helped Goldman Sacks make ungodly gobs of money by the intelligent application of modeling and

risk management. A lot of people don't think about the blocking and tackling and the basic approach that you have to engage in when you're dealing with things like stock spoons and options of a liquid assets that that are hard to come up with a price because they don't necessarily trade all the time anyway. Really a fascinating conversation.

If you're at all interested in in quantitative finance and modeling, um it's and you're not a student at Colombia, you don't often get access to someone like Professor Norman, and I think you'll find this to be a really interesting conversation. And so, without any further ado, my interview with Professor Emmanuel Derman. This is Masters in Business with Barry Ridholts on Bloomberg Radio. My special guest this week is a

Manuel Derman. He is a particle physicist and better known in the world of finance as a quant His background is quite fascinating. Born in South Africa, came to the United States later in life, spent seventeen years on Wall Street, eventually becoming head of the renowned Quantitative Strategies group at Goldman Sachs, where he co developed the Black Derman Toy interest Rate model and the Derman Connie local Volatility model,

both of which have become industry standards. He won the I a f E. Sun Guard Financial Engineer of the Year Award in two thousand. He's the director of the Master's Programming in Financial Engineering at Columbia and author of two books, the first, My Life as a quant Reflections on Physics and Finance, and more recently, Models Behaving Badly. Emmanuel Derman, Welcome to Bloomberg. Thanks, I'm very glad to be here. So you're someone whose career I have followed

for a long time. Uh, and I would imagine a lot of our listeners are are probably not familiar with either yourself personally or what you do. So let's start with a really basic question. What is financial engineering? Okay, financial engineering, it's sort of a polyglock field. It's not really one simple thing. It developed over the last twenty

five or thirty years. Um. It's a mix of building models for describing businesses and more particularly securities, involving mathematics, artistics, the use of computer science programming, all inspired somewhat by physics or scientific type models, and using the kind of math that's traditionally used in describing the inanimate world of the material world, but now applied, for better or for worse,

to the world of stocks, bonds, securities, options, etcetera. So so that naturally leads to the next question, how does someone transition from being a specialist in particle physics to a specialist in quantitative finance. Yeah, it's like how do you get to Carnegie whole practice practice practice. So then, so there are obviously some similarities math. Yes, so a

lot of a lot of them. Modeling in particularly in options and in describing fixed income instruments actually has its origin in in economists who were trained in physics and started applying that kind of math to physics or stochastic calculus series discunning already are physics, mathematics and pargular physics in spired models, and so it's kind of a fairly natural transision for people to make. The trouble is a lot of physicists don't have the economics of the finance background.

And thirty years ago that wasn't a problem. When I did it, um, they didn't expect you to know anything. The whole quantitative finance field was sort of amateur heaven. You just came along and people told you to pick up. It happened to me when I came to Golden they said, read the Cox Ross rubens Steen model for pricing options and start working. And now so it was fairly easy in though, is that they hired you if you had potential.

Now it's a much tougher transcision. It's become a professional field. I'm in charge of a program at Columbia where people get degrees in the field, professional degrees. And you can't. You can't just from an economic point of view. There's a glut of people not so easy to get in now. So when we look at physics, you're dealing with inanimate objects that aren't pushing back. They're not getting excited about rumors,

they're not getting panicky. But in finance you have the individual players who are all suffer from cognitive foibles and emotional extremes. How do you adapt to that shift from particles which are pretty clean and interesting to humans which are messy and volatile. Yeah, that's um. You put your finger on the key problem in the whole field, which is some people. Yeah, people exactly. And and that's why

the models don't really work as well. When you make a model, even Newtonian mechanics for describing how planets go around the Sun, the planets don't really care what you say about them, and if you publish an article about them that don't change their position even if you have the wrong theory about them. But really, if you look at what happens in financial markets, they interact with people in financial markets all about opinions, and opinions affect the

future and affect the presence. So um, it's it's a much iffier field. And models don't work as well. They don't describe the system is accurately, but they're not useless. That's the famous George Box quote. All models are wrong, but some are useful. Obviously, if you have a useful model in finance, it can make a lot of money for for its owners. Um, how does somebody like George Soros and his theory of reflexivity, how market prices subsequently

affect market prices? How does that impact someone who's trying to create a financial model. Well, I think it impacts I think most financial models don't really take a kind of reflexivity. There's some some models. Maybe you're starting to do that, but I wouldn't say they trading models. Um. If I can summarize in one sentence, I would say that anytime you build a model, it's a financial model

is mathematical. You have to keep in the back of your head that you're actually short volatility, meaning if the world changes, your model is going to be wrong. It may make money for you, may lose money for you, but it's certainly going to be wrong. Most models only work in a very narrow regime where things are more

or less like the world you're in currently. And you see when you get to negative interest rate, for example, or when volatility blows up, or when you get you know, the Great Financial Crisis, all of these models stopped working. Since you you hinted at this in terms of a changing regime, I'm thinking about the models that were put together by a firm like Long Term Capital Management in the late nineties, in the last minute we have. Is that why they blew up so spectacularly The world changed

and their model failed to adapt plus hundred one leverage. Yeah. I think my impression of what happened to Long Term Capital was that they were basically looking for very small deviations most things that earn your money in market, So when you buy something liquid, because the liquid things tend to be cheap, and so they were buying off the

run treasuries out of the money options. I don't know the details anymore, but essentially things that were cheap, but in the long run would revert to the mean and give you the full amount. But they leveraged up like crazy to accentuate small pennies differences. And then when Russia defaulted, what happened was everybody in the world got scared. There's a flight to quality. Nobody wants to hold the liquid stuff, and they were so overleveraged that they were they were

put out of business. I'm very ridults. You're listening to Masters and Business on Bloomberg Radio. My special guest today is a Manuel Derman. He is a professor of financial engineering at Columbia University. He used to work at Goldman Sachs where he was head of the Quantitative Strategies group. And I want to talk a little bit about a book you wrote a few years ago after the financial

crisis called Models Behaving Badly. And one of the first things that stood out to me from that book was sentence you had written, models are metaphors that explain the world we don't understand in terms that we do understand. Expand on that. Okay, Um, yeah, that's that's some that's

my feeling. I can give an example. So what I tried to do in that book was distinguished between models and theories, as as different means of approaching trying to explain the world animate or inanimate, and models seemed to me to the analogies in the sense, for example, you say the brain is like a computer, or the computer is like a brain. People used to say now that now they go the other way. Or they say the

heart is like a water pump. Or there's a great quote I like about fixed income from Schopenhauer where he says, sleep is the interest that we have to pay on the capital which is called in a death. And the more regularly the interest is paid, the further the date of redemption is postponed. So in other words, it's important to get eight hours nights. Yes, and if you can

get eight hours and I'd sleep. But if you think about what he's doing, he's sort of there's a small overlap between sleep and between paying interest on a bond, which is that you sleep regular and you pay interest on a bond. Rey that's really only analogy. And then but on a bond, you've borrowed money and you have to pay it back at the end. And now he's saying, you've borrowed your life from the darkness, and at the end you have to pay it back again, and you're

paying back darkness all the way. So I think that's a good example. And most of the models in finance or analogies where you say, for example, um in the Kape model or in or in in modern finance, you say stock prices behave like smoke diffusing from from a cigarette end, you know, doing Branny in motion. That's not

literally true. It could be true, but it isn't. So in other words, stock prices don't randomly fill a room evenly distributed like the behavior of a gas right, But you're creating a metaphor that, hey, it's somewhat random, it's a little predictable, but not very predictable, especially over the long term exactly. And and for example, in physics, that's called Brannyan motion, this motion of diffusion in physics, that really is a theory. It's an accurate description of the

way smoke behaves. But stock prices don't behave that way. Volatility jumps, stock prices crash, apple rises are dumps buck twenty or thirty bucks in a day more actually son of ten percent in a day, So it's not it's not a diffusion, it's something that's that's violent. Um, So why do we why are we so enamored of all these metaphors because we can't do better? And the difference

is I think economists don't understand. I'll to not be too rude about economists, but they don't understand the difference between a model and a theory, or a metaphor and an accurate description. And physicists, which is my original background, actually understand that very well. So for example, if you say Newton's laws, which destrive the planets going around the Sun, they say four sequals mass times acceleration. That's really accurate.

It may not be perfect, but it's very accurate. On the other hand, they might say a nucleus at the center of an atom is like a liquid drop, and they understand that when they say a nucleus is like a liquid drop, it isn't really a liquid drop. It's just a lot like it. But at some point that analogy is going to break down, and people get Nobel prizes for saying a nucleus is like a liquid drop, and they get Nobel prizes for doing what Feynman did,

which is an accurate description of electrons. But but physicists understand that one of them is truth or close to truth, and the other one's just a model. So let me go off script and say, um, how accurate is it to say that economists suffer from physics envy? Uh? I don't know who originally coined that. I think maybe Andy Low, who's a professor at M I T. I think that's accurate. I think maybe it's fading to some extent now The behavioral finance people clearly don't. But but yeah they do.

And you know Bachelier, the guy who started a lot of the stuff, and Paul Samuelson, they were all ahead of physics smith background somewhere in there. And I noticed that you drop a lot of philosophical references in discussion. Is that from the financial engineering side or is that from the physics side. Um, I've got a late life interest in philosophy. Well, it's throughout your books. I notice you're you're always referring to that. We'll come back to

that a little later. Um, let me give you a quote of yours that I found intriguing and wildly overlooked by a lot of investors. If you want to take a chance on the upside, you have also to take a chance on the down side. Explain what you mean

by that, what I mean by that. There was a response to I think you wrote a book about this, two to the bailouts of two thousand seven eight nine, when I felt that there were a lot of companies around, banks in particular, but financial companies, that we're taking risk and saying that's the essence of capitalism and sort of leave me alone and let me, let me do what I'm good at. And then suddenly, when everything collapsed, they

wanted to be saved from death. And I found that incredibly, I don't know whether ethically or morally, sort of yeah, and saying, oh, the whole system will die if you don't save us. And and that's what I meant. If you want to be somebody that benefits from taking risk, you also have to benefit when taking risk kills you. So in other words, you can't have privatized gains and

socialized laws. Yes, and I like to think that what a lot of the banks should have done if they were saved by the saved by the government, was they were essentially given a put if you look at it from an options point of view, they were safe from death, and if they were given a put they should have given away a call to the when they were When they survived, companies had a little a I G. Gave the government some uh, I mean it was forced upon them, and then the G S, C S, Fannie and Freddie

they essentially became Yeah, yeah, so that's fair. You can't get ten million dollar bonuses the rfter you was saved from. Makes sense that should we have nationalized those companies, cleaned them up, and then spun them out as free standing companies with the benefits of those I p o s going to the taxpayer? Is that a better ethical way to do that. I think it's a better ethical way to do it. I have friends who argue with this about him. They say the whole system would have collapsed

if we hadn't bailed them out. Um, people are not what the FED who say everything was credit was on the verge of sort of freezing up. But I think it's left a permanent um, a permanent bad taste in everybody's mouth. Certainly created moral hazard that Hey, you know, look, it's arguable to say we rescued bear Sterns and the FED backed up JP Morgan's purchase, and that might have given the impetus to Lehman Brothers to say someone will come along and rescue us. Right? Is that fair? Fair

assessment of that? There was a funny cartoon. This was a little maybe not right, but there was a funny cartoon by Barry Blitzer or Berry Blitzer, forget his name is in New York and New Yorker cartoonists who had a picture somewhere in two thousand and eight of Obama dressed as a as a New York City policeman with a hat and a navy navy uniform and walking down the street tooling of a ton with these eyes cast up in heaven as though he couldn't see what was

around him, and meanwhile people behind him or running with bags of money into buildings. And that's sort of typified, typified it for me. But it was certainly an interesting period. I'm Barry Ridults. You're listening to Masters in Business on Bloomberg Radio. My special guest today is a manual German. He spent seventeen years at Goldman Sachs, eventually becoming head

of the renowned quantitative strategies group. He all So is currently uh, the director of financial Engineering at Columbia University. It's a master's program, I believe is that the master's program. It's a sort of travel month eighteen month professional degree. He also is the author of Models Behaving Badly and My Life as a Quant Reflections on Physics and Finance. Let's talk a little bit about quants on Wall Street. You mentioned when you began twenty five years ago or so,

it was a wide open field. There was no playbook. But here it is. We have high frequency trading, we have al go driven strategies, we we have all manners of mathematical um investing, mathematically based investing. Is this the age of the quant? Yeah? I think it is. Actually, I mean when I started out, very few traders were very numerous. They didn't know a lot of math, they hadn't studied. A lot of them came from law backgrounds and stuff like that. And now, in fact, quant was

a derogatory word. I've got a dictionary from the late nineties where somebody it's it's by Mark Chritsman, called a dictionary financial terms, and he says quanton. He says quantity of analysts often used pejoratively and when I wrote that book called My Life as a Quant. What was actually in the back of my head was I took my kids twenty five years ago to see us a Swedish

movie called My Life as a Dog. You see, there was a great movie as for kids and parents to see simultaneously, and I was seeing on My Life as a Dog. That was really the back in the back of my mind. When I came to Goldman, quant was a sort of like being a geek or being a and people laughed at you, although they kind of treated you with respect. I liked it, but it was a kind of mocking kind of respect. So since you brought that up, let me go back to a question I missed.

So you were at Goldman Sachs in the ninety nineties, that had to be one of the questions I was going to ask you, was how were the quants treated? So you you said there was some degree of respect, but was it was there? Were you treated as one of their own? Or were you guys kind of on the outskirts and people, you know, slipped pizza under the door and kind of left you alone. Um, that's funny.

It varied, it varied um for a while for a while I worked for Fisher Black in his group, and then he had everybody sit on the desk and get lunch when the traders got lunch, and you were kind of more equal. But generally I would say, that didn't last long, and people people treated you like like geeks, but like useful but useful people that they spoke to you.

What's the expression useful idiots? Is that how they looked at did was that the action when I got in When I got there in eighty five, which was earlier, people would get in the elevator and sort of make jokes and say, oh, all of you allowed to travel in the same elevator at the same time. Or I would be with I wrote about it in my life as a quant. I would be with some guy and I would I was very excited about being on Wall

Street and it was interesting. I would say something to him about duration or convexity, and he would get embarrassed and shrink away and say, what do you think of the Yankees last night? You know? So, in other words, he didn't know what convects it your duration? Actually now he knew what it was. He just didn't want to be outed in the fation in other words, he was a geek, but he didn't he didn't like me talking

about it in public. But then, but then, even in the early nineties, you know, I remember when not just Quantz, but everybody. If you had a PhD, you didn't put it on your business card, and if you had an email address, you didn't put it on your business card because that was like the brand of Caine. You know, that's amazing. I think that that's early nineties. People didn't want to put email addresses or or or PhD degrees on their business card. Now, of course they'd love to

do it. That's wow. Become things have changed. So you were Goldman when they went public, right, what what was that like from the experience of someone with your background on Wall Street? You know, I think things were steadily improving for quans Alo at the same time Goldman was

getting much more bureaucrats. But by that time, you've been through UM long Term Capital, You've been through d Shore, so LTCM, so all sorts of hedge funds starting to make a lot of money out of supposedly quantitative strategies, and and particularly the whole Internet Internet and not Bubble, but the whole Internet excitement where suddenly um being being technologically competent became a way to make money. And so that even destroying destroying the whole system, like LTC M

gave you, gave you respect. So things were getting better for quants. But by the late nineties, I have to think renaissance technologies had been putting up absurd returns for a long time, right, And if memory serves didn't Jim Simon's return outside investors money sometime around then is maybe a little bit later, but yeah, they only have their own money now, which is which is tells you, hey, these guys are really putting up huge returns without a

whole lot of capacity to do it. In the tens of billions your d shore as well returns outside money. And no, I didn't return outside money, but we're very visible and and at that point don't people look around and say, hey, these guys are minting money. Let's get us some quants, and maybe we need to pay these

people proper respect. Well, they gave them respect, and they paid them kind of decently, but there was still, at least in the area I worked in at that point, there was still a very fine oute gap between being a trader and being a quant Now, I think that's vanished a lot. But then you were a support person, you went a position taker. I'm Barry Ridholts. You're listening to Masters in Business on Bloomberg Radio. My special guest today is a Manual German. He is a renowned quantitative thinker.

UH teachers financial engineering at Columbia. You surround the quant group at Goldman Sachs. I think that's a pretty good CVUH to chat with. Little O me. Let's let's talk a little bit about models and what they can and can't do. Because you've every answer you've given me has has set off in my mind the number of digressions. But I wanted to stick with some of the questions. So you look at models, and you look at what

they can do. What is it that people can do that models can't Well, I think the right way to use models is to sort of quantify your intuition. Like people can have intuition, but it's hard to translate that intuition into a number. And so for example, if somebody said to you, um, what should I pay for an option? Another price of an option? At the money? What should I pay for an out of the money option. You know it should be less, but it's hard to actually

quantify how much less and if you have something. But when you look at black shoals or options pricing, they invent the whole notion of volatility and measuring volatility. And just like you can have intuition about interest rates, a human being can have intuition about will volatility go up, how much will it go up by? How much would go down by? And what the model does is let you take something you can think about in your head, like interest rates, abstractly and converted into a dollar price.

So now let me ask the opposite of that question. Let's let's say you've developed a model and it's working well. At what point should a human intervene and say, hey, this model is no longer producing the output we expect. We need to make modifications. How does one even begin to think about that? Um. When I worked at Golden we kind of try to write to the last year.

The last years I was there. I was in a group called firm wide Risk not in Quantitative Strategies, where we were trying to look at derivatives risk throughout the firm, and we sort of had a recommendation that every time somebody write a model, they'd be forced to specify all the assumptions and conditions they were they were making so

that they could specify when these things wouldn't hold. So, for example, if you build a model to price options on Apple, you're pretending interest rates will be pretty much stable. You don't worry about interest rates. But if you suddenly go to some emerging my icket county or interest rates can rock at somebody should understand that that's not the

right model to use. That's interesting. Um. On a related note, how could you tell the difference between a model working its way through a rough patch and a model that just no longer works. That's a really tough question. That's especially applicable to people who do a statistical arbitrage or people you mentioned like Renaissance or UM. I think that's very difficult. You have to two ways. The first way is statistical. You sort of have to have some idea

of when you're getting results that are statistically unlikely. So if your model is correct, maybe you'll get, you know, some fraction of the time something that's one or two standard deviations away. If that persists, you start to sam in a different regime or the model is broken down. And then the second way, which which I also like, is you really ought to have some model. Isn't just mathematics.

A model has some idea beneath its structural about the way things behave, how people respond, how markets behave, and you ought to be able to make some sort of judgment as to whether the world is still behaving according to the assumptions that you make. So it has to be disprovable if X and y and z happen, therefore the underlying thesis and the model no longer applies. Yes, that's quite that's quite interesting. I like the idea that

that I was going to say this earlier. I sort of think just using mathematics blindly is kind of stupid, And most models get their inspiration out of some economic or or or financial idea first, and the mathematics is just the implementation. Where you get into trouble is when you think that the mathematics is the thing in itself rather than the idea that's behind it. That that goes back to the George Box quote. They're wrong, but some

are useful. Let's so let's talk a little bit about some changes that that quants have forced on on both markets and investing. There was recently a column in The Financial Times that talked about the secret source of hedge funds and how quants are essentially reducing what some people have previously called as alpha and identifying it as a

factor that can be reduced to mathematics. And others have suggested that the quants are part of the reason why hedge fund performance has been so mediocre over the past decade or so. As the quants have risen in prominence and stature and influence, the ability of a person working in a hedge fund to create out performance, to develop alpha is going away because of of what quants are doing. What What are your thoughts on that? I've sort of

got a rush of thoughts coming to my head. Say that because I think all of the things you said are partially true. I think the first one, it's true that the whole hedge fund and st allocation or asset management world has become much more quantitative. When I started out, nobody in those areas knew a lot of math or use a lot of math. Now they all do um.

As a result, I think they're all in competition with each other, And I think if somebody is a good idea some way people move around a lot, and these I've literally seen examples of somebody from one firm going somewhere else bringing an idea there, they use it, they get irritated because somebody else leaves and takes it somewhere else. So I think these models propagate a lot and and become become widely used, and that does cut into the

so called alpha of of everybody. Um. We saw a little bit of that with LTCM when people had left at or the people covering them at different brokerage firms ended up moving around learning a little bit about what they were doing, and so a lot of people were piggybacking those trades as bad as the leverage was. When every desk on the streets imitating it, it's really a

crowded trade. Yes, And now I think people actually have sort of statistical mathematical models that they fit to the whole the whole surface of of stock prices and decide which ones are cheap and which ones are rich. And somebody takes that model somewhere else or or or maybe finds the inspiration for that model in some finance paper that buried somewhere, and people people actually mind finance papers for these sort of anomalies or behavioral anomalies and sought

to implement them everywhere. So I think these methods are finding alpha getting a short run, shorter lifetime. So in other words, it's either arbitraged away or just imitated and and it loses its ability. That that's my impression. Yeah, But at the same time, so there is a lot more of this quantitative stuff. But at the same time, to be a little on the cynical side, all of the hitge funds and asset managers are in a competition for assets under management, and they all like to pretend

that they have more secret source than maybe they actually do. Right, So what does that tell us about the future of of asset management. We've watched lots of money flow to Vanguard, which is primarily a huge indexing shop, but we've also seen a lot of money flow to hedge funds despite a pretty bad run of underperformance. What explains that is it? Is it just marketing wizardry or is there something more

than that? No, I think there are people and hedge funds that really do have um some skill that that that people who are just doing indexing, which doesn't take any skill at all. Really, I think they do have a skill um. I've seen studies that show that, especially in in very liquid complex introments like mortgages UM as opposed to equity long short, which is fairly simple, that hedge funds that tend to do better than average one year, there's some persistence they tend to do better than average

the next year. So it's not just a random district. I don't think it's just random. I think if you have skill, that tends to persist. But on the other hand, the whole world is becoming so swept by people trading mechanically. For example, I think if you're a value investor now, then you kind of buy things when they go down and you think they're cheap. On the other hand, the momentum investors who think momentum is a factor and they sell things that are going down and buy things that

are going up. So I think the large number of people that are now acting in this mechanical way with some model like following momentum tends to mess up the people who are looking for value, and it's much harder for hedge funds to compete in this world. There's a quant name West Gray who writes Alpha Architect, and they have a model that is both momentum and value. It's actually two sleeves, and he said one sleeve is always doing great while the other one is doing terrible, and

vice versa. But over the long haul, both of them actually work out pretty well. That just gives you an idea of the complexity of what we've seen come along. That just there was nothing like that ten or twenty

years ago. You know, you look at the risk parity people that say you should have instead of being sixty equities bonds along the same philosophy you bedroo of having one third of your risk in commodities, one third of your risk and equities and one third of your risk and bonds, which means livering leveraging a bonds a lot because they have such low low volatility, and they also believe that in the long run that will work best. But in fact they've done badly for the last few months.

So well, you know, we saw a lot of money floated risk parity, as as bonds looked like as rates looked like they were bottoming, and as lot of the commodity complex seemed to be topping out. About five years ago. So if you're a third commodities and gold is down thirty percent and oil is cut in half, that's gonna have a pretty big impact on on your returns. Yeah.

I like to be a little philosophical and say that if you take a model too seriously, it's a kind of idolatry in the sense you're assuming that something somebody. You're assuming that you can write down a formula that's gonna mimic the way people behave. But people are too complicated and and if you really believe that you can capture people in a formula equation, you're you're looking for trouble in the long run. I know you have a website,

a Manual Derman dot com. If people want to find more of your your writings, it's my life is a quant and models behaving badly, And you post regular papers at a manual Derman dot com not that often anymore. I do go on Twitter regularly. I'm a fan. Um what is your Twitter handle? ETI manual dermant in one word at a Manual Derman. If you've enjoyed this conversation, be sure and check out our podcast extras, where the tape keeps rolling, and we continue the conversation about physics

and quantitative trading and philosophy. Be sure and check out my daily column on Bloomberg View dot com. Follow me on Twitter at rid Halts. I'm Barry Ridholts. You're listening to Masters in Business on Bloomberg Radio. Welcome back to the podcast. Thank you so much, Professor Derman for doing this. This is really fascinating stuff. And I don't think people get to hear you often enough other than a handful

of Columbia students. Um. And I've been following your career long enough that I really wanted to get you in here and and put you under the microscope. So so let's go over a couple of questions that we missed earlier on And I know I only have you for a finite amount of time, but we're we got plenty of time to go. Um, So first let's go back to this was way ahead of the curve in you wrote an article titled model risk, pointing out the dangers

that inevitably accompany the use of models. How did that play out? I think it played out pretty accurately. Actually, although I was considered I was sort of looking I can't remember too clearly. But I was looking at two kinds of risk. One was implementation risk, where you have the idea right, but you've got all sorts of computer problems or efficiency problems. Then I was looking at what

happens when you actually have the idea wrong. And yeah, there was the first paper I think ever written on model risk, and I think it played out kind of accurately. That's what sort of what sort of happened. So so the next question, let's see if I can fix this little thing here that is there you go. So the crisis in two thousand seven two thou eight, how much of that was a function of UM models not working well?

You know, as part of it definitely was, But I don't think it was the fundamental cause there are a lot of things happening. My sort of slightly biased view is that UM really interest rates were very low, and everybody was trying to stretch for yield and do anything that would get more yield. Absolutely, and that's partly the

federal reserves fault, but whatever. And I think models played a secondary role in that they were used to construct, perhaps somewhat deceptively, through the rating agencies instruments that that purported to give you a high yield with a low risk, and some models were, um, we're a tool in trying to cater to high risk, but I don't cater to to disaster. But I don't think they were the fundamental cause.

It's certainly when you look at the rating agencies. I'll never forget being in a conference room when a bunch of salespeople came in and we're pitching us this new fangled subprime product, and the phrase that resonated me with me was this is just as safe as U S treasuries, but it pays two fifty basis points more. And to me it was, well, which is it? It can't pay that much more? If it pays a little more, and get arbitraged away, but how could it pay almost three more?

That either you guys are gonna win the Nobel Prize or you're gonna be wearing aren't jumpsuits picking up trash on the side of the road. It turned out neither were true. They most of the people who participate in that, well, they just moved on to the next thing and there was no subsequent accountability. But the rating agencies and the idea that hey, we've created taken draws and turned it

into gold. People wildly believed that that was possible, wasn't it. Yeah, you know, I always like to make the analogy that finances a lot like nutrition, in that people take a

small amount of information and extrapolated like crazy. You know, so they will tell you women should or shouldn't get estrogenally, you should or shouldn't eat eggs, and then they changed in mind dramatically a few months a few years later, and it's hard to tell who's a crank and who's correct, because, um, basically they're all aimed at I don't know, they're all aimed at marketing. In some sense, I'm fond of saying predictions and forecasts and marketing because that's all they are.

But it's amazing how common sense, and granted this is all after the fact, but a lot of common sense could have saved people a lot of money if wait, how could it be as safest treasuring yet pay so much more? But nobody really stopped to ask those questions. Then it it's amazing that nineties paper is really a decade ahead of its time and it turned out, um really to be significant, which leads to my next question. So models behaving badly. How did that impact capitalism and

the Great Financial Crisis? Yeah, I mentioned this earlier, but I've found the whole thing really disappointing. I'm sort of disillusioned in that. That's what I said earlier. Some I expected that. Um, I sort of like capitalism, but as I said before, I think if you to benefit from from taking risk, you've got to suffer the consequences. And that really hasn't happened. And I'm with you on that also. It's well from my perspective, it's like everyone's a capital nation.

Everybody's a capitalist until you know they're in trouble, and then suddenly they they become temporary socialists until they get there, right. I don't know, it was it was astonishing to see how quickly people flipped from that. Um, let me go through some more of my questions. We went over, Oh, I have something that I've been dying to ask you. I love this quote of yours. The efficient market model a model and an analogy, but not a valid theory.

How is that not a valid theory. Let's hold aside whether or not the efficient market hypothesis is accurate, but why isn't it a theory, it's not a theory because it doesn't describe the way things really behave. If I can give you an example of I had a sort of anecdot to tell you what I meant by theory in a corny sort of way. When my son was very smaller, used to put him my knee and play with him and bounce him up and down. And my

sister used to do it to me. Say half a pound of tupanny rice, half a pound of treacle, mix them up and make them last. Pop goes the weasel. It's an old English thing. And then you drop your knees and the kid drops down to the floor and he chortles with glee. And then he said again, and I did it again, and then he said again, and I did again. When I did it the fifteenth time, when I dropped him down, he said to me, why it's not funny anymore. And so there's no explanation of

why it's not funny anymore. The fact is, if you tell the drug fifteen times, it's not funny anymore. And that's both the fact and the theory. It's true, and there's no explanation for it. That's just so I was trying to say earlier about GOODA. When you describe something that's true, you can't say why is it true? You just say it's true, and the words are the theory and the fact is that that's the way it behaves. The efficient market model is not like that, Um, markets

are inefficient. Um. It's a model. It's not a description of the way the world actually is. The world could

be that way, but it isn't. So so I'm fond of saying that markets are kind of sort of eventually somewhat efficient, yes, in the longer, in the long run, more or less, I liked I used to like to say that the efficient market hypothesis and model was a very clever jiu jitsu trick by economists who couldn't predict what was going to happen, and instead of giving up society to make that a principle, so turning sort of weakness into strength. That's that's interesting, and and it is true.

Everybody who believes in the efficient market hypothesis also believe that in the random walk, we don't know what's going to happen. So therefore by low cost indexes and rebalance on a regular basis, And that's good. As long as not everybody does that, you still need somebody out there looking for value otherwise. Otherwise, Yeah, as much as money has been flowing to places like Black Rock, I Shares and and Guard, it's still a tiny percentage of the

overall investment. What is the three trillion dollars in indicries now three trillion dollars in ETFs area that today not a whole lot of money relative to what's the total investable universe sixty eight or seventy five trillion dollars worldwide? Yeah, I think so, yeah, that maybe trillion. I think the US equity markets are are are about that, the bond markets are more, and then you have the rest of

the world. At one point in time, US was almost half of the investible assets and in the globe that has since since changed. So so I think a lot of models work well. Related to what you were saying, they work well as long as you're just a rupple on the sea. But when you become the whole, see, then the model right, it's it's you. You no longer trading in the market, you are the market, and that's a very different um. I love this line. Explain for

us what is the unbearable futility of modeling. Oh is that a line of mine? Yes? Okay, Um, well, I'm not sure where that comes from, but I believe it's a chapter or sub chapter heading in towards the ending. Um. I'm not sure what I meant by that, to be honest, I think I probably just meant what I said before that in the end, you will always be wrong, and you will you will never you will never be you know,

if if you it's what you were saying earlier. If you find some law of nature, it kind of holds forever, and if you find a model, it's inevitably going to going to require revamping, changing alteration as people start to change your behavior. Do you miss physics at all, given that the certitude that you get in physics versus always dealing with uncertainty about what's happening and about how long a model is going to have a Literally, No, I don't.

I'm very glad I got a background in physics, but I'm a most at this when glad I got out of it. It's like a rough life being in physics because there's so many incredibly smart people that you actually run into people and you say to yourself. I can, I can sort of understand what they're doing, but I could never have done that myself. So it's actually a

bit of a relish. So you come to finance, where essentially it's easy to be the smart I'm not, I'm reasonably smart, but but finance also Actually, physics and finance theres lots of room for taste, I think, and where people go wrong is when they don't have taste and they start slavishly sort of following the mathematics too much. Physics is really driven by ideas and then mathematics to implement them, and I think it should be that way

in finance too. I'm gonna push back at you know a little bit, because I think you were a grad student and you wrote a paper on it wasn't Higgs, Boston, but maybe it was one of the Higgs particles that became widely um, you know, widely regarded, And to do that in physics as a grad student is no easy thing. No. Yeah, I wrote a thesis on writing tests for how to find a w boson that was cause don't ask me where I pull that from than Wikipedia, but that's true.

And then they eventually discovered that although I was. Yeah, now I was. I was a reasonably good physicist. But nevertheless, and maybe in finance toopid. Let's say, in physics you run into people I heard Fineman talk or people like that, or Richard Fineman over in California was at cal Tech. Yeah, and so did that Was that intimidating or impressive? It was kind of intimidating or even I sort of wrote in My Life as a Quantuent, my memoir, I wrote,

sort of, you see your ambitions slowly getting degraded. When you're like eighteen, you want to be like Einstein, and when you're like twenty five, you want to be like the professor that you know in your department. And then when you're like thirty two, you say, gee, the guy at the desk next to me is getting more invitations to give seminars than me. And then you suddenly think, well, look where I've got to. So it's that competitive, and

it's that it is competitive. I got a job as a particle physicist at Boulder in nine was my last job in physics, and they were like a hundred and forty people applying for one job. It was really rough. I mean it was after the Vietnam War, there was no money for research anymore. All the physics jobs that filled up when there was still a lot of money before the head Field govern Amendment which stopped the money

going to research. And I was maybe number four on the list or something in the first three either had had dual career problems or got better jobs and I got the job. But but um, yeah, it was kind of it was kind of discouraging. So what about today for financial engineers? You mentioned there's lots of quants around. Are there are there still opportunities for people who want to go into this field and and apply mathematics to finance. Yeah, I think there are. There's a much bigger market for

risk management now. Some of it is PR. People just want to say they have risk management, but sometimes they have real risk management. And ye know what I liked about if I talk pass what I liked about getting to Goldman as opposed to in physics. I like physics, but in physics someone had the feeling like you had to be really superb or otherwise you were wasting your

life because you spent all your time. I was doing theoretical physics singing in an office and banging your head against the wall trying to solve something difficult, and you could spend half your time sort of depressed. It was nice about being a Goldman. What I actually liked about finance was it was a more it was a more multi sided world where you spend part of your time doing theory, but part of your time writing programs, and part of your time interacting with clients and with traders,

and so there were many more sources of satisfaction. You had long term projects and short term projects, and I found that very satisfying. I got quite excited when I went to it goes. So, after almost twenty years on Wall Street, you transition to being a professor. How do you How do you like teaching? How do you like working with with young people? I like it, um I would say it's not as exciting as as being on

Wall Street. People in universities are sort of siloed, and that everybody is there because they want to get on with their research and have students, and so I would say in a sense, being at Goldman was more collegial in being in a university for me, despite the university being a college, because actually everybody wants to sort of I'm exaggerating a little bit, but leave me alone. I have work to do. Was it, Goldmen? You could go to somebody and say, I'm thinking about this problem. Can

you help me? And because you're all paid to pull in the same direction, they sort of helped you more willingly. I would say, so a little more of a team effort on Wall Street than in a university where everybody has a list of things to do and students to deal with and mid terms and papers exactly and their tenures. So go away, I'm busy right that that that's fascinating. Um, how has quantitative investing evolved since you began in the

industry all those years ago? Um? Well, first of all, everybody on trading disks is, as I said, simple times before, much more numerous. Now a lot of them can write their own models. A lot of them studied math, especially in derivatives. You get a lot of French or or even American students who have all gotten advant degrees or PhDs in finance, So that's become much more complicated. The second thing that's changed a lot is is electronic price

feeds and electronic settlements. So suddenly computers have become much more important. Jeff Gunlock tells the story about his one of his first jobs on a bond desk. He shows up with a math background and is able to do some really basic calculations in his head, and everybody there thinks he's a wizard, because they're not math guys yet they're on a bond desk. Thirty years ago. Yeah, I started out on a bond desk, and actually along the lines of what you're saying, I worked on this model

which you mentioned, the Black Derman toy model. But I think one of the things that almost as much impact was that I could program well in a in a in a world where people couldn't, and I built a user interface that let the traders enter trade, save it, think about it, come back the next day, and talk the salespeople talk to a client again. And user interface makes a lot of difference. So in those days, if you could act really in those days, you couldn't get

something to do it for you. If you could do it yourself, you could be that much more effective if you knew the theory and the programming. That's a that's quite interesting. So that was leads to what was the first model you built at Goldman it was the some dormantory model for interest rates. It was a model for options on treasury bonds, options on interest rates. Goldman was doing a lot of business. People were beginning to extend black shoals, which is for options on stock, options on bonds.

And it's actually quite a complicated problem to do consistently. And I can imagine, yeah, because bonds bonds are all you can pretend apple and Apple and Walmart have nothing to do with each other and write an option model for each. But you can't pretend to five year bond and a three year bond have nothing to do with each other because the five year bond will become a three year bond. Yeah, And so you have to really

many more constraints on building the model. So what is it that people misunderstand about quants and model in finance? The lay person has an idea year about a person sitting in front of a computer, But what do the average what does the average person not know about what's happening behind the scenes that is telling us to as to just the perspective of plants in finance. You know, I think people who academics or other people haven't worked

with trading desks. You imagine that you're making predictions all the time, and I think the truth is, most of the time, certainly derivatives, you're not making predictions about the future. You're trying to figure out in the present what's costs too much and what what's too rich and what's too dear.

And your models are much more good to saying this is more expensive than it should be, and this is richer than it should be, And then the prediction is, Okay, the rich things will become cheaper and the cheap things will become richer. But most of your time is spent trying to at least most of my time, we've spent trying to figure out how to tell the price of

something liquid from a lot of liquid things. So, for example, if you want to buy an option, how you figure out the option price from the stock on the bond price, which are both liquid and so the example I like to give in a way it's also a metaphor, is if a lot of the problems you faced with is not what will happen to the price of apples in the future, if you're dealing with with with fruit, but what should a pay for fruit salad? Given the price of apples, pears and peaches, and and so you say,

what's the cost of canning? What's the cost of buying the apples, pears and peaches, unless should be the price of fruit salad. And then sometimes you do the reverse, you say, I know the price of fruit salad, I know the price of apples and oranges, so what's the right price for pears. But it's always trying to figure out in the present how to get the liquid thing, given the price of the liquid things, how to get the value of the liquid things. Am I making sense? Yeah? No,

that's that's quite fascinating. You're absolutely less prediction than trying to figure out what the current price of something should be. And very often there's incomplete information. If if something is liquid and trading, well, when then we know what the price is. But if something is illiquid doesn't trade a lot, you really don't have a market based price, so you have to come up with a different way to figure out what you should or shouldn't pay for this. Is

that a fair yes? And the prediction comes less in predicting what will happen, then in saying this is cheap. And eventually I think my model says this is cheap, and so eventually it will come to what I think is fair of value. Everything comes back down to reversion to the means that's fair, I think. Um, So what other quants do you admire? What? What quants have moved the industry forward? I think Fisher Black most of all.

I wrote a lot about him in the book that I wrote in my memoir My Life is a quant because I think he was kind of an exceptional guy, both from a character difficult guy, but exceptional from a character point of view, and that he liked to tackle everything ab initio. You know, he would from scratch, from right from start, right from the start, somebody who was totally unknown could come to him and send him an email about something, and he would think about it, you know,

without any prejudice. And he thought about everything in his own way. So I kind of admire him. Um. I kind of admired Steve Ross and Mark Rubinstein. They were more PhD academics, but for for their setting out the whole basis of them of option pricing, which I spent most of my life on. I kind of admired Paul Wilmot, who I once wrote a paper with. We actually wrote based on the based on the on the financial crisis, we wrote a financial model as manifesto. It was a

bit of a joke, I recall. I recall that now for people who may not know. Wilmot puts out a Quantitative quarterly Quantitative magazine magazine on really developments Paul Wilmot, right, developments in in the world of quantitative trading and analysis, and it's really high level stuff when when you sift through it um as I on occasion have done, most of it's going to be over the head of the

average trader, the average investor, the average person. But within the industry, I have to think that that's become practically the go to bible. Is that. Am I overstating it? Maybe a little bit, but that's one of them. But I sort of admiring because he's written some I mean for that too, but he's written some good textbooks, and I like to think, like me, he tries to make

things simpler rather than complexify them. There's been a tendency for finance academics to do everything in a very formal, axiomatic way as of the teaching math, and Paul and myself both like to use the least amount of math possible that that's quite interesting. So let's keep plowing through some of some of these questions here. Um, we talked

about the ft, we talked about alpha. What's been the impact of of high frequency trading and al go driven trading strategies on the sort of work that that you do? Very broad if if I talk from a sociological point of view, ill see from the students at Columbia until a few years ago, and they all wanted to take derivatives, and the last year or two everybody's being tempted probably about the job market and by excitement to work on high frequency trading, algorithmic trading. And now for the last

year or two machine learning. You know, you're programming machines to to to just grow through all the data that's out there and figure out their own rules. Yes, or even even things like can you scour the internet to find out whether there were a lot of cars in Walmart parking lots the last year, you know, to try to get them get source advice? There? There have been people doing that with Twitter. Can we identify sentiment shifts

on Twitter and then generate a tradeable algorithm from that? Yeah, I've noticed that, So a lot of that, A lot of that stuff, some sentiment based, some really objective, like can you figure out in some way how much sales are happening in various places from data you can collect on the internet. Um, what was your question again, us,

I have no idea, so I have no recollection. Um, what's the pact of high frequency trading on model development and and model construction and then actually having these models execute in the market if there are theoretically predatory algorithms looking for whatever it is they're trying to do. Yeah, I think if I get a little bit general that there've been sort of two classes of models that people

use in finance. One or structural models, where like for derivatives, where you say an option is really a hybrid of a stock in a bond, and I've got to figure out exactly how it works, And that's what black shouls does. So it's really saying like an option is like a molecule made out of atoms and there's a structure there.

And those were always what quantum mostly did. But what's happened, as you point out, in the last few years is econometrical statistical models have become much more the rage and students are gaining that direction where you just say, cannot find a regression between um, you know, various factors that that seem to predict the market. I don't care. I care a little bit why, but I care less. Why

then finding the actual pattern? And that's become so statistics an econometrics have become much more fashionable as a result of high frequency trading and algorithmic trading. That's interesting why econometrics? How how did that work its way into the sort of modeling. I'm looking for relations between time series, between FED behavior, between interest rates, between UM the behavior effectors and and particular stocks. So let's let's caring about why

it's happening, the detecting a pattern. So I see a lot of these sort of correlations, and they always make me and I see people writing about them, and I read articles and I see charts. But the back of my head, I'm always saying, well, yes, but is one thing causing another? Is this a temporary you know? Sometimes

they're in sync, and so I can't tell. I can't count how many people have been insisting, well, look the FED cut rates and here's what the market did, and now we have quantitative easing, and here's what the market did. And as soon as this unwinds, the opposite is going to happen. And they run in parallel for long periods of time and then suddenly they just go their own way. How much of a risk is that on the econometric side, that we're gonna be fooled by randomness. We're gonna see

a correlation and it works until it stops working. I agree with you totally. I'm I mean, I understand why people do this stuff, but I'm a bit of a skeptic about it myself. I think, UM, just what you said, there's a standard phrase which you are sort of paralleling, saying correlation is not causation, and I think that's true. But but people are so keen to make money for legitimate reasons that that's the easiest thing to do, and

sometimes it works. All right, I have to ask you before I get to some of my favorite UM questions. I have to ask you about Spinosa's theory of emotions, which you mentioned in UM models behavior behaving badly, moral concepts of good and evil, virtue, and perspective have a basis in human psychology, which naturally leads to the question, what does this have to do with trading models. Well,

Spinoza was like three years ahead of his time. I tried to read The Ethics once when I was writing my book because I was trying to find an example of something that I thought was the theory rather than a model. And what Spinoza did in the Ethics, so that it was only published posthumously, was trying to figure out actually very a lot like behavioral economists, but three years earlier, trying to figure out what drives people to

behave the way they do. And his argument was that, um, if you can understand the passions of the emotions, as he calls them, then he'll be able to understand why people are unhappy and then be able to figure out how they should live their lives. And so what he starts out doing is analyzing all the emotions or as he called with the passions that they are. And he calls him passions because they affect you as a passive

person rather than an active person. They sweep over you rather than you deciding you would you would want to want to behave that way. And it's actually so it's very interesting and be it's really closely related to derivatives. It's sort of astonishing because what he does is he says, at the bottom of the chain, there are only three things, which is pleasure, pain, and desire. And everybody knows what pleasure, pain and desire, probably actually defines it in some philosophical way.

And then he defines every other emotion, and there are hundreds of them verbally in terms of how they reduced to pleasure, pain, and desire. So, for example, um, love is expectation of pleasure from some other person, and pain is hatred is the opposite expectation of pain. That's a little bit corny. But then for example, he says, um um, so envy, Yeah, envy is the one I wanted. I couldn't think, so he says, envy, Um, I have to

try to remember the pain of absence of pleasure. Yeah, And and envy is um wow, it's suddenly slipping me. I remember cruelty better. Cruelty. Cruelty is what you call the desire of somebody else to inflict pain on someone that you love. So cruelty eventually goes down to pleasure, pain, and desire. And I like to say it's like a it's um it's like a convertible bond that has both equity exposure, credit exposure, and fixed income exposure. It goes

down to all three of the derivatives. He's really got to actually do a chart, which is on a giant then diagram pleasure, pain and eventually goes down through some chain to these three things at the bottom. I wonder if there's a graphic of that somewhere. I've got a

graphic of it. Yeah, it's on my website, and I was only going to make a post out of it because it's really in fact, I once I once submitted that somebody, somebody at the Serpentine Gallery in London had a map competition that somebody connected me to and I submitted that as a map, and it's in a book somewhere maps of all kinds of things. That's fascinating. Um, So now let's move over to some of my favorite questions. These are the standard questions we ask everybody. So we

went over how you how you got into the finance. Well, actually we really didn't get that question. So you started as a physicist, how do you go from bould of Colorado studying particle physics? Too? I'm gonna do quantitative work on Wall Street. How did that transition actually? You come kind of um, not planned. I I got a PhD in nineteen seventy three from Colombia. And then, as I said, jobs were hard to find, and I had a post stock at University of Pennsylvania, and then I had a

job at Oxford in England. And then I had a job at Rockefeller University of New York. And my wife was in biology, and we were moving all over the world out of sync with each other actually, and then when we were in England together we had a kid, and and then we both had jobs in New York. And then it was very hard to get permanent jobs. I got a job in Boulder. She couldn't get a job there. I lived there for a while, um, and eventually I sort of threw in the towel. It was

getting too complicated. And then this is nineteen eighty where people went then was there was the oil crisis and telecom was building up, and you have the people who were physicists either went to work for Exxon or Mobile, or they went to Bell Labs. And I took a job at Bell Labs later became loosen. You were a

good couple of years. I was there for five years and and so then I stopped doing physics and I worked in a business analysis center that sort of was applying physics techniques or mostly programming to A T and T business problems. That's funny because when I saw your background and you were at at Bell Labs at Loosen, I assumed you were doing some fine physics work as an telecom I never for a second said, oh, well, he must be working in the in the finances there were.

I was a sort of retread when I went there, and I'm aladd of that lead to Goldman Sachs. Well, that was when I went there and I started working on actually much more computer program I learned to be a good program at the time, and then we were tackling sort of problems that A T and T had, where you could use sort of computer modeling and financial modeling. UM. I didn't like it very much. I learned a lot of useful stuff, but I always really hanchred to go

back to a more academic job. But that was two difficult and then all the head and just started knocking on the doors of people because interest rates were high, and Solomon and Goldman in these places were hiring people. And it took me a few years to adjust the idea, but I decided I would I would take a leap out of out of the whole sphere and into the business world. It was sort of a shock for me. I never expected to ever do that, and it worked

out pretty well. Yeah, I got very excited, as I say, when I went to Golden and eighty five. It was like a shot in the arm for me. And so and you were there through a brief period. I was at Solomon for one year a little bit more, and then in mortgages, and then I went back to Goldman. I started in fixing income. I went to Solomon mortgages, and then I came back and I was in equity to Routers for like ten years, which was really my favorite.

And it sounds like you thrived and did really well there. So let's talk about mentors. Who are some of your early mentors? Oh, in physics are in well both? Yeah, in physics, there were a couple of professors in South Africa that I that I worked for. There was a

guy called Professor Whiteman who sort of tutored me. It's long dead, probably, um And how about in in the finance and finance I would say Fisher black the most in that I got there and there was still not a lot of concert Goldman and and I got involved immediately in working for the bond options trading desk, and they connected me with Fisher because he was the expert and m he really had a very big impact on me. So I would say I don't think he I don't

think he set out to mentor me. He was kind of a bit of a cold fish in an ice way. But I really learned a lot a lot from him, both about perseverance and about not taking models too seriously. Quite quite interesting. Um, what investors influenced the way you think about modeling or investment? Um, that's a good question. I to be honest, I don't invest that much. I'm a I'm a E T F mutual fund guy. In

the old days E d F Now UM. For a long time when I worked at Goldman and I recently worked at a fund of funds, I wasn't allowed to buy individual stocks. Um. So so I'm so you're an index sir. I'm an index a pretty much. And by the way, that is not uncommon amongst academics who say I don't have the interest, the time, the effort. I don't want to babysit a bunch of stocks on the possibility about performing. Let me just go. I'm kind of I'm kind of like that. I like it intellectually, but

I don't and I like following the markets. But um, I have at times board options years ago when I was allowed to, but I don't really do that anymore. Uh, let's talk about books. Always like Peter Lynch, interesting guy, right, fascinating and I was fascinated. But yeah, a quick digression. I always thought Peter Lynch and the idea that when you're out looking at things on your own and you discover stuff. I don't know if that still exists anymore. Yeah, I kind of like that a few times. I used

to do that. I remember noticing with my kids years ago that all of the all of all of my kids friends mothers were wearing rebox shoes before I've ever heard of Reebok when they were doing aerobics, and they were all drinking clearly Canadian at some point. He's all like twenty five years ago. And I remember going into Lulu Lemon a few years ago and being amazed by the good running gear they had and seeing people drink them drink what do you call it? Curry Green mountains.

So I still like the idea that you that you spot something that you like, or I was like that about Apple actually, where you just love the product and you say I'm going to go with it without reading

about EBIT, orbit, d A or anything like that. The question is given, how sophisticated technology and biotech and and all sorts of things that require an expertise everything you've described as either a consumer product in fact they're so the does the Peter Lynch approach is it's still valid given that so much on the market is not related

to retail or consumer spending. It's like, stop and thinking about you're not doing biotech, you're not doing pharma, you're not doing almost all these different software, hardware, networking technology companies. The I wonder if Peter Lynch is of an era, and maybe that era no longer exists. I'm inclined to

agree with you. I have friends who went into money management after they left Golden into their own money management firms, and occasionally one of them says to me, well, this has been a really hard time to be a money manager for the last fifteen years, and then I think, well, kind of, what are you getting paid for? Right? But how do you It's hard to do better than other people, and you know, he sort of imagines maybe that the world should keep prices should keep going up, and he

should get paid. Well, that's that's you know, if you're just buying a trend, you might as well just do the t f my Peter Lynch experiences. I moved out of the city to the suburbs. It's like fifteen years ago, maybe even a little. It was before nine eleven, so it was just before two thousand and one, and I discovered this new company that nobody had ever heard of called home Depot, and I'm like, wow, this place is amazing. They have everything. A yeah, we're fixing up the house.

Is the first thing I do is like I have to buy some of this company. And I punched it up in a on the on the Bloomberg and it's just done nothing but go up for like fifteen years. I'm like the last person on the planet who discovered home Depot. So that was my like, oh, I guess I'm a little late to the Peter Lynch party with with Home with Home Depot. I know you like books, and I know you like philosophers, so I have to ask, what are some of your favorite books? Okay, fiction, nonfiction,

finance related, non it doesn't matter. Fiction, I like sort of Um, I like good romantic books. So I like Anna Karenina and I like Madame Bovary. Those are two of my favorite and low leader I have to say, so that's similar theme people people obsessively, obsessively in love. It's always a good story. Um. Nonfiction, Yeah, I like history, although I kind of like I have a hard time suddenly thinking of things, but I sort of like good I read a lot of modern I read mostly a

lot of modern fiction. But um, but nonfiction I like. I like some philosophy. I like Schopenhauer. I like him. I like Chopin out the most because he's kind of cynical in a very real several times. So what what's your favorite work of Chopin. There's a collection that I have called him um Essays and Aphorisms. It's a thin Penguin book with a lot of essays about everything from getting old to wisdom to how you should not read

until you've until you've exhausted thinking. And I'm very good and very very beautifully written, a little bit like Freud, like you could get a Nobel Prize just for just for his writing style. Really that there's a thin penguin. I've got it for years. But it's called essays and effort and really funny. Oh really yeah, I mean in a in a in a in a slightly bitter sort of way, but very sharp. So you strike me as someone who would read Fineman, who is brilliant and a

serbic and funny all at the same time. Yes, I I studied the Fineman lectures and I actually actually shook his hand once in a men's room at a conference in not shook his hand. I spoke to him in in nineteen. But he was a phenomenal guy. I heard him speak several times. The essays is supposed to be the lectures are supposed to be phenomenal. I got a birthday gift of the read books. No, I got someone gave. Actually it was a gift to somebody else that they

re gifted to me. It was all the c D ease of the and and they were all none of them worked. It was I don't know what the heck to do. I have like twelve CDs. They're essentially paperweights, but it's like the whole box set and I don't know what was DVD or it was because it was done in the sixties and it's um. I'm sure I could. I sure I could pick up another set somewhere, but it's sitting somewhere in my basement in a box. The Fineman Lectures all cd s, they're all completely but don't

read much physics anymore. I have them. I like to keep up in a popular way, but I don't. I don't read anything. It's a handful of of astrophysics blogs. I still track. Phil Plate is a guy whose Twitter handle is bad Astronomer, and he writes some really interesting stuff. He can bring, um, some really complex things to a to an understandable At this point, I'm a lay person, not a you know, not able to keep up with any of the high mass. But the ongoing advancements in

physics are just phenomenal, most recently the gravitational waves. And it's just of all the sciences that are eventually going to crack the secrets to the universe, it looks like physics is way out ahead of everybody. Yeah, it's astonishing if you if you sat out in physics, you kind of get spoiled for everything else. That's that's a good way to and it's it's nice to sit back and say, well,

I'm glad I don't have double labs anymore. But it's interesting to watch this and and it almost seems that the pace of new knowledge is accelerating. Look, we just landed on that comet last year. These things were hard to even imagine outside of science fiction a decade ago. And it's just it's really to me that stuff is is utterly fascinating. Yeah, I almost have a slightly religious

feeling about it. I'm not religious, but you think about this gravitational way thing, and you say, okay, it took a hundred years for a thousand people to verify the prediction of one guy years ago, and the fact that somebody could figure out the way the universe works just by pure thought. It's sort of an intuition which he then elaborates into a model. It's just sort of yeah, it's enough to make your believerable. There's there's a fascinating book.

As long as we're talking about this, So um, there's a and now I'm drawing a blank on the name. There's a very famous physicist who asked about why we've never come across life elsewhere where? Is a firm's paradox. Where is everybody? So if if you have you have two billion stars in in a galaxy, there are billions and billions of galaxies, how is it possible that we've

never come across any other intelligent life anywhere else? And some interesting biologists and physicists put together what they call the rare Earth thesis, which is essentially, life is common on the planet butte in the universe, but intelligent life is relatively rare because the universe turns out to be a very hostile place. So so that gravitational wave that

hit us was relatively modest. But there are there are magnetars, and there are all sorts of pulsars, and if you're even like a few hundred light years away, the gamma waves and the radiations that wash over essentially sterilize the planet of all life. It eventually comes back, but intelligent life has needs a lot of stability for a long time. I find that sort of stuff endlessly fascinated to and the parallels to finance are very much there if you

sit back and think about it a little bit. That's really I think I've seen a vision of this that's really interesting, and I think I've only read about descriptively, but where they say you might get intelligent life, but soon or later they destroy themselves and they unable to communicate with the rest of the universe by reverting to

any primitive state that either either universe. So first of all, you have to be in the exact right sweet spot distance from from the Sun. You have to hope a meteor doesn't come along or any of the early Solar

System formations that hits you. There's another thesis that says, so the Earth has this giant, oversized moon relative to all the other planets, and but for that, you may not have had tides, which really lead to accelerating a lot of of just taking protein strings and leading to minerals and and taking tides all the waing inland and then having the tides leave. You get that with an oversized moon without that, So you know, once, once you study physics, you can never really let it go. It's

always there. I find that stuff I don't know how we had this with this digression from other than Schopenhauer's essays and aphorisms, I guess and and finement stuff, which is uh, which is fascinating. So so since you joined the industry, boy, that was like a I don't know where that digression came from, but it's in the back of my head. And it's uh, I never heard that

about the moon, the moon making making them cheer. Well, it's not so much that it made it possible, but you end up with these really large tidal Now keep in mind the moon is slowly moving away from the Earth um and and a few millennia ago, a few billion years ago, it was much closer, which, by the way, raises a whole another question. How did this moon end up around this planet? Was it captured? Was there were there two moons um one of which got absorbed into

the other? I mean, there's all sorts of thesis is as to how you end up with a really big moon relative to a mid sized planet. When we look at Jupiter of saner and they have what is it a hundred dozens and dozens of moons, all of which yeah, so it's it's I'll dig up the name of that in a while ago. I think it's called Rare Earth.

But if you like the occasional physics nonfiction, you might find this um and it is a little uh, not religious or spiritual, but anything that changes your perspective of our place in the universe is really kind of fascinating and spiritual and curious. Although there are people who insist firm's right and will eventually find people, but so far. And then there's another thesis that says, why are you

looking for them? If they can communicate with you, they could come here and basically, oh, a nice planet to Uh. There's a third one that says, don't let anybody see you, because people in the Amazon or to the bush exactly that we we are, the American Indians, and anybody who's coming superior technology is superior here ability to do whatever they want. And let's hope that doesn't happen anytime soon. Um So, but we digress. So so let's go back

to uh, quantitative finance. So since you started in that industry, what do you think are the biggest changes that have affected finance? I think electronic trading and electronic markets and the effect that said filtering down on everything so kind of computers in a sense no more open outcry um, you know, everything matched by computer, and that certainly affected the careers of people and and um did that did that enable a lot of what we see on the

quantitative side to progress. Without that, you kind of at an impasse, aren't you. Yeah, they kind of go in lock step. You know, you can now get good price data, good volatility that I guess the bond market isn't totally isn't really electronic yet, but hitting in that direction. And

currencies are still over the counter. But I think that's the biggest trend that people can now trade by algorithms and trade by computer, and um, those skills are more and more more and more more and more in demand. So when you when you look at models, so you could create models for equities and derivatives, and you can create models for fixed income. Can you can we not create models for currencies because of the way they're traded.

You can still make models, but they're kind of harder to implement because you I don't know how it is now a little out of date, but you had to call somebody and you can't really do statistical arbitrage easily where you have an algorithm that just sends out orders and buys when it needs to in cells when it needs to, because you still have to call somebody on the telephone. Had to do that in equities to sort of you know, twenty five years ago, whereas now now

nobody makes schools. I wonder why currencies aren't as automated or as electronically driven as as some of these other asset classes. Yeah, because actually they should be in the sense that there you know, it's hard to do with bonds because bunds are very idiosyncratic and there aren't one stock millions of different bonds and yeah, but currencies are

and I think it will go that way. I think partly because in a cynical sort of way, there's a lot of money to be made by I think part of the people who trade currencies, my guests, would be that they're reluctant to go electronic because there's a lot of them. You look at what Chase charging you three for for every time you use your credit card in a foreign country. There's very big, very big margins over there. So it hasn't gone electronic because there's a big incentive.

I think that's incentive to not to Did you see somebody got into trouble some bank a few a few months ago for giving giving clients UM the worst price, the worst currency price of the day, consistently consistently giving them the low if they were if they were selling in the hive, they were buying. That's amazing. Why why are we not surprised by that? UM? So we've we've talked about what's changed in the past. What are the upcoming shifts that you see, UM that's going to impact

quants or impact the concept of quantitative UH modeling. UM. Try to think a little bit about this as you're talking. Just the extensions of what's happening. I think the vanishing of small investors, which has been happening and maybe not so much in Japan, but it's certainly happened here. Nothing is now starting to happen in Japan. What about China, which I was going to say, China seems to be all mom and pop investors. When does that become I

think they're more mature and institutional. I think that's that's doomed in the long run. I mean, I think mom and poppy investors are doomed in the long run, and in China to China is still manipulating their markets a lot, as far as I can tell, um, So how does that play out? I suppose, I suppose badly. At some point Chinese markets will collapse and I'm worse than what

we've just four correction we've seen, I would guess. So I'm not really good at predicting the future about the stuff, but I think as China is going to have to democratize them now you see a lot of people are trying to get money out of the country. They're gonna have to put in capital controls. Um. It'll be interesting to see what happens. Certainly, in the program I teach, it's sort of astonishing of the of the students are

basically from mainland China. Really that's a wild number, and that's true in most of the sciences in this country. Are they going back home or is this a way to get out of the country? A mix, a mix, but a lot of them. You know, it's an expensive program. People pay you know, I don't know, sixty or eighty

thousand dollars a year to study plus living costs. Um. Chinese Chinese have money, they get they can get money out of the country until now to pay for tuition or is this just a way to know this is for tuition? And and so yeah, they're they're, they're millions of It's good. The millions of Chinese students in this country. Some stay, some go back. But really the graduate schools in the sciences and certainly in the finance engineering programs, but I think in physics general will run on all

run on on foreign students in China. Why is that? That's fascinating. It's a good And when I worked at Goldman I ran this con group. You know, the majority of foreign, not necessarily Chinese, but the majority of I don't know, Rember. What I see in a lot of math is so so it's it's Chinese, it's Korean, it's Indian. Used to be Jews sort of fifty years ago. And so what is it Each subsequent generation of immigrants takes the hardest working area until the next. General, How does

that work? Yeah, I think I think that's what kind of happens. They immigrants, um immigrants come in and and do the stuff that's that's hard. Not so much hard in a difficult sense, but hard in that very concrete that you know, what successes and you know what, you know what what being good at it means. And then their kids want to be businessmen. They don't want to

work that hard. So then that opens up the flow as first who was the Jews, and then it's Asians, and then it's another word, it's each subsequent generation of immigrants. So you know, a friend of mine is fond of pointing out that when you mentioned a lot of the Jews had come over in a way of immigration, studying the sciences at one point in time, the predecessor to the NBA, which back then was heavily represented with Jewish

basketball players. And I was astonished when I first heard it, But neither did I the first time I heard that, I'm like, what come on, you're pulling my leg. But it's the same thesis of a wave of immigrants comes. They're willing to do stuff that everybody who's here and somewhat wealthy and a little spoiled perhaps don't want to do. And then after they go through that and achieve some degree of success, their kids don't want to do that, and it creates yet another opening, and then the next

wave of immigrants come. That was, uh, I wonder how far this continues, who who's going to follow the Chinese after that? But eight of your students as Chinese in this master's program, about like a hundred students, So I would say I'm guessing a little bit. I would say comes straight from mainland China. Another fort come from America or Europe, but the Chinese citizens and went their undergrad In fact, this is sort of a little funny. But my son who lives in Hong Kong, he teaches history

at Hong Kong University of Science and Technology. And when I've been there, of setting on his class and his classroom looks more cosmopolitan than mine. Really, yeah, I wonder what that's about. That's quite fashionating. He's got some fair amount of and expacts and people or Australians who come there, whereas our classes are pretty much nothing wrong with it. But that just shows you where it's going. So your students, who who are who graduate the master's degree in financial engineering?

Where they end up working after that? Some in China, some in money management, some in the risk management, a few in trading UM, some in non purely financial firms. Now there's a big trend towards machine learning and big data. UM. How many how many what percentage of your students stay in the United States and work in that field? Um, I would guess fifty or sixty, but I'm actually on shaky ground now. I don't know. For the ballpark about

half that wouldn't surprise you. Huh, that's quite fascinating. Um. So, speaking of students, what what sort of advice would you give to a millennial or someone who's just graduating from school and is interested in in a career in modeling and quantitative finance. Okay, I'm somewhat cynical stuff. I don't know.

I would say, first of all, yeah, I started out thinking I was going to have one career and you're the same from what you were telling And then you discover that you're going to change and life has defeated to day. Yeah too, So I've kind of had three in a sense. I started in physics, then I went to Bell Labs for a while, and then I ended up in finance. So I would say, expect to have more than one career. Don't do don't if it you're going to do one thing for the same time, it's

actually quite invigorating to change. Yeah, it's a whole different set of muscles, and after twenty years, it's kind of nice to have a difference. Um, I would say, get good at programming, at least that's my experience in almost anything. If you can do your own Yeah, I'd like to tell people to be willing to get your hands dirty and do your own dirty work. Don't just be a manager maybe one day. But that's the thing I was

going to say earlier. It summer seems to me a lot of maybe not now after the internet craze, but in the nineties, most Americans and when I worked at most Americans wanted to be managers and most foreigners wanted to work with their hands or their heads sort of spread. And now it's changed a little bit. I'm getting off topic.

It's changed a little bit because Americans are suddenly discovering that you can get rich by by being a good program and like like like Mark Zuckerberg or somebody like that, and so it's changed a little bit. But for a long time it seemed to me where you can just wanted to be managers, so why go do financial engineering,

whereas foreigners sort of had no choice. I wonder how much of that was a post War War two phenomena, because if you think about the era that fouled the Second World War, you had a huge rise of corporations and what we almost derisively described as middle management today, it was the path to a reasonably safe, certain comfortable job. But that's all gone away a long time ago. I wonder, I wonder how much of that is demographics and how

that's changed. That's interesting, Probably probably a large part. Yeah, I was trying to think. So I was saying, be wanting to do your own dirty work, get your hands dirty, and learned to program. You learned to program, don't ignore I don't know I was gonna say him. One thing I kind of learned is when I left physics. When I left particle physics, I am I was sort of a little bit disgusted with myself that I thought I was going to be a physicist and I wasn't gonna

be one. And they were jobs in apply physics as opposed to pure research like an energy or or heat, you know, heat stuff, and I didn't want to do that. I thought, if I'm going to get out of physics, I'm going to go sort of all the way. And it works for me. But at the same time, I realized over the years that almost everything is interesting, and that maybe I was wrong. I could have gotten just

as interested in doing something else. So I think the more applied as opposed to theoretical Yes, and maybe it might have still been interesting. And but but I sort of scandered at the time in a snobby sort of way. And um, my experience over the years with lots of things is that when you get involved in them hardcore and deeply, you find all sorts of interesting things that

you didn't expect. And so I think it's that's the advice I would give people is m is um plunge in and if you have to do something different, and and when you get deep inside you find a whole world opening up. And and then our final question, what is it that you know about modeling, about investing, about quantitative finance today you wish you knew thirty years ago when you you were first stepping into the field. Don't

get out when things look bad. Don't get out when things look bad, because in the long run, don't sell at the bottom. So, in other words, mean regression, that reversion to one of these people that that when everything was about to collapse, I thought, oh, my God, like now I've been a soul before it goes to zero and I'm but it doesn't go so far. It doesn't go to zero. So I would say take a long term VI you and ignore the fluctuations. But let's talk

to do Professor Dermott. I have to thank you for being so generous with you with your time. This has really been a fascinating conversation. I could sit here for hours longer, but I know you have places um to go and and things to do to review if people want to find your work. My life is a quant is on Amazon. Models Behaving Badly can be found just about anywhere. A Manual Derman dot com at a Manual Derman on Twitter, and your homepage is on at Columbia.

I don't actually have one in Columbia. I'll just have Emmanuel Deman dot com. And I once wrote a book of columns and short stories which you can get on Amazon as an e book that I put them myself. And what's the name of that's called bad Behavior? Bad Behavior. That's not a big best seller by any means. But I once wrote a bunch of columns for a German newspaper.

There was an editor there. He actually died shortly episode the Frank foot or argument at Siton who liked models behaving badly, And for about a year I wrote a column for them every two weeks, and I took most of it. They put it into German, but I had it in English, and I put most of it into this some little book. I'll put a link up to this when this goes up. Thank you UM so much for your time. For those of you who enjoy this conversation, look upward down an inch on iTunes and you could

see all of our prior conversations. I would be remiss if I did not thank UH my research direct jer Michael Batnick, who helped do the deep dive UH into Professor Derman's background. UH special thanks to Taylor Riggs for handling all the booking, and Charlie Vohmer for being our producer. You're listening to Masters in Business on Bloomberg Radio.