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Interview With Emanuel Derman: Masters in Business (Audio) · Masters in Business

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  Speaker 1: This is Masters in Business with Barry Ridholds on Bloomberg Radio.
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  Speaker 1: This week on Masters in Business on Bloomberg Radio, we
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  Speaker 1: have a special guest. If you are a fan of
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  Speaker 1: quantitative finance, modeling, any application of mathematics to the world
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  Speaker 1: of investing. UH. This is really a master class in
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  Speaker 1: what the world used to be like in finance when
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  Speaker 1: people really you know, math was optional as opposed to
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  Speaker 1: how things have developed today. Professor Emmanuel Derman is the
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  Speaker 1: head of Financial Engineering UH coursework in the master's program
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  Speaker 1: at Columbia University. His background is really quite amazing, and
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  Speaker 1: I go into a lot of detail UH in the program.
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  Speaker 1: Suffice it to say at Goldman Sachs, where he UH
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  Speaker 1: eventually became head of the renowned quantitative Strategies group. Gives
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  Speaker 1: you an insight of of what sort of UH mathematical
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  Speaker 1: and programming background he has helped Goldman Sacks make ungodly
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  Speaker 1: gobs of money by the intelligent application of modeling and
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  Speaker 1: risk management. A lot of people don't think about the
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  Speaker 1: blocking and tackling and the basic approach that you have
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  Speaker 1: to engage in when you're dealing with things like stock
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  Speaker 1: spoons and options of a liquid assets that that are
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  Speaker 1: hard to come up with a price because they don't
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  Speaker 1: necessarily trade all the time anyway. Really a fascinating conversation.
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  Speaker 1: If you're at all interested in in quantitative finance and modeling,
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  Speaker 1: um it's and you're not a student at Colombia, you
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  Speaker 1: don't often get access to someone like Professor Norman, and
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  Speaker 1: I think you'll find this to be a really interesting conversation.
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  Speaker 1: And so, without any further ado, my interview with Professor
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  Speaker 1: Emmanuel Derman. This is Masters in Business with Barry Ridholts
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  Speaker 1: on Bloomberg Radio. My special guest this week is a
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  Speaker 1: Manuel Derman. He is a particle physicist and better known
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  Speaker 1: in the world of finance as a quant His background
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  Speaker 1: is quite fascinating. Born in South Africa, came to the
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  Speaker 1: United States later in life, spent seventeen years on Wall Street,
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  Speaker 1: eventually becoming head of the renowned Quantitative Strategies group at
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  Speaker 1: Goldman Sachs, where he co developed the Black Derman Toy
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  Speaker 1: interest Rate model and the Derman Connie local Volatility model,
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  Speaker 1: both of which have become industry standards. He won the
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  Speaker 1: I a f E. Sun Guard Financial Engineer of the
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  Speaker 1: Year Award in two thousand. He's the director of the
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  Speaker 1: Master's Programming in Financial Engineering at Columbia and author of
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  Speaker 1: two books, the first, My Life as a quant Reflections
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  Speaker 1: on Physics and Finance, and more recently, Models Behaving Badly.
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  Speaker 1: Emmanuel Derman, Welcome to Bloomberg. Thanks, I'm very glad to
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  Speaker 1: be here. So you're someone whose career I have followed
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  Speaker 1: for a long time. Uh, and I would imagine a
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  Speaker 1: lot of our listeners are are probably not familiar with
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  Speaker 1: either yourself personally or what you do. So let's start
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  Speaker 1: with a really basic question. What is financial engineering? Okay,
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  Speaker 1: financial engineering, it's sort of a polyglock field. It's not
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  Speaker 1: really one simple thing. It developed over the last twenty
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  Speaker 1: five or thirty years. Um. It's a mix of building
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  Speaker 1: models for describing businesses and more particularly securities, involving mathematics, artistics,
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  Speaker 1: the use of computer science programming, all inspired somewhat by
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  Speaker 1: physics or scientific type models, and using the kind of
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  Speaker 1: math that's traditionally used in describing the inanimate world of
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  Speaker 1: the material world, but now applied, for better or for worse,
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  Speaker 1: to the world of stocks, bonds, securities, options, etcetera. So
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  Speaker 1: so that naturally leads to the next question, how does
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  Speaker 1: someone transition from being a specialist in particle physics to
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  Speaker 1: a specialist in quantitative finance. Yeah, it's like how do
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  Speaker 1: you get to Carnegie whole practice practice practice. So then,
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  Speaker 1: so there are obviously some similarities math. Yes, so a
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  Speaker 1: lot of a lot of them. Modeling in particularly in
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  Speaker 1: options and in describing fixed income instruments actually has its
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  Speaker 1: origin in in economists who were trained in physics and
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  Speaker 1: started applying that kind of math to physics or stochastic
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  Speaker 1: calculus series discunning already are physics, mathematics and pargular physics
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  Speaker 1: in spired models, and so it's kind of a fairly
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  Speaker 1: natural transision for people to make. The trouble is a
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  Speaker 1: lot of physicists don't have the economics of the finance background.
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  Speaker 1: And thirty years ago that wasn't a problem. When I
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  Speaker 1: did it, um, they didn't expect you to know anything.
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  Speaker 1: The whole quantitative finance field was sort of amateur heaven.
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  Speaker 1: You just came along and people told you to pick up.
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  Speaker 1: It happened to me when I came to Golden they said,
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  Speaker 1: read the Cox Ross rubens Steen model for pricing options
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  Speaker 1: and start working. And now so it was fairly easy
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  Speaker 1: in though, is that they hired you if you had potential.
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  Speaker 1: Now it's a much tougher transcision. It's become a professional field.
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  Speaker 1: I'm in charge of a program at Columbia where people
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  Speaker 1: get degrees in the field, professional degrees. And you can't.
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  Speaker 1: You can't just from an economic point of view. There's
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  Speaker 1: a glut of people not so easy to get in now.
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  Speaker 1: So when we look at physics, you're dealing with inanimate
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  Speaker 1: objects that aren't pushing back. They're not getting excited about rumors,
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  Speaker 1: they're not getting panicky. But in finance you have the
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  Speaker 1: individual players who are all suffer from cognitive foibles and
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  Speaker 1: emotional extremes. How do you adapt to that shift from
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  Speaker 1: particles which are pretty clean and interesting to humans which
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  Speaker 1: are messy and volatile. Yeah, that's um. You put your
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  Speaker 1: finger on the key problem in the whole field, which
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  Speaker 1: is some people. Yeah, people exactly. And and that's why
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  Speaker 1: the models don't really work as well. When you make
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  Speaker 1: a model, even Newtonian mechanics for describing how planets go
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  Speaker 1: around the Sun, the planets don't really care what you
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  Speaker 1: say about them, and if you publish an article about
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  Speaker 1: them that don't change their position even if you have
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  Speaker 1: the wrong theory about them. But really, if you look
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  Speaker 1: at what happens in financial markets, they interact with people
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  Speaker 1: in financial markets all about opinions, and opinions affect the
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  Speaker 1: future and affect the presence. So um, it's it's a
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  Speaker 1: much iffier field. And models don't work as well. They
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  Speaker 1: don't describe the system is accurately, but they're not useless.
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  Speaker 1: That's the famous George Box quote. All models are wrong,
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  Speaker 1: but some are useful. Obviously, if you have a useful
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  Speaker 1: model in finance, it can make a lot of money
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  Speaker 1: for for its owners. Um, how does somebody like George
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  Speaker 1: Soros and his theory of reflexivity, how market prices subsequently
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  Speaker 1: affect market prices? How does that impact someone who's trying
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  Speaker 1: to create a financial model. Well, I think it impacts
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  Speaker 1: I think most financial models don't really take a kind
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  Speaker 1: of reflexivity. There's some some models. Maybe you're starting to
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  Speaker 1: do that, but I wouldn't say they trading models. Um.
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  Speaker 1: If I can summarize in one sentence, I would say
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  Speaker 1: that anytime you build a model, it's a financial model
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  Speaker 1: is mathematical. You have to keep in the back of
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  Speaker 1: your head that you're actually short volatility, meaning if the
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  Speaker 1: world changes, your model is going to be wrong. It
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  Speaker 1: may make money for you, may lose money for you,
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  Speaker 1: but it's certainly going to be wrong. Most models only
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  Speaker 1: work in a very narrow regime where things are more
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  Speaker 1: or less like the world you're in currently. And you
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  Speaker 1: see when you get to negative interest rate, for example,
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  Speaker 1: or when volatility blows up, or when you get you know,
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  Speaker 1: the Great Financial Crisis, all of these models stopped working.
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  Speaker 1: Since you you hinted at this in terms of a
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  Speaker 1: changing regime, I'm thinking about the models that were put
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  Speaker 1: together by a firm like Long Term Capital Management in
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  Speaker 1: the late nineties, in the last minute we have. Is
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  Speaker 1: that why they blew up so spectacularly The world changed
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  Speaker 1: and their model failed to adapt plus hundred one leverage. Yeah.
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  Speaker 1: I think my impression of what happened to Long Term
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  Speaker 1: Capital was that they were basically looking for very small
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  Speaker 1: deviations most things that earn your money in market, So
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  Speaker 1: when you buy something liquid, because the liquid things tend
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  Speaker 1: to be cheap, and so they were buying off the
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  Speaker 1: run treasuries out of the money options. I don't know
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  Speaker 1: the details anymore, but essentially things that were cheap, but
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  Speaker 1: in the long run would revert to the mean and
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  Speaker 1: give you the full amount. But they leveraged up like
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  Speaker 1: crazy to accentuate small pennies differences. And then when Russia defaulted,
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  Speaker 1: what happened was everybody in the world got scared. There's
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  Speaker 1: a flight to quality. Nobody wants to hold the liquid stuff,
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  Speaker 1: and they were so overleveraged that they were they were
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  Speaker 1: put out of business. I'm very ridults. You're listening to
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  Speaker 1: Masters and Business on Bloomberg Radio. My special guest today
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  Speaker 1: is a Manuel Derman. He is a professor of financial
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  Speaker 1: engineering at Columbia University. He used to work at Goldman
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  Speaker 1: Sachs where he was head of the Quantitative Strategies group.
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  Speaker 1: And I want to talk a little bit about a
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  Speaker 1: book you wrote a few years ago after the financial
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  Speaker 1: crisis called Models Behaving Badly. And one of the first
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  Speaker 1: things that stood out to me from that book was
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  Speaker 1: sentence you had written, models are metaphors that explain the
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  Speaker 1: world we don't understand in terms that we do understand.
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  Speaker 1: Expand on that. Okay, Um, yeah, that's that's some that's
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  Speaker 1: my feeling. I can give an example. So what I
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  Speaker 1: tried to do in that book was distinguished between models
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  Speaker 1: and theories, as as different means of approaching trying to
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  Speaker 1: explain the world animate or inanimate, and models seemed to
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  Speaker 1: me to the analogies in the sense, for example, you
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  Speaker 1: say the brain is like a computer, or the computer
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  Speaker 1: is like a brain. People used to say now that
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  Speaker 1: now they go the other way. Or they say the
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  Speaker 1: heart is like a water pump. Or there's a great
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  Speaker 1: quote I like about fixed income from Schopenhauer where he says,
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  Speaker 1: sleep is the interest that we have to pay on
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  Speaker 1: the capital which is called in a death. And the
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  Speaker 1: more regularly the interest is paid, the further the date
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  Speaker 1: of redemption is postponed. So in other words, it's important
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  Speaker 1: to get eight hours nights. Yes, and if you can
• 10:27
  Speaker 1: get eight hours and I'd sleep. But if you think
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  Speaker 1: about what he's doing, he's sort of there's a small
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  Speaker 1: overlap between sleep and between paying interest on a bond,
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  Speaker 1: which is that you sleep regular and you pay interest
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  Speaker 1: on a bond. Rey that's really only analogy. And then
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  Speaker 1: but on a bond, you've borrowed money and you have
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  Speaker 1: to pay it back at the end. And now he's saying,
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  Speaker 1: you've borrowed your life from the darkness, and at the
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  Speaker 1: end you have to pay it back again, and you're
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  Speaker 1: paying back darkness all the way. So I think that's
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  Speaker 1: a good example. And most of the models in finance
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  Speaker 1: or analogies where you say, for example, um in the
• 10:57
  Speaker 1: Kape model or in or in in modern finance, you
• 11:01
  Speaker 1: say stock prices behave like smoke diffusing from from a
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  Speaker 1: cigarette end, you know, doing Branny in motion. That's not
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  Speaker 1: literally true. It could be true, but it isn't. So
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  Speaker 1: in other words, stock prices don't randomly fill a room
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  Speaker 1: evenly distributed like the behavior of a gas right, But
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  Speaker 1: you're creating a metaphor that, hey, it's somewhat random, it's
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  Speaker 1: a little predictable, but not very predictable, especially over the
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  Speaker 1: long term exactly. And and for example, in physics, that's
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  Speaker 1: called Brannyan motion, this motion of diffusion in physics, that
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  Speaker 1: really is a theory. It's an accurate description of the
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  Speaker 1: way smoke behaves. But stock prices don't behave that way.
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  Speaker 1: Volatility jumps, stock prices crash, apple rises are dumps buck
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  Speaker 1: twenty or thirty bucks in a day more actually son
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  Speaker 1: of ten percent in a day, So it's not it's
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  Speaker 1: not a diffusion, it's something that's that's violent. Um, So
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  Speaker 1: why do we why are we so enamored of all
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  Speaker 1: these metaphors because we can't do better? And the difference
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  Speaker 1: is I think economists don't understand. I'll to not be
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  Speaker 1: too rude about economists, but they don't understand the difference
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  Speaker 1: between a model and a theory, or a metaphor and
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  Speaker 1: an accurate description. And physicists, which is my original background,
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  Speaker 1: actually understand that very well. So for example, if you
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  Speaker 1: say Newton's laws, which destrive the planets going around the Sun,
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  Speaker 1: they say four sequals mass times acceleration. That's really accurate.
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  Speaker 1: It may not be perfect, but it's very accurate. On
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  Speaker 1: the other hand, they might say a nucleus at the
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  Speaker 1: center of an atom is like a liquid drop, and
• 12:29
  Speaker 1: they understand that when they say a nucleus is like
• 12:31
  Speaker 1: a liquid drop, it isn't really a liquid drop. It's
• 12:34
  Speaker 1: just a lot like it. But at some point that
• 12:35
  Speaker 1: analogy is going to break down, and people get Nobel
• 12:38
  Speaker 1: prizes for saying a nucleus is like a liquid drop,
• 12:41
  Speaker 1: and they get Nobel prizes for doing what Feynman did,
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  Speaker 1: which is an accurate description of electrons. But but physicists
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  Speaker 1: understand that one of them is truth or close to truth,
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  Speaker 1: and the other one's just a model. So let me
• 12:51
  Speaker 1: go off script and say, um, how accurate is it
• 12:55
  Speaker 1: to say that economists suffer from physics envy? Uh? I
• 13:00
  Speaker 1: don't know who originally coined that. I think maybe Andy Low,
• 13:02
  Speaker 1: who's a professor at M I T. I think that's accurate.
• 13:06
  Speaker 1: I think maybe it's fading to some extent now The
• 13:08
  Speaker 1: behavioral finance people clearly don't. But but yeah they do.
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  Speaker 1: And you know Bachelier, the guy who started a lot
• 13:16
  Speaker 1: of the stuff, and Paul Samuelson, they were all ahead
• 13:18
  Speaker 1: of physics smith background somewhere in there. And I noticed
• 13:21
  Speaker 1: that you drop a lot of philosophical references in discussion.
• 13:25
  Speaker 1: Is that from the financial engineering side or is that
• 13:28
  Speaker 1: from the physics side. Um, I've got a late life
• 13:31
  Speaker 1: interest in philosophy. Well, it's throughout your books. I notice
• 13:35
  Speaker 1: you're you're always referring to that. We'll come back to
• 13:38
  Speaker 1: that a little later. Um, let me give you a
• 13:41
  Speaker 1: quote of yours that I found intriguing and wildly overlooked
• 13:46
  Speaker 1: by a lot of investors. If you want to take
• 13:48
  Speaker 1: a chance on the upside, you have also to take
• 13:52
  Speaker 1: a chance on the down side. Explain what you mean
• 13:55
  Speaker 1: by that, what I mean by that. There was a
• 13:58
  Speaker 1: response to I think you wrote a book about this,
• 14:01
  Speaker 1: two to the bailouts of two thousand seven eight nine,
• 14:05
  Speaker 1: when I felt that there were a lot of companies around,
• 14:08
  Speaker 1: banks in particular, but financial companies, that we're taking risk
• 14:12
  Speaker 1: and saying that's the essence of capitalism and sort of
• 14:14
  Speaker 1: leave me alone and let me, let me do what
• 14:16
  Speaker 1: I'm good at. And then suddenly, when everything collapsed, they
• 14:20
  Speaker 1: wanted to be saved from death. And I found that incredibly,
• 14:25
  Speaker 1: I don't know whether ethically or morally, sort of yeah,
• 14:29
  Speaker 1: and saying, oh, the whole system will die if you
• 14:31
  Speaker 1: don't save us. And and that's what I meant. If
• 14:33
  Speaker 1: you want to be somebody that benefits from taking risk,
• 14:36
  Speaker 1: you also have to benefit when taking risk kills you.
• 14:39
  Speaker 1: So in other words, you can't have privatized gains and
• 14:42
  Speaker 1: socialized laws. Yes, and I like to think that what
• 14:44
  Speaker 1: a lot of the banks should have done if they
• 14:46
  Speaker 1: were saved by the saved by the government, was they
• 14:51
  Speaker 1: were essentially given a put if you look at it
• 14:52
  Speaker 1: from an options point of view, they were safe from death,
• 14:55
  Speaker 1: and if they were given a put they should have
• 14:57
  Speaker 1: given away a call to the when they were When
• 15:01
  Speaker 1: they survived, companies had a little a I G. Gave
• 15:04
  Speaker 1: the government some uh, I mean it was forced upon them,
• 15:07
  Speaker 1: and then the G S, C S, Fannie and Freddie
• 15:10
  Speaker 1: they essentially became Yeah, yeah, so that's fair. You can't
• 15:15
  Speaker 1: get ten million dollar bonuses the rfter you was saved from.
• 15:19
  Speaker 1: Makes sense that should we have nationalized those companies, cleaned
• 15:23
  Speaker 1: them up, and then spun them out as free standing
• 15:25
  Speaker 1: companies with the benefits of those I p o s
• 15:28
  Speaker 1: going to the taxpayer? Is that a better ethical way
• 15:31
  Speaker 1: to do that. I think it's a better ethical way
• 15:33
  Speaker 1: to do it. I have friends who argue with this
• 15:35
  Speaker 1: about him. They say the whole system would have collapsed
• 15:37
  Speaker 1: if we hadn't bailed them out. Um, people are not
• 15:39
  Speaker 1: what the FED who say everything was credit was on
• 15:42
  Speaker 1: the verge of sort of freezing up. But I think
• 15:46
  Speaker 1: it's left a permanent um, a permanent bad taste in
• 15:49
  Speaker 1: everybody's mouth. Certainly created moral hazard that Hey, you know, look,
• 15:54
  Speaker 1: it's arguable to say we rescued bear Sterns and the
• 15:58
  Speaker 1: FED backed up JP Morgan's purchase, and that might have
• 16:02
  Speaker 1: given the impetus to Lehman Brothers to say someone will
• 16:06
  Speaker 1: come along and rescue us. Right? Is that fair? Fair
• 16:09
  Speaker 1: assessment of that? There was a funny cartoon. This was
• 16:11
  Speaker 1: a little maybe not right, but there was a funny
• 16:14
  Speaker 1: cartoon by Barry Blitzer or Berry Blitzer, forget his name
• 16:17
  Speaker 1: is in New York and New Yorker cartoonists who had
• 16:20
  Speaker 1: a picture somewhere in two thousand and eight of Obama
• 16:23
  Speaker 1: dressed as a as a New York City policeman with
• 16:26
  Speaker 1: a hat and a navy navy uniform and walking down
• 16:30
  Speaker 1: the street tooling of a ton with these eyes cast
• 16:33
  Speaker 1: up in heaven as though he couldn't see what was
• 16:34
  Speaker 1: around him, and meanwhile people behind him or running with
• 16:36
  Speaker 1: bags of money into buildings. And that's sort of typified,
• 16:40
  Speaker 1: typified it for me. But it was certainly an interesting period.
• 16:44
  Speaker 1: I'm Barry Ridults. You're listening to Masters in Business on
• 16:47
  Speaker 1: Bloomberg Radio. My special guest today is a manual German.
• 16:51
  Speaker 1: He spent seventeen years at Goldman Sachs, eventually becoming head
• 16:56
  Speaker 1: of the renowned quantitative strategies group. He all So is
• 17:00
  Speaker 1: currently uh, the director of financial Engineering at Columbia University.
• 17:06
  Speaker 1: It's a master's program, I believe is that the master's program.
• 17:09
  Speaker 1: It's a sort of travel month eighteen month professional degree.
• 17:12
  Speaker 1: He also is the author of Models Behaving Badly and
• 17:17
  Speaker 1: My Life as a Quant Reflections on Physics and Finance.
• 17:21
  Speaker 1: Let's talk a little bit about quants on Wall Street.
• 17:25
  Speaker 1: You mentioned when you began twenty five years ago or so,
• 17:28
  Speaker 1: it was a wide open field. There was no playbook.
• 17:32
  Speaker 1: But here it is. We have high frequency trading, we
• 17:35
  Speaker 1: have al go driven strategies, we we have all manners
• 17:40
  Speaker 1: of mathematical um investing, mathematically based investing. Is this the
• 17:48
  Speaker 1: age of the quant? Yeah? I think it is. Actually,
• 17:53
  Speaker 1: I mean when I started out, very few traders were
• 17:55
  Speaker 1: very numerous. They didn't know a lot of math, they
• 17:58
  Speaker 1: hadn't studied. A lot of them came from law backgrounds
• 18:00
  Speaker 1: and stuff like that. And now, in fact, quant was
• 18:03
  Speaker 1: a derogatory word. I've got a dictionary from the late
• 18:07
  Speaker 1: nineties where somebody it's it's by Mark Chritsman, called a
• 18:10
  Speaker 1: dictionary financial terms, and he says quanton. He says quantity
• 18:14
  Speaker 1: of analysts often used pejoratively and when I wrote that
• 18:16
  Speaker 1: book called My Life as a Quant. What was actually
• 18:19
  Speaker 1: in the back of my head was I took my
• 18:20
  Speaker 1: kids twenty five years ago to see us a Swedish
• 18:23
  Speaker 1: movie called My Life as a Dog. You see, there
• 18:26
  Speaker 1: was a great movie as for kids and parents to
• 18:29
  Speaker 1: see simultaneously, and I was seeing on My Life as
• 18:32
  Speaker 1: a Dog. That was really the back in the back
• 18:33
  Speaker 1: of my mind. When I came to Goldman, quant was
• 18:35
  Speaker 1: a sort of like being a geek or being a
• 18:38
  Speaker 1: and people laughed at you, although they kind of treated
• 18:40
  Speaker 1: you with respect. I liked it, but it was a
• 18:42
  Speaker 1: kind of mocking kind of respect. So since you brought
• 18:45
  Speaker 1: that up, let me go back to a question I missed.
• 18:48
  Speaker 1: So you were at Goldman Sachs in the ninety nineties,
• 18:50
  Speaker 1: that had to be one of the questions I was
• 18:53
  Speaker 1: going to ask you, was how were the quants treated?
• 18:56
  Speaker 1: So you you said there was some degree of respect,
• 18:59
  Speaker 1: but was it was there? Were you treated as one
• 19:03
  Speaker 1: of their own? Or were you guys kind of on
• 19:07
  Speaker 1: the outskirts and people, you know, slipped pizza under the
• 19:10
  Speaker 1: door and kind of left you alone. Um, that's funny.
• 19:14
  Speaker 1: It varied, it varied um for a while for a
• 19:18
  Speaker 1: while I worked for Fisher Black in his group, and
• 19:20
  Speaker 1: then he had everybody sit on the desk and get
• 19:23
  Speaker 1: lunch when the traders got lunch, and you were kind
• 19:25
  Speaker 1: of more equal. But generally I would say, that didn't
• 19:28
  Speaker 1: last long, and people people treated you like like geeks,
• 19:32
  Speaker 1: but like useful but useful people that they spoke to you.
• 19:35
  Speaker 1: What's the expression useful idiots? Is that how they looked
• 19:37
  Speaker 1: at did was that the action when I got in
• 19:42
  Speaker 1: When I got there in eighty five, which was earlier,
• 19:44
  Speaker 1: people would get in the elevator and sort of make
• 19:46
  Speaker 1: jokes and say, oh, all of you allowed to travel
• 19:47
  Speaker 1: in the same elevator at the same time. Or I
• 19:51
  Speaker 1: would be with I wrote about it in my life
• 19:52
  Speaker 1: as a quant. I would be with some guy and
• 19:53
  Speaker 1: I would I was very excited about being on Wall
• 19:55
  Speaker 1: Street and it was interesting. I would say something to
• 19:57
  Speaker 1: him about duration or convexity, and he would get embarrassed
• 20:00
  Speaker 1: and shrink away and say, what do you think of
• 20:02
  Speaker 1: the Yankees last night? You know? So, in other words,
• 20:04
  Speaker 1: he didn't know what convects it your duration? Actually now
• 20:06
  Speaker 1: he knew what it was. He just didn't want to
• 20:08
  Speaker 1: be outed in the fation in other words, he was
• 20:11
  Speaker 1: a geek, but he didn't he didn't like me talking
• 20:14
  Speaker 1: about it in public. But then, but then, even in
• 20:17
  Speaker 1: the early nineties, you know, I remember when not just Quantz,
• 20:20
  Speaker 1: but everybody. If you had a PhD, you didn't put
• 20:24
  Speaker 1: it on your business card, and if you had an
• 20:25
  Speaker 1: email address, you didn't put it on your business card
• 20:27
  Speaker 1: because that was like the brand of Caine. You know,
• 20:30
  Speaker 1: that's amazing. I think that that's early nineties. People didn't
• 20:35
  Speaker 1: want to put email addresses or or or PhD degrees
• 20:38
  Speaker 1: on their business card. Now, of course they'd love to
• 20:41
  Speaker 1: do it. That's wow. Become things have changed. So you
• 20:45
  Speaker 1: were Goldman when they went public, right, what what was
• 20:49
  Speaker 1: that like from the experience of someone with your background
• 20:54
  Speaker 1: on Wall Street? You know, I think things were steadily
• 20:56
  Speaker 1: improving for quans Alo at the same time Goldman was
• 20:58
  Speaker 1: getting much more bureaucrats. But by that time, you've been
• 21:01
  Speaker 1: through UM long Term Capital, You've been through d Shore,
• 21:06
  Speaker 1: so LTCM, so all sorts of hedge funds starting to
• 21:10
  Speaker 1: make a lot of money out of supposedly quantitative strategies,
• 21:13
  Speaker 1: and and particularly the whole Internet Internet and not Bubble,
• 21:17
  Speaker 1: but the whole Internet excitement where suddenly um being being
• 21:21
  Speaker 1: technologically competent became a way to make money. And so
• 21:24
  Speaker 1: that even destroying destroying the whole system, like LTC M
• 21:29
  Speaker 1: gave you, gave you respect. So things were getting better
• 21:32
  Speaker 1: for quants. But by the late nineties, I have to
• 21:34
  Speaker 1: think renaissance technologies had been putting up absurd returns for
• 21:39
  Speaker 1: a long time, right, And if memory serves didn't Jim
• 21:43
  Speaker 1: Simon's return outside investors money sometime around then is maybe
• 21:48
  Speaker 1: a little bit later, but yeah, they only have their
• 21:50
  Speaker 1: own money now, which is which is tells you, hey,
• 21:53
  Speaker 1: these guys are really putting up huge returns without a
• 21:57
  Speaker 1: whole lot of capacity to do it. In the tens
• 22:01
  Speaker 1: of billions your d shore as well returns outside money.
• 22:05
  Speaker 1: And no, I didn't return outside money, but we're very
• 22:07
  Speaker 1: visible and and at that point don't people look around
• 22:11
  Speaker 1: and say, hey, these guys are minting money. Let's get
• 22:14
  Speaker 1: us some quants, and maybe we need to pay these
• 22:16
  Speaker 1: people proper respect. Well, they gave them respect, and they
• 22:19
  Speaker 1: paid them kind of decently, but there was still, at
• 22:22
  Speaker 1: least in the area I worked in at that point,
• 22:24
  Speaker 1: there was still a very fine oute gap between being
• 22:26
  Speaker 1: a trader and being a quant Now, I think that's
• 22:28
  Speaker 1: vanished a lot. But then you were a support person,
• 22:31
  Speaker 1: you went a position taker. I'm Barry Ridholts. You're listening
• 22:35
  Speaker 1: to Masters in Business on Bloomberg Radio. My special guest
• 22:38
  Speaker 1: today is a Manual German. He is a renowned quantitative thinker.
• 22:44
  Speaker 1: UH teachers financial engineering at Columbia. You surround the quant
• 22:48
  Speaker 1: group at Goldman Sachs. I think that's a pretty good
• 22:52
  Speaker 1: CVUH to chat with. Little O me. Let's let's talk
• 22:56
  Speaker 1: a little bit about models and what they can and
• 23:00
  Speaker 1: can't do. Because you've every answer you've given me has
• 23:03
  Speaker 1: has set off in my mind the number of digressions.
• 23:06
  Speaker 1: But I wanted to stick with some of the questions.
• 23:10
  Speaker 1: So you look at models, and you look at what
• 23:13
  Speaker 1: they can do. What is it that people can do
• 23:17
  Speaker 1: that models can't Well, I think the right way to
• 23:21
  Speaker 1: use models is to sort of quantify your intuition. Like
• 23:24
  Speaker 1: people can have intuition, but it's hard to translate that
• 23:27
  Speaker 1: intuition into a number. And so for example, if somebody
• 23:30
  Speaker 1: said to you, um, what should I pay for an option?
• 23:33
  Speaker 1: Another price of an option? At the money? What should
• 23:35
  Speaker 1: I pay for an out of the money option. You
• 23:38
  Speaker 1: know it should be less, but it's hard to actually
• 23:40
  Speaker 1: quantify how much less and if you have something. But
• 23:43
  Speaker 1: when you look at black shoals or options pricing, they
• 23:46
  Speaker 1: invent the whole notion of volatility and measuring volatility. And
• 23:49
  Speaker 1: just like you can have intuition about interest rates, a
• 23:51
  Speaker 1: human being can have intuition about will volatility go up,
• 23:54
  Speaker 1: how much will it go up by? How much would
• 23:56
  Speaker 1: go down by? And what the model does is let
• 23:58
  Speaker 1: you take something you can think about in your head,
• 24:02
  Speaker 1: like interest rates, abstractly and converted into a dollar price.
• 24:07
  Speaker 1: So now let me ask the opposite of that question.
• 24:10
  Speaker 1: Let's let's say you've developed a model and it's working well.
• 24:13
  Speaker 1: At what point should a human intervene and say, hey,
• 24:18
  Speaker 1: this model is no longer producing the output we expect.
• 24:21
  Speaker 1: We need to make modifications. How does one even begin
• 24:24
  Speaker 1: to think about that? Um. When I worked at Golden
• 24:27
  Speaker 1: we kind of try to write to the last year.
• 24:29
  Speaker 1: The last years I was there. I was in a
• 24:31
  Speaker 1: group called firm wide Risk not in Quantitative Strategies, where
• 24:34
  Speaker 1: we were trying to look at derivatives risk throughout the firm,
• 24:37
  Speaker 1: and we sort of had a recommendation that every time
• 24:39
  Speaker 1: somebody write a model, they'd be forced to specify all
• 24:43
  Speaker 1: the assumptions and conditions they were they were making so
• 24:46
  Speaker 1: that they could specify when these things wouldn't hold. So,
• 24:49
  Speaker 1: for example, if you build a model to price options
• 24:53
  Speaker 1: on Apple, you're pretending interest rates will be pretty much stable.
• 24:57
  Speaker 1: You don't worry about interest rates. But if you suddenly
• 24:59
  Speaker 1: go to some emerging my icket county or interest rates
• 25:01
  Speaker 1: can rock at somebody should understand that that's not the
• 25:04
  Speaker 1: right model to use. That's interesting. Um. On a related note,
• 25:09
  Speaker 1: how could you tell the difference between a model working
• 25:13
  Speaker 1: its way through a rough patch and a model that
• 25:15
  Speaker 1: just no longer works. That's a really tough question. That's
• 25:19
  Speaker 1: especially applicable to people who do a statistical arbitrage or
• 25:23
  Speaker 1: people you mentioned like Renaissance or UM. I think that's
• 25:27
  Speaker 1: very difficult. You have to two ways. The first way
• 25:30
  Speaker 1: is statistical. You sort of have to have some idea
• 25:32
  Speaker 1: of when you're getting results that are statistically unlikely. So
• 25:36
  Speaker 1: if your model is correct, maybe you'll get, you know,
• 25:39
  Speaker 1: some fraction of the time something that's one or two
• 25:41
  Speaker 1: standard deviations away. If that persists, you start to sam
• 25:44
  Speaker 1: in a different regime or the model is broken down.
• 25:47
  Speaker 1: And then the second way, which which I also like,
• 25:49
  Speaker 1: is you really ought to have some model. Isn't just mathematics.
• 25:52
  Speaker 1: A model has some idea beneath its structural about the
• 25:55
  Speaker 1: way things behave, how people respond, how markets behave, and
• 25:58
  Speaker 1: you ought to be able to make some sort of
• 26:00
  Speaker 1: judgment as to whether the world is still behaving according
• 26:02
  Speaker 1: to the assumptions that you make. So it has to
• 26:04
  Speaker 1: be disprovable if X and y and z happen, therefore
• 26:07
  Speaker 1: the underlying thesis and the model no longer applies. Yes,
• 26:10
  Speaker 1: that's quite that's quite interesting. I like the idea that
• 26:13
  Speaker 1: that I was going to say this earlier. I sort
• 26:16
  Speaker 1: of think just using mathematics blindly is kind of stupid,
• 26:19
  Speaker 1: And most models get their inspiration out of some economic
• 26:23
  Speaker 1: or or or financial idea first, and the mathematics is
• 26:26
  Speaker 1: just the implementation. Where you get into trouble is when
• 26:28
  Speaker 1: you think that the mathematics is the thing in itself
• 26:30
  Speaker 1: rather than the idea that's behind it. That that goes
• 26:34
  Speaker 1: back to the George Box quote. They're wrong, but some
• 26:37
  Speaker 1: are useful. Let's so let's talk a little bit about
• 26:42
  Speaker 1: some changes that that quants have forced on on both
• 26:46
  Speaker 1: markets and investing. There was recently a column in The
• 26:49
  Speaker 1: Financial Times that talked about the secret source of hedge
• 26:53
  Speaker 1: funds and how quants are essentially reducing what some people
• 26:59
  Speaker 1: have previously called as alpha and identifying it as a
• 27:03
  Speaker 1: factor that can be reduced to mathematics. And others have
• 27:08
  Speaker 1: suggested that the quants are part of the reason why
• 27:12
  Speaker 1: hedge fund performance has been so mediocre over the past
• 27:15
  Speaker 1: decade or so. As the quants have risen in prominence
• 27:19
  Speaker 1: and stature and influence, the ability of a person working
• 27:23
  Speaker 1: in a hedge fund to create out performance, to develop
• 27:27
  Speaker 1: alpha is going away because of of what quants are doing.
• 27:33
  Speaker 1: What What are your thoughts on that? I've sort of
• 27:36
  Speaker 1: got a rush of thoughts coming to my head. Say
• 27:38
  Speaker 1: that because I think all of the things you said
• 27:40
  Speaker 1: are partially true. I think the first one, it's true
• 27:42
  Speaker 1: that the whole hedge fund and st allocation or asset
• 27:46
  Speaker 1: management world has become much more quantitative. When I started out,
• 27:49
  Speaker 1: nobody in those areas knew a lot of math or
• 27:52
  Speaker 1: use a lot of math. Now they all do um.
• 27:56
  Speaker 1: As a result, I think they're all in competition with
• 27:58
  Speaker 1: each other, And I think if somebody is a good
• 28:00
  Speaker 1: idea some way people move around a lot, and these
• 28:02
  Speaker 1: I've literally seen examples of somebody from one firm going
• 28:05
  Speaker 1: somewhere else bringing an idea there, they use it, they
• 28:08
  Speaker 1: get irritated because somebody else leaves and takes it somewhere else.
• 28:11
  Speaker 1: So I think these models propagate a lot and and
• 28:14
  Speaker 1: become become widely used, and that does cut into the
• 28:17
  Speaker 1: so called alpha of of everybody. Um. We saw a
• 28:21
  Speaker 1: little bit of that with LTCM when people had left
• 28:25
  Speaker 1: at or the people covering them at different brokerage firms
• 28:28
  Speaker 1: ended up moving around learning a little bit about what
• 28:31
  Speaker 1: they were doing, and so a lot of people were
• 28:33
  Speaker 1: piggybacking those trades as bad as the leverage was. When
• 28:37
  Speaker 1: every desk on the streets imitating it, it's really a
• 28:41
  Speaker 1: crowded trade. Yes, And now I think people actually have
• 28:43
  Speaker 1: sort of statistical mathematical models that they fit to the
• 28:47
  Speaker 1: whole the whole surface of of stock prices and decide
• 28:50
  Speaker 1: which ones are cheap and which ones are rich. And
• 28:53
  Speaker 1: somebody takes that model somewhere else or or or maybe
• 28:56
  Speaker 1: finds the inspiration for that model in some finance paper
• 28:59
  Speaker 1: that buried somewhere, and people people actually mind finance papers
• 29:02
  Speaker 1: for these sort of anomalies or behavioral anomalies and sought
• 29:05
  Speaker 1: to implement them everywhere. So I think these methods are
• 29:08
  Speaker 1: finding alpha getting a short run, shorter lifetime. So in
• 29:12
  Speaker 1: other words, it's either arbitraged away or just imitated and
• 29:16
  Speaker 1: and it loses its ability. That that's my impression. Yeah,
• 29:19
  Speaker 1: But at the same time, so there is a lot
• 29:21
  Speaker 1: more of this quantitative stuff. But at the same time,
• 29:23
  Speaker 1: to be a little on the cynical side, all of
• 29:25
  Speaker 1: the hitge funds and asset managers are in a competition
• 29:28
  Speaker 1: for assets under management, and they all like to pretend
• 29:31
  Speaker 1: that they have more secret source than maybe they actually do. Right,
• 29:35
  Speaker 1: So what does that tell us about the future of
• 29:38
  Speaker 1: of asset management. We've watched lots of money flow to Vanguard,
• 29:43
  Speaker 1: which is primarily a huge indexing shop, but we've also
• 29:46
  Speaker 1: seen a lot of money flow to hedge funds despite
• 29:50
  Speaker 1: a pretty bad run of underperformance. What explains that is it?
• 29:55
  Speaker 1: Is it just marketing wizardry or is there something more
• 29:58
  Speaker 1: than that? No, I think there are people and hedge
• 30:00
  Speaker 1: funds that really do have um some skill that that
• 30:04
  Speaker 1: that people who are just doing indexing, which doesn't take
• 30:07
  Speaker 1: any skill at all. Really, I think they do have
• 30:09
  Speaker 1: a skill um. I've seen studies that show that, especially
• 30:12
  Speaker 1: in in very liquid complex introments like mortgages UM as
• 30:16
  Speaker 1: opposed to equity long short, which is fairly simple, that
• 30:19
  Speaker 1: hedge funds that tend to do better than average one year,
• 30:22
  Speaker 1: there's some persistence they tend to do better than average
• 30:24
  Speaker 1: the next year. So it's not just a random district.
• 30:26
  Speaker 1: I don't think it's just random. I think if you
• 30:28
  Speaker 1: have skill, that tends to persist. But on the other hand,
• 30:31
  Speaker 1: the whole world is becoming so swept by people trading mechanically.
• 30:36
  Speaker 1: For example, I think if you're a value investor now,
• 30:38
  Speaker 1: then you kind of buy things when they go down
• 30:41
  Speaker 1: and you think they're cheap. On the other hand, the
• 30:43
  Speaker 1: momentum investors who think momentum is a factor and they
• 30:46
  Speaker 1: sell things that are going down and buy things that
• 30:48
  Speaker 1: are going up. So I think the large number of
• 30:51
  Speaker 1: people that are now acting in this mechanical way with
• 30:53
  Speaker 1: some model like following momentum tends to mess up the
• 30:57
  Speaker 1: people who are looking for value, and it's much harder
• 30:59
  Speaker 1: for hedge funds to compete in this world. There's a
• 31:02
  Speaker 1: quant name West Gray who writes Alpha Architect, and they
• 31:06
  Speaker 1: have a model that is both momentum and value. It's
• 31:09
  Speaker 1: actually two sleeves, and he said one sleeve is always
• 31:12
  Speaker 1: doing great while the other one is doing terrible, and
• 31:14
  Speaker 1: vice versa. But over the long haul, both of them
• 31:17
  Speaker 1: actually work out pretty well. That just gives you an
• 31:20
  Speaker 1: idea of the complexity of what we've seen come along.
• 31:24
  Speaker 1: That just there was nothing like that ten or twenty
• 31:27
  Speaker 1: years ago. You know, you look at the risk parity
• 31:29
  Speaker 1: people that say you should have instead of being sixty
• 31:32
  Speaker 1: equities bonds along the same philosophy you bedroo of having
• 31:36
  Speaker 1: one third of your risk in commodities, one third of
• 31:38
  Speaker 1: your risk and equities and one third of your risk
• 31:40
  Speaker 1: and bonds, which means livering leveraging a bonds a lot
• 31:43
  Speaker 1: because they have such low low volatility, and they also
• 31:47
  Speaker 1: believe that in the long run that will work best.
• 31:49
  Speaker 1: But in fact they've done badly for the last few months.
• 31:51
  Speaker 1: So well, you know, we saw a lot of money
• 31:53
  Speaker 1: floated risk parity, as as bonds looked like as rates
• 31:57
  Speaker 1: looked like they were bottoming, and as lot of the
• 32:00
  Speaker 1: commodity complex seemed to be topping out. About five years ago.
• 32:04
  Speaker 1: So if you're a third commodities and gold is down
• 32:06
  Speaker 1: thirty percent and oil is cut in half, that's gonna
• 32:09
  Speaker 1: have a pretty big impact on on your returns. Yeah.
• 32:12
  Speaker 1: I like to be a little philosophical and say that
• 32:15
  Speaker 1: if you take a model too seriously, it's a kind
• 32:17
  Speaker 1: of idolatry in the sense you're assuming that something somebody.
• 32:21
  Speaker 1: You're assuming that you can write down a formula that's
• 32:23
  Speaker 1: gonna mimic the way people behave. But people are too
• 32:26
  Speaker 1: complicated and and if you really believe that you can
• 32:29
  Speaker 1: capture people in a formula equation, you're you're looking for
• 32:32
  Speaker 1: trouble in the long run. I know you have a website,
• 32:34
  Speaker 1: a Manual Derman dot com. If people want to find
• 32:37
  Speaker 1: more of your your writings, it's my life is a
• 32:40
  Speaker 1: quant and models behaving badly, And you post regular papers
• 32:44
  Speaker 1: at a manual Derman dot com not that often anymore.
• 32:47
  Speaker 1: I do go on Twitter regularly. I'm a fan. Um
• 32:51
  Speaker 1: what is your Twitter handle? ETI manual dermant in one
• 32:54
  Speaker 1: word at a Manual Derman. If you've enjoyed this conversation,
• 32:58
  Speaker 1: be sure and check out our podcast extras, where the
• 33:01
  Speaker 1: tape keeps rolling, and we continue the conversation about physics
• 33:05
  Speaker 1: and quantitative trading and philosophy. Be sure and check out
• 33:09
  Speaker 1: my daily column on Bloomberg View dot com. Follow me
• 33:13
  Speaker 1: on Twitter at rid Halts. I'm Barry Ridholts. You're listening
• 33:16
  Speaker 1: to Masters in Business on Bloomberg Radio. Welcome back to
• 33:20
  Speaker 1: the podcast. Thank you so much, Professor Derman for doing this.
• 33:24
  Speaker 1: This is really fascinating stuff. And I don't think people
• 33:27
  Speaker 1: get to hear you often enough other than a handful
• 33:30
  Speaker 1: of Columbia students. Um. And I've been following your career
• 33:34
  Speaker 1: long enough that I really wanted to get you in
• 33:36
  Speaker 1: here and and put you under the microscope. So so
• 33:41
  Speaker 1: let's go over a couple of questions that we missed
• 33:43
  Speaker 1: earlier on And I know I only have you for
• 33:46
  Speaker 1: a finite amount of time, but we're we got plenty
• 33:49
  Speaker 1: of time to go. Um, So first let's go back
• 33:53
  Speaker 1: to this was way ahead of the curve in you
• 33:59
  Speaker 1: wrote an article titled model risk, pointing out the dangers
• 34:04
  Speaker 1: that inevitably accompany the use of models. How did that
• 34:08
  Speaker 1: play out? I think it played out pretty accurately. Actually,
• 34:12
  Speaker 1: although I was considered I was sort of looking I
• 34:15
  Speaker 1: can't remember too clearly. But I was looking at two
• 34:17
  Speaker 1: kinds of risk. One was implementation risk, where you have
• 34:20
  Speaker 1: the idea right, but you've got all sorts of computer
• 34:22
  Speaker 1: problems or efficiency problems. Then I was looking at what
• 34:25
  Speaker 1: happens when you actually have the idea wrong. And yeah,
• 34:28
  Speaker 1: there was the first paper I think ever written on
• 34:30
  Speaker 1: model risk, and I think it played out kind of accurately.
• 34:33
  Speaker 1: That's what sort of what sort of happened. So so
• 34:35
  Speaker 1: the next question, let's see if I can fix this
• 34:37
  Speaker 1: little thing here that is there you go. So the
• 34:42
  Speaker 1: crisis in two thousand seven two thou eight, how much
• 34:47
  Speaker 1: of that was a function of UM models not working well?
• 34:54
  Speaker 1: You know, as part of it definitely was, But I
• 34:56
  Speaker 1: don't think it was the fundamental cause there are a
• 34:58
  Speaker 1: lot of things happening. My sort of slightly biased view
• 35:03
  Speaker 1: is that UM really interest rates were very low, and
• 35:07
  Speaker 1: everybody was trying to stretch for yield and do anything
• 35:09
  Speaker 1: that would get more yield. Absolutely, and that's partly the
• 35:12
  Speaker 1: federal reserves fault, but whatever. And I think models played
• 35:17
  Speaker 1: a secondary role in that they were used to construct,
• 35:19
  Speaker 1: perhaps somewhat deceptively, through the rating agencies instruments that that
• 35:24
  Speaker 1: purported to give you a high yield with a low risk,
• 35:27
  Speaker 1: and some models were, um, we're a tool in trying
• 35:30
  Speaker 1: to cater to high risk, but I don't cater to
• 35:33
  Speaker 1: to disaster. But I don't think they were the fundamental cause.
• 35:37
  Speaker 1: It's certainly when you look at the rating agencies. I'll
• 35:40
  Speaker 1: never forget being in a conference room when a bunch
• 35:44
  Speaker 1: of salespeople came in and we're pitching us this new
• 35:48
  Speaker 1: fangled subprime product, and the phrase that resonated me with
• 35:53
  Speaker 1: me was this is just as safe as U S treasuries,
• 35:56
  Speaker 1: but it pays two fifty basis points more. And to
• 36:01
  Speaker 1: me it was, well, which is it? It can't pay
• 36:04
  Speaker 1: that much more? If it pays a little more, and
• 36:06
  Speaker 1: get arbitraged away, but how could it pay almost three more?
• 36:11
  Speaker 1: That either you guys are gonna win the Nobel Prize
• 36:13
  Speaker 1: or you're gonna be wearing aren't jumpsuits picking up trash
• 36:16
  Speaker 1: on the side of the road. It turned out neither
• 36:18
  Speaker 1: were true. They most of the people who participate in that, well,
• 36:22
  Speaker 1: they just moved on to the next thing and there
• 36:24
  Speaker 1: was no subsequent accountability. But the rating agencies and the
• 36:29
  Speaker 1: idea that hey, we've created taken draws and turned it
• 36:34
  Speaker 1: into gold. People wildly believed that that was possible, wasn't it. Yeah,
• 36:39
  Speaker 1: you know, I always like to make the analogy that
• 36:41
  Speaker 1: finances a lot like nutrition, in that people take a
• 36:45
  Speaker 1: small amount of information and extrapolated like crazy. You know,
• 36:48
  Speaker 1: so they will tell you women should or shouldn't get estrogenally,
• 36:51
  Speaker 1: you should or shouldn't eat eggs, and then they changed
• 36:53
  Speaker 1: in mind dramatically a few months a few years later,
• 36:56
  Speaker 1: and it's hard to tell who's a crank and who's correct, because, um,
• 37:00
  Speaker 1: basically they're all aimed at I don't know, they're all
• 37:02
  Speaker 1: aimed at marketing. In some sense, I'm fond of saying
• 37:05
  Speaker 1: predictions and forecasts and marketing because that's all they are.
• 37:09
  Speaker 1: But it's amazing how common sense, and granted this is
• 37:16
  Speaker 1: all after the fact, but a lot of common sense
• 37:19
  Speaker 1: could have saved people a lot of money if wait,
• 37:22
  Speaker 1: how could it be as safest treasuring yet pay so
• 37:25
  Speaker 1: much more? But nobody really stopped to ask those questions.
• 37:28
  Speaker 1: Then it it's amazing that nineties paper is really a
• 37:32
  Speaker 1: decade ahead of its time and it turned out, um
• 37:35
  Speaker 1: really to be significant, which leads to my next question.
• 37:38
  Speaker 1: So models behaving badly. How did that impact capitalism and
• 37:43
  Speaker 1: the Great Financial Crisis? Yeah, I mentioned this earlier, but
• 37:49
  Speaker 1: I've found the whole thing really disappointing. I'm sort of
• 37:51
  Speaker 1: disillusioned in that. That's what I said earlier. Some I
• 37:54
  Speaker 1: expected that. Um, I sort of like capitalism, but as
• 37:58
  Speaker 1: I said before, I think if you to benefit from
• 38:01
  Speaker 1: from taking risk, you've got to suffer the consequences. And
• 38:04
  Speaker 1: that really hasn't happened. And I'm with you on that also.
• 38:08
  Speaker 1: It's well from my perspective, it's like everyone's a capital nation.
• 38:14
  Speaker 1: Everybody's a capitalist until you know they're in trouble, and
• 38:18
  Speaker 1: then suddenly they they become temporary socialists until they get there, right.
• 38:23
  Speaker 1: I don't know, it was it was astonishing to see
• 38:26
  Speaker 1: how quickly people flipped from that. Um, let me go
• 38:30
  Speaker 1: through some more of my questions. We went over, Oh,
• 38:34
  Speaker 1: I have something that I've been dying to ask you.
• 38:36
  Speaker 1: I love this quote of yours. The efficient market model
• 38:40
  Speaker 1: a model and an analogy, but not a valid theory.
• 38:45
  Speaker 1: How is that not a valid theory. Let's hold aside
• 38:49
  Speaker 1: whether or not the efficient market hypothesis is accurate, but
• 38:53
  Speaker 1: why isn't it a theory, it's not a theory because
• 38:57
  Speaker 1: it doesn't describe the way things really behave. If I
• 39:00
  Speaker 1: can give you an example of I had a sort
• 39:03
  Speaker 1: of anecdot to tell you what I meant by theory
• 39:05
  Speaker 1: in a corny sort of way. When my son was
• 39:07
  Speaker 1: very smaller, used to put him my knee and play
• 39:09
  Speaker 1: with him and bounce him up and down. And my
• 39:11
  Speaker 1: sister used to do it to me. Say half a
• 39:13
  Speaker 1: pound of tupanny rice, half a pound of treacle, mix
• 39:15
  Speaker 1: them up and make them last. Pop goes the weasel.
• 39:17
  Speaker 1: It's an old English thing. And then you drop your
• 39:19
  Speaker 1: knees and the kid drops down to the floor and
• 39:22
  Speaker 1: he chortles with glee. And then he said again, and
• 39:25
  Speaker 1: I did it again, and then he said again, and
• 39:26
  Speaker 1: I did again. When I did it the fifteenth time,
• 39:29
  Speaker 1: when I dropped him down, he said to me, why
• 39:31
  Speaker 1: it's not funny anymore. And so there's no explanation of
• 39:36
  Speaker 1: why it's not funny anymore. The fact is, if you
• 39:38
  Speaker 1: tell the drug fifteen times, it's not funny anymore. And
• 39:41
  Speaker 1: that's both the fact and the theory. It's true, and
• 39:45
  Speaker 1: there's no explanation for it. That's just so I was
• 39:47
  Speaker 1: trying to say earlier about GOODA. When you describe something
• 39:49
  Speaker 1: that's true, you can't say why is it true? You
• 39:52
  Speaker 1: just say it's true, and the words are the theory
• 39:55
  Speaker 1: and the fact is that that's the way it behaves.
• 39:57
  Speaker 1: The efficient market model is not like that, Um, markets
• 40:01
  Speaker 1: are inefficient. Um. It's a model. It's not a description
• 40:05
  Speaker 1: of the way the world actually is. The world could
• 40:07
  Speaker 1: be that way, but it isn't. So so I'm fond
• 40:10
  Speaker 1: of saying that markets are kind of sort of eventually
• 40:15
  Speaker 1: somewhat efficient, yes, in the longer, in the long run,
• 40:18
  Speaker 1: more or less, I liked I used to like to
• 40:20
  Speaker 1: say that the efficient market hypothesis and model was a
• 40:23
  Speaker 1: very clever jiu jitsu trick by economists who couldn't predict
• 40:27
  Speaker 1: what was going to happen, and instead of giving up
• 40:29
  Speaker 1: society to make that a principle, so turning sort of
• 40:31
  Speaker 1: weakness into strength. That's that's interesting, and and it is true.
• 40:35
  Speaker 1: Everybody who believes in the efficient market hypothesis also believe
• 40:39
  Speaker 1: that in the random walk, we don't know what's going
• 40:42
  Speaker 1: to happen. So therefore by low cost indexes and rebalance
• 40:46
  Speaker 1: on a regular basis, And that's good. As long as
• 40:48
  Speaker 1: not everybody does that, you still need somebody out there
• 40:51
  Speaker 1: looking for value otherwise. Otherwise, Yeah, as much as money
• 40:55
  Speaker 1: has been flowing to places like Black Rock, I Shares
• 40:59
  Speaker 1: and and Guard, it's still a tiny percentage of the
• 41:03
  Speaker 1: overall investment. What is the three trillion dollars in indicries
• 41:06
  Speaker 1: now three trillion dollars in ETFs area that today not
• 41:10
  Speaker 1: a whole lot of money relative to what's the total
• 41:13
  Speaker 1: investable universe sixty eight or seventy five trillion dollars worldwide? Yeah,
• 41:19
  Speaker 1: I think so, yeah, that maybe trillion. I think the
• 41:23
  Speaker 1: US equity markets are are are about that, the bond
• 41:28
  Speaker 1: markets are more, and then you have the rest of
• 41:30
  Speaker 1: the world. At one point in time, US was almost
• 41:33
  Speaker 1: half of the investible assets and in the globe that
• 41:35
  Speaker 1: has since since changed. So so I think a lot
• 41:39
  Speaker 1: of models work well. Related to what you were saying,
• 41:42
  Speaker 1: they work well as long as you're just a rupple
• 41:44
  Speaker 1: on the sea. But when you become the whole, see,
• 41:47
  Speaker 1: then the model right, it's it's you. You no longer
• 41:52
  Speaker 1: trading in the market, you are the market, and that's
• 41:54
  Speaker 1: a very different um. I love this line. Explain for
• 42:00
  Speaker 1: us what is the unbearable futility of modeling. Oh is
• 42:04
  Speaker 1: that a line of mine? Yes? Okay, Um, well, I'm
• 42:08
  Speaker 1: not sure where that comes from, but I believe it's
• 42:11
  Speaker 1: a chapter or sub chapter heading in towards the ending. Um.
• 42:20
  Speaker 1: I'm not sure what I meant by that, to be honest,
• 42:21
  Speaker 1: I think I probably just meant what I said before
• 42:23
  Speaker 1: that in the end, you will always be wrong, and
• 42:27
  Speaker 1: you will you will never you will never be you know,
• 42:29
  Speaker 1: if if you it's what you were saying earlier. If
• 42:31
  Speaker 1: you find some law of nature, it kind of holds forever,
• 42:33
  Speaker 1: and if you find a model, it's inevitably going to
• 42:37
  Speaker 1: going to require revamping, changing alteration as people start to
• 42:42
  Speaker 1: change your behavior. Do you miss physics at all, given
• 42:46
  Speaker 1: that the certitude that you get in physics versus always
• 42:50
  Speaker 1: dealing with uncertainty about what's happening and about how long
• 42:54
  Speaker 1: a model is going to have a Literally, No, I don't.
• 42:58
  Speaker 1: I'm very glad I got a background in physics, but
• 43:00
  Speaker 1: I'm a most at this when glad I got out
• 43:01
  Speaker 1: of it. It's like a rough life being in physics
• 43:03
  Speaker 1: because there's so many incredibly smart people that you actually
• 43:06
  Speaker 1: run into people and you say to yourself. I can,
• 43:09
  Speaker 1: I can sort of understand what they're doing, but I
• 43:11
  Speaker 1: could never have done that myself. So it's actually a
• 43:14
  Speaker 1: bit of a relish. So you come to finance, where
• 43:18
  Speaker 1: essentially it's easy to be the smart I'm not, I'm
• 43:22
  Speaker 1: reasonably smart, but but finance also Actually, physics and finance
• 43:26
  Speaker 1: theres lots of room for taste, I think, and where
• 43:28
  Speaker 1: people go wrong is when they don't have taste and
• 43:30
  Speaker 1: they start slavishly sort of following the mathematics too much.
• 43:33
  Speaker 1: Physics is really driven by ideas and then mathematics to
• 43:37
  Speaker 1: implement them, and I think it should be that way
• 43:39
  Speaker 1: in finance too. I'm gonna push back at you know
• 43:41
  Speaker 1: a little bit, because I think you were a grad
• 43:44
  Speaker 1: student and you wrote a paper on it wasn't Higgs, Boston,
• 43:47
  Speaker 1: but maybe it was one of the Higgs particles that
• 43:50
  Speaker 1: became widely um, you know, widely regarded, And to do
• 43:55
  Speaker 1: that in physics as a grad student is no easy thing. No. Yeah,
• 43:59
  Speaker 1: I wrote a thesis on writing tests for how to
• 44:02
  Speaker 1: find a w boson that was cause don't ask me
• 44:05
  Speaker 1: where I pull that from than Wikipedia, but that's true.
• 44:09
  Speaker 1: And then they eventually discovered that although I was. Yeah,
• 44:12
  Speaker 1: now I was. I was a reasonably good physicist. But nevertheless,
• 44:15
  Speaker 1: and maybe in finance toopid. Let's say, in physics you
• 44:17
  Speaker 1: run into people I heard Fineman talk or people like that,
• 44:20
  Speaker 1: or Richard Fineman over in California was at cal Tech. Yeah,
• 44:26
  Speaker 1: and so did that Was that intimidating or impressive? It
• 44:30
  Speaker 1: was kind of intimidating or even I sort of wrote
• 44:34
  Speaker 1: in My Life as a Quantuent, my memoir, I wrote,
• 44:36
  Speaker 1: sort of, you see your ambitions slowly getting degraded. When
• 44:39
  Speaker 1: you're like eighteen, you want to be like Einstein, and
• 44:42
  Speaker 1: when you're like twenty five, you want to be like
• 44:44
  Speaker 1: the professor that you know in your department. And then
• 44:46
  Speaker 1: when you're like thirty two, you say, gee, the guy
• 44:48
  Speaker 1: at the desk next to me is getting more invitations
• 44:50
  Speaker 1: to give seminars than me. And then you suddenly think, well,
• 44:54
  Speaker 1: look where I've got to. So it's that competitive, and
• 44:57
  Speaker 1: it's that it is competitive. I got a job as
• 45:01
  Speaker 1: a particle physicist at Boulder in nine was my last
• 45:04
  Speaker 1: job in physics, and they were like a hundred and
• 45:06
  Speaker 1: forty people applying for one job. It was really rough.
• 45:10
  Speaker 1: I mean it was after the Vietnam War, there was
• 45:11
  Speaker 1: no money for research anymore. All the physics jobs that
• 45:15
  Speaker 1: filled up when there was still a lot of money
• 45:16
  Speaker 1: before the head Field govern Amendment which stopped the money
• 45:19
  Speaker 1: going to research. And I was maybe number four on
• 45:23
  Speaker 1: the list or something in the first three either had
• 45:26
  Speaker 1: had dual career problems or got better jobs and I
• 45:29
  Speaker 1: got the job. But but um, yeah, it was kind
• 45:32
  Speaker 1: of it was kind of discouraging. So what about today
• 45:36
  Speaker 1: for financial engineers? You mentioned there's lots of quants around.
• 45:40
  Speaker 1: Are there are there still opportunities for people who want
• 45:42
  Speaker 1: to go into this field and and apply mathematics to finance. Yeah,
• 45:47
  Speaker 1: I think there are. There's a much bigger market for
• 45:49
  Speaker 1: risk management now. Some of it is PR. People just
• 45:52
  Speaker 1: want to say they have risk management, but sometimes they
• 45:54
  Speaker 1: have real risk management. And ye know what I liked
• 45:56
  Speaker 1: about if I talk pass what I liked about getting
• 45:59
  Speaker 1: to Goldman as opposed to in physics. I like physics,
• 46:01
  Speaker 1: but in physics someone had the feeling like you had
• 46:03
  Speaker 1: to be really superb or otherwise you were wasting your
• 46:06
  Speaker 1: life because you spent all your time. I was doing
• 46:08
  Speaker 1: theoretical physics singing in an office and banging your head
• 46:11
  Speaker 1: against the wall trying to solve something difficult, and you
• 46:13
  Speaker 1: could spend half your time sort of depressed. It was
• 46:16
  Speaker 1: nice about being a Goldman. What I actually liked about
• 46:19
  Speaker 1: finance was it was a more it was a more
• 46:21
  Speaker 1: multi sided world where you spend part of your time
• 46:23
  Speaker 1: doing theory, but part of your time writing programs, and
• 46:26
  Speaker 1: part of your time interacting with clients and with traders,
• 46:29
  Speaker 1: and so there were many more sources of satisfaction. You
• 46:31
  Speaker 1: had long term projects and short term projects, and I
• 46:34
  Speaker 1: found that very satisfying. I got quite excited when I
• 46:36
  Speaker 1: went to it goes. So, after almost twenty years on
• 46:38
  Speaker 1: Wall Street, you transition to being a professor. How do
• 46:40
  Speaker 1: you How do you like teaching? How do you like
• 46:42
  Speaker 1: working with with young people? I like it, um I
• 46:47
  Speaker 1: would say it's not as exciting as as being on
• 46:49
  Speaker 1: Wall Street. People in universities are sort of siloed, and
• 46:52
  Speaker 1: that everybody is there because they want to get on
• 46:54
  Speaker 1: with their research and have students, and so I would
• 46:57
  Speaker 1: say in a sense, being at Goldman was more collegial
• 47:01
  Speaker 1: in being in a university for me, despite the university
• 47:03
  Speaker 1: being a college, because actually everybody wants to sort of
• 47:06
  Speaker 1: I'm exaggerating a little bit, but leave me alone. I
• 47:08
  Speaker 1: have work to do. Was it, Goldmen? You could go
• 47:10
  Speaker 1: to somebody and say, I'm thinking about this problem. Can
• 47:13
  Speaker 1: you help me? And because you're all paid to pull
• 47:16
  Speaker 1: in the same direction, they sort of helped you more willingly.
• 47:18
  Speaker 1: I would say, so a little more of a team
• 47:20
  Speaker 1: effort on Wall Street than in a university where everybody
• 47:25
  Speaker 1: has a list of things to do and students to
• 47:28
  Speaker 1: deal with and mid terms and papers exactly and their tenures.
• 47:31
  Speaker 1: So go away, I'm busy right that that that's fascinating. Um,
• 47:36
  Speaker 1: how has quantitative investing evolved since you began in the
• 47:41
  Speaker 1: industry all those years ago? Um? Well, first of all,
• 47:47
  Speaker 1: everybody on trading disks is, as I said, simple times before,
• 47:50
  Speaker 1: much more numerous. Now a lot of them can write
• 47:52
  Speaker 1: their own models. A lot of them studied math, especially
• 47:55
  Speaker 1: in derivatives. You get a lot of French or or
• 47:58
  Speaker 1: even American students who have all gotten advant degrees or
• 48:00
  Speaker 1: PhDs in finance, So that's become much more complicated. The
• 48:04
  Speaker 1: second thing that's changed a lot is is electronic price
• 48:07
  Speaker 1: feeds and electronic settlements. So suddenly computers have become much
• 48:11
  Speaker 1: more important. Jeff Gunlock tells the story about his one
• 48:16
  Speaker 1: of his first jobs on a bond desk. He shows
• 48:19
  Speaker 1: up with a math background and is able to do
• 48:21
  Speaker 1: some really basic calculations in his head, and everybody there
• 48:26
  Speaker 1: thinks he's a wizard, because they're not math guys yet
• 48:30
  Speaker 1: they're on a bond desk. Thirty years ago. Yeah, I
• 48:33
  Speaker 1: started out on a bond desk, and actually along the
• 48:35
  Speaker 1: lines of what you're saying, I worked on this model
• 48:38
  Speaker 1: which you mentioned, the Black Derman toy model. But I
• 48:40
  Speaker 1: think one of the things that almost as much impact
• 48:43
  Speaker 1: was that I could program well in a in a
• 48:45
  Speaker 1: in a world where people couldn't, and I built a
• 48:47
  Speaker 1: user interface that let the traders enter trade, save it,
• 48:51
  Speaker 1: think about it, come back the next day, and talk
• 48:53
  Speaker 1: the salespeople talk to a client again. And user interface
• 48:57
  Speaker 1: makes a lot of difference. So in those days, if
• 48:59
  Speaker 1: you could act really in those days, you couldn't get
• 49:02
  Speaker 1: something to do it for you. If you could do
• 49:03
  Speaker 1: it yourself, you could be that much more effective if
• 49:05
  Speaker 1: you knew the theory and the programming. That's a that's
• 49:08
  Speaker 1: quite interesting. So that was leads to what was the
• 49:12
  Speaker 1: first model you built at Goldman it was the some
• 49:15
  Speaker 1: dormantory model for interest rates. It was a model for
• 49:19
  Speaker 1: options on treasury bonds, options on interest rates. Goldman was
• 49:22
  Speaker 1: doing a lot of business. People were beginning to extend
• 49:25
  Speaker 1: black shoals, which is for options on stock, options on bonds.
• 49:29
  Speaker 1: And it's actually quite a complicated problem to do consistently.
• 49:32
  Speaker 1: And I can imagine, yeah, because bonds bonds are all
• 49:35
  Speaker 1: you can pretend apple and Apple and Walmart have nothing
• 49:38
  Speaker 1: to do with each other and write an option model
• 49:40
  Speaker 1: for each. But you can't pretend to five year bond
• 49:43
  Speaker 1: and a three year bond have nothing to do with
• 49:44
  Speaker 1: each other because the five year bond will become a
• 49:46
  Speaker 1: three year bond. Yeah, And so you have to really
• 49:49
  Speaker 1: many more constraints on building the model. So what is
• 49:53
  Speaker 1: it that people misunderstand about quants and model in finance?
• 49:57
  Speaker 1: The lay person has an idea year about a person
• 50:01
  Speaker 1: sitting in front of a computer, But what do the
• 50:04
  Speaker 1: average what does the average person not know about what's
• 50:08
  Speaker 1: happening behind the scenes that is telling us to as
• 50:13
  Speaker 1: to just the perspective of plants in finance. You know,
• 50:19
  Speaker 1: I think people who academics or other people haven't worked
• 50:22
  Speaker 1: with trading desks. You imagine that you're making predictions all
• 50:25
  Speaker 1: the time, and I think the truth is, most of
• 50:28
  Speaker 1: the time, certainly derivatives, you're not making predictions about the future.
• 50:32
  Speaker 1: You're trying to figure out in the present what's costs
• 50:35
  Speaker 1: too much and what what's too rich and what's too dear.
• 50:38
  Speaker 1: And your models are much more good to saying this
• 50:40
  Speaker 1: is more expensive than it should be, and this is
• 50:42
  Speaker 1: richer than it should be, And then the prediction is, Okay,
• 50:46
  Speaker 1: the rich things will become cheaper and the cheap things
• 50:48
  Speaker 1: will become richer. But most of your time is spent
• 50:51
  Speaker 1: trying to at least most of my time, we've spent
• 50:53
  Speaker 1: trying to figure out how to tell the price of
• 50:57
  Speaker 1: something liquid from a lot of liquid things. So, for example,
• 51:01
  Speaker 1: if you want to buy an option, how you figure
• 51:03
  Speaker 1: out the option price from the stock on the bond price,
• 51:05
  Speaker 1: which are both liquid and so the example I like
• 51:08
  Speaker 1: to give in a way it's also a metaphor, is
• 51:11
  Speaker 1: if a lot of the problems you faced with is
• 51:13
  Speaker 1: not what will happen to the price of apples in
• 51:15
  Speaker 1: the future, if you're dealing with with with fruit, but
• 51:19
  Speaker 1: what should a pay for fruit salad? Given the price
• 51:21
  Speaker 1: of apples, pears and peaches, and and so you say,
• 51:25
  Speaker 1: what's the cost of canning? What's the cost of buying
• 51:27
  Speaker 1: the apples, pears and peaches, unless should be the price
• 51:29
  Speaker 1: of fruit salad. And then sometimes you do the reverse,
• 51:32
  Speaker 1: you say, I know the price of fruit salad, I
• 51:34
  Speaker 1: know the price of apples and oranges, so what's the
• 51:36
  Speaker 1: right price for pears. But it's always trying to figure
• 51:39
  Speaker 1: out in the present how to get the liquid thing,
• 51:42
  Speaker 1: given the price of the liquid things, how to get
• 51:44
  Speaker 1: the value of the liquid things. Am I making sense? Yeah? No,
• 51:47
  Speaker 1: that's that's quite fascinating. You're absolutely less prediction than trying
• 51:51
  Speaker 1: to figure out what the current price of something should be.
• 51:54
  Speaker 1: And very often there's incomplete information. If if something is
• 51:57
  Speaker 1: liquid and trading, well, when then we know what the
• 51:59
  Speaker 1: price is. But if something is illiquid doesn't trade a lot,
• 52:03
  Speaker 1: you really don't have a market based price, so you
• 52:06
  Speaker 1: have to come up with a different way to figure
• 52:09
  Speaker 1: out what you should or shouldn't pay for this. Is
• 52:11
  Speaker 1: that a fair yes? And the prediction comes less in
• 52:15
  Speaker 1: predicting what will happen, then in saying this is cheap.
• 52:18
  Speaker 1: And eventually I think my model says this is cheap,
• 52:20
  Speaker 1: and so eventually it will come to what I think
• 52:22
  Speaker 1: is fair of value. Everything comes back down to reversion
• 52:25
  Speaker 1: to the means that's fair, I think. Um, So what
• 52:33
  Speaker 1: other quants do you admire? What? What quants have moved
• 52:37
  Speaker 1: the industry forward? I think Fisher Black most of all.
• 52:41
  Speaker 1: I wrote a lot about him in the book that
• 52:43
  Speaker 1: I wrote in my memoir My Life is a quant
• 52:45
  Speaker 1: because I think he was kind of an exceptional guy,
• 52:48
  Speaker 1: both from a character difficult guy, but exceptional from a
• 52:51
  Speaker 1: character point of view, and that he liked to tackle
• 52:53
  Speaker 1: everything ab initio. You know, he would from scratch, from
• 52:57
  Speaker 1: right from start, right from the start, somebody who was
• 52:59
  Speaker 1: totally unknown could come to him and send him an
• 53:01
  Speaker 1: email about something, and he would think about it, you know,
• 53:03
  Speaker 1: without any prejudice. And he thought about everything in his
• 53:06
  Speaker 1: own way. So I kind of admire him. Um. I
• 53:09
  Speaker 1: kind of admired Steve Ross and Mark Rubinstein. They were
• 53:13
  Speaker 1: more PhD academics, but for for their setting out the
• 53:17
  Speaker 1: whole basis of them of option pricing, which I spent
• 53:19
  Speaker 1: most of my life on. I kind of admired Paul Wilmot,
• 53:22
  Speaker 1: who I once wrote a paper with. We actually wrote
• 53:25
  Speaker 1: based on the based on the on the financial crisis,
• 53:29
  Speaker 1: we wrote a financial model as manifesto. It was a
• 53:31
  Speaker 1: bit of a joke, I recall. I recall that now
• 53:34
  Speaker 1: for people who may not know. Wilmot puts out a
• 53:37
  Speaker 1: Quantitative quarterly Quantitative magazine magazine on really developments Paul Wilmot, right,
• 53:45
  Speaker 1: developments in in the world of quantitative trading and analysis,
• 53:50
  Speaker 1: and it's really high level stuff when when you sift
• 53:54
  Speaker 1: through it um as I on occasion have done, most
• 53:58
  Speaker 1: of it's going to be over the head of the
• 54:00
  Speaker 1: average trader, the average investor, the average person. But within
• 54:04
  Speaker 1: the industry, I have to think that that's become practically
• 54:09
  Speaker 1: the go to bible. Is that. Am I overstating it?
• 54:12
  Speaker 1: Maybe a little bit, but that's one of them. But
• 54:13
  Speaker 1: I sort of admiring because he's written some I mean
• 54:16
  Speaker 1: for that too, but he's written some good textbooks, and
• 54:19
  Speaker 1: I like to think, like me, he tries to make
• 54:21
  Speaker 1: things simpler rather than complexify them. There's been a tendency
• 54:24
  Speaker 1: for finance academics to do everything in a very formal,
• 54:27
  Speaker 1: axiomatic way as of the teaching math, and Paul and
• 54:30
  Speaker 1: myself both like to use the least amount of math
• 54:33
  Speaker 1: possible that that's quite interesting. So let's keep plowing through
• 54:38
  Speaker 1: some of some of these questions here. Um, we talked
• 54:41
  Speaker 1: about the ft, we talked about alpha. What's been the
• 54:46
  Speaker 1: impact of of high frequency trading and al go driven
• 54:50
  Speaker 1: trading strategies on the sort of work that that you do?
• 54:55
  Speaker 1: Very broad if if I talk from a sociological point
• 54:57
  Speaker 1: of view, ill see from the students at Columbia until
• 54:59
  Speaker 1: a few years ago, and they all wanted to take derivatives,
• 55:02
  Speaker 1: and the last year or two everybody's being tempted probably
• 55:05
  Speaker 1: about the job market and by excitement to work on
• 55:08
  Speaker 1: high frequency trading, algorithmic trading. And now for the last
• 55:11
  Speaker 1: year or two machine learning. You know, you're programming machines
• 55:15
  Speaker 1: to to to just grow through all the data that's
• 55:18
  Speaker 1: out there and figure out their own rules. Yes, or
• 55:21
  Speaker 1: even even things like can you scour the internet to
• 55:23
  Speaker 1: find out whether there were a lot of cars in
• 55:26
  Speaker 1: Walmart parking lots the last year, you know, to try
• 55:29
  Speaker 1: to get them get source advice? There? There have been
• 55:32
  Speaker 1: people doing that with Twitter. Can we identify sentiment shifts
• 55:35
  Speaker 1: on Twitter and then generate a tradeable algorithm from that? Yeah,
• 55:41
  Speaker 1: I've noticed that, So a lot of that, A lot
• 55:42
  Speaker 1: of that stuff, some sentiment based, some really objective, like
• 55:46
  Speaker 1: can you figure out in some way how much sales
• 55:48
  Speaker 1: are happening in various places from data you can collect
• 55:51
  Speaker 1: on the internet. Um, what was your question again, us,
• 55:54
  Speaker 1: I have no idea, so I have no recollection. Um,
• 55:59
  Speaker 1: what's the pact of high frequency trading on model development
• 56:04
  Speaker 1: and and model construction and then actually having these models
• 56:09
  Speaker 1: execute in the market if there are theoretically predatory algorithms
• 56:14
  Speaker 1: looking for whatever it is they're trying to do. Yeah,
• 56:17
  Speaker 1: I think if I get a little bit general that
• 56:19
  Speaker 1: there've been sort of two classes of models that people
• 56:22
  Speaker 1: use in finance. One or structural models, where like for derivatives,
• 56:26
  Speaker 1: where you say an option is really a hybrid of
• 56:28
  Speaker 1: a stock in a bond, and I've got to figure
• 56:30
  Speaker 1: out exactly how it works, And that's what black shouls does.
• 56:33
  Speaker 1: So it's really saying like an option is like a
• 56:35
  Speaker 1: molecule made out of atoms and there's a structure there.
• 56:38
  Speaker 1: And those were always what quantum mostly did. But what's happened,
• 56:41
  Speaker 1: as you point out, in the last few years is
• 56:43
  Speaker 1: econometrical statistical models have become much more the rage and
• 56:46
  Speaker 1: students are gaining that direction where you just say, cannot
• 56:49
  Speaker 1: find a regression between um, you know, various factors that
• 56:53
  Speaker 1: that seem to predict the market. I don't care. I
• 56:56
  Speaker 1: care a little bit why, but I care less. Why
• 56:58
  Speaker 1: then finding the actual pattern? And that's become so statistics
• 57:01
  Speaker 1: an econometrics have become much more fashionable as a result
• 57:05
  Speaker 1: of high frequency trading and algorithmic trading. That's interesting why econometrics?
• 57:09
  Speaker 1: How how did that work its way into the sort
• 57:12
  Speaker 1: of modeling. I'm looking for relations between time series, between
• 57:18
  Speaker 1: FED behavior, between interest rates, between UM the behavior effectors
• 57:22
  Speaker 1: and and particular stocks. So let's let's caring about why
• 57:26
  Speaker 1: it's happening, the detecting a pattern. So I see a
• 57:28
  Speaker 1: lot of these sort of correlations, and they always make
• 57:32
  Speaker 1: me and I see people writing about them, and I
• 57:34
  Speaker 1: read articles and I see charts. But the back of
• 57:37
  Speaker 1: my head, I'm always saying, well, yes, but is one
• 57:41
  Speaker 1: thing causing another? Is this a temporary you know? Sometimes
• 57:45
  Speaker 1: they're in sync, and so I can't tell. I can't
• 57:47
  Speaker 1: count how many people have been insisting, well, look the
• 57:50
  Speaker 1: FED cut rates and here's what the market did, and
• 57:52
  Speaker 1: now we have quantitative easing, and here's what the market did.
• 57:55
  Speaker 1: And as soon as this unwinds, the opposite is going
• 57:57
  Speaker 1: to happen. And they run in parallel for long periods
• 58:00
  Speaker 1: of time and then suddenly they just go their own way.
• 58:03
  Speaker 1: How much of a risk is that on the econometric side,
• 58:06
  Speaker 1: that we're gonna be fooled by randomness. We're gonna see
• 58:11
  Speaker 1: a correlation and it works until it stops working. I
• 58:14
  Speaker 1: agree with you totally. I'm I mean, I understand why
• 58:17
  Speaker 1: people do this stuff, but I'm a bit of a
• 58:18
  Speaker 1: skeptic about it myself. I think, UM, just what you said,
• 58:22
  Speaker 1: there's a standard phrase which you are sort of paralleling,
• 58:25
  Speaker 1: saying correlation is not causation, and I think that's true.
• 58:28
  Speaker 1: But but people are so keen to make money for
• 58:31
  Speaker 1: legitimate reasons that that's the easiest thing to do, and
• 58:34
  Speaker 1: sometimes it works. All right, I have to ask you
• 58:37
  Speaker 1: before I get to some of my favorite UM questions.
• 58:41
  Speaker 1: I have to ask you about Spinosa's theory of emotions,
• 58:45
  Speaker 1: which you mentioned in UM models behavior behaving badly, moral
• 58:50
  Speaker 1: concepts of good and evil, virtue, and perspective have a
• 58:53
  Speaker 1: basis in human psychology, which naturally leads to the question,
• 58:58
  Speaker 1: what does this have to do with trading models. Well,
• 59:03
  Speaker 1: Spinoza was like three years ahead of his time. I
• 59:05
  Speaker 1: tried to read The Ethics once when I was writing
• 59:07
  Speaker 1: my book because I was trying to find an example
• 59:10
  Speaker 1: of something that I thought was the theory rather than
• 59:12
  Speaker 1: a model. And what Spinoza did in the Ethics, so
• 59:15
  Speaker 1: that it was only published posthumously, was trying to figure
• 59:20
  Speaker 1: out actually very a lot like behavioral economists, but three
• 59:24
  Speaker 1: years earlier, trying to figure out what drives people to
• 59:26
  Speaker 1: behave the way they do. And his argument was that, um,
• 59:31
  Speaker 1: if you can understand the passions of the emotions, as
• 59:34
  Speaker 1: he calls them, then he'll be able to understand why
• 59:36
  Speaker 1: people are unhappy and then be able to figure out
• 59:39
  Speaker 1: how they should live their lives. And so what he
• 59:41
  Speaker 1: starts out doing is analyzing all the emotions or as
• 59:46
  Speaker 1: he called with the passions that they are. And he
• 59:47
  Speaker 1: calls him passions because they affect you as a passive
• 59:51
  Speaker 1: person rather than an active person. They sweep over you
• 59:53
  Speaker 1: rather than you deciding you would you would want to
• 59:56
  Speaker 1: want to behave that way. And it's actually so it's
• 59:59
  Speaker 1: very interesting and be it's really closely related to derivatives.
• 1:00:02
  Speaker 1: It's sort of astonishing because what he does is he says,
• 1:00:06
  Speaker 1: at the bottom of the chain, there are only three things,
• 1:00:08
  Speaker 1: which is pleasure, pain, and desire. And everybody knows what pleasure,
• 1:00:13
  Speaker 1: pain and desire, probably actually defines it in some philosophical way.
• 1:00:16
  Speaker 1: And then he defines every other emotion, and there are
• 1:00:20
  Speaker 1: hundreds of them verbally in terms of how they reduced
• 1:00:24
  Speaker 1: to pleasure, pain, and desire. So, for example, um, love
• 1:00:28
  Speaker 1: is expectation of pleasure from some other person, and pain
• 1:00:31
  Speaker 1: is hatred is the opposite expectation of pain. That's a
• 1:00:34
  Speaker 1: little bit corny. But then for example, he says, um um,
• 1:00:39
  Speaker 1: so envy, Yeah, envy is the one I wanted. I
• 1:00:41
  Speaker 1: couldn't think, so he says, envy, Um, I have to
• 1:00:45
  Speaker 1: try to remember the pain of absence of pleasure. Yeah,
• 1:00:48
  Speaker 1: And and envy is um wow, it's suddenly slipping me.
• 1:00:51
  Speaker 1: I remember cruelty better. Cruelty. Cruelty is what you call
• 1:00:56
  Speaker 1: the desire of somebody else to inflict pain on someone
• 1:00:59
  Speaker 1: that you love. So cruelty eventually goes down to pleasure, pain,
• 1:01:03
  Speaker 1: and desire. And I like to say it's like a
• 1:01:08
  Speaker 1: it's um it's like a convertible bond that has both
• 1:01:11
  Speaker 1: equity exposure, credit exposure, and fixed income exposure. It goes
• 1:01:14
  Speaker 1: down to all three of the derivatives. He's really got
• 1:01:16
  Speaker 1: to actually do a chart, which is on a giant
• 1:01:19
  Speaker 1: then diagram pleasure, pain and eventually goes down through some
• 1:01:26
  Speaker 1: chain to these three things at the bottom. I wonder
• 1:01:29
  Speaker 1: if there's a graphic of that somewhere. I've got a
• 1:01:31
  Speaker 1: graphic of it. Yeah, it's on my website, and I
• 1:01:33
  Speaker 1: was only going to make a post out of it
• 1:01:35
  Speaker 1: because it's really in fact, I once I once submitted
• 1:01:37
  Speaker 1: that somebody, somebody at the Serpentine Gallery in London had
• 1:01:41
  Speaker 1: a map competition that somebody connected me to and I
• 1:01:44
  Speaker 1: submitted that as a map, and it's in a book
• 1:01:46
  Speaker 1: somewhere maps of all kinds of things. That's fascinating. Um,
• 1:01:52
  Speaker 1: So now let's move over to some of my favorite questions.
• 1:01:56
  Speaker 1: These are the standard questions we ask everybody. So we
• 1:02:01
  Speaker 1: went over how you how you got into the finance. Well,
• 1:02:04
  Speaker 1: actually we really didn't get that question. So you started
• 1:02:08
  Speaker 1: as a physicist, how do you go from bould of
• 1:02:13
  Speaker 1: Colorado studying particle physics? Too? I'm gonna do quantitative work
• 1:02:18
  Speaker 1: on Wall Street. How did that transition actually? You come
• 1:02:23
  Speaker 1: kind of um, not planned. I I got a PhD
• 1:02:27
  Speaker 1: in nineteen seventy three from Colombia. And then, as I said,
• 1:02:30
  Speaker 1: jobs were hard to find, and I had a post
• 1:02:32
  Speaker 1: stock at University of Pennsylvania, and then I had a
• 1:02:34
  Speaker 1: job at Oxford in England. And then I had a
• 1:02:37
  Speaker 1: job at Rockefeller University of New York. And my wife
• 1:02:39
  Speaker 1: was in biology, and we were moving all over the
• 1:02:41
  Speaker 1: world out of sync with each other actually, and then
• 1:02:44
  Speaker 1: when we were in England together we had a kid,
• 1:02:46
  Speaker 1: and and then we both had jobs in New York.
• 1:02:48
  Speaker 1: And then it was very hard to get permanent jobs.
• 1:02:51
  Speaker 1: I got a job in Boulder. She couldn't get a
• 1:02:53
  Speaker 1: job there. I lived there for a while, um, and
• 1:02:55
  Speaker 1: eventually I sort of threw in the towel. It was
• 1:02:57
  Speaker 1: getting too complicated. And then this is nineteen eighty where
• 1:03:01
  Speaker 1: people went then was there was the oil crisis and
• 1:03:04
  Speaker 1: telecom was building up, and you have the people who
• 1:03:07
  Speaker 1: were physicists either went to work for Exxon or Mobile,
• 1:03:11
  Speaker 1: or they went to Bell Labs. And I took a
• 1:03:14
  Speaker 1: job at Bell Labs later became loosen. You were a
• 1:03:16
  Speaker 1: good couple of years. I was there for five years
• 1:03:18
  Speaker 1: and and so then I stopped doing physics and I
• 1:03:22
  Speaker 1: worked in a business analysis center that sort of was
• 1:03:25
  Speaker 1: applying physics techniques or mostly programming to A T and
• 1:03:29
  Speaker 1: T business problems. That's funny because when I saw your
• 1:03:32
  Speaker 1: background and you were at at Bell Labs at Loosen,
• 1:03:36
  Speaker 1: I assumed you were doing some fine physics work as
• 1:03:39
  Speaker 1: an telecom I never for a second said, oh, well,
• 1:03:43
  Speaker 1: he must be working in the in the finances there were.
• 1:03:46
  Speaker 1: I was a sort of retread when I went there,
• 1:03:48
  Speaker 1: and I'm aladd of that lead to Goldman Sachs. Well,
• 1:03:52
  Speaker 1: that was when I went there and I started working
• 1:03:55
  Speaker 1: on actually much more computer program I learned to be
• 1:03:58
  Speaker 1: a good program at the time, and then we were
• 1:04:00
  Speaker 1: tackling sort of problems that A T and T had,
• 1:04:04
  Speaker 1: where you could use sort of computer modeling and financial modeling. UM.
• 1:04:09
  Speaker 1: I didn't like it very much. I learned a lot
• 1:04:11
  Speaker 1: of useful stuff, but I always really hanchred to go
• 1:04:13
  Speaker 1: back to a more academic job. But that was two
• 1:04:15
  Speaker 1: difficult and then all the head and just started knocking
• 1:04:18
  Speaker 1: on the doors of people because interest rates were high,
• 1:04:21
  Speaker 1: and Solomon and Goldman in these places were hiring people.
• 1:04:24
  Speaker 1: And it took me a few years to adjust the idea,
• 1:04:26
  Speaker 1: but I decided I would I would take a leap
• 1:04:30
  Speaker 1: out of out of the whole sphere and into the
• 1:04:33
  Speaker 1: business world. It was sort of a shock for me.
• 1:04:34
  Speaker 1: I never expected to ever do that, and it worked
• 1:04:37
  Speaker 1: out pretty well. Yeah, I got very excited, as I say,
• 1:04:39
  Speaker 1: when I went to Golden and eighty five. It was
• 1:04:41
  Speaker 1: like a shot in the arm for me. And so
• 1:04:43
  Speaker 1: and you were there through a brief period. I was
• 1:04:48
  Speaker 1: at Solomon for one year a little bit more, and
• 1:04:51
  Speaker 1: then in mortgages, and then I went back to Goldman.
• 1:04:53
  Speaker 1: I started in fixing income. I went to Solomon mortgages,
• 1:04:56
  Speaker 1: and then I came back and I was in equity
• 1:04:58
  Speaker 1: to Routers for like ten years, which was really my favorite.
• 1:05:00
  Speaker 1: And it sounds like you thrived and did really well there.
• 1:05:04
  Speaker 1: So let's talk about mentors. Who are some of your
• 1:05:08
  Speaker 1: early mentors? Oh, in physics are in well both? Yeah,
• 1:05:13
  Speaker 1: in physics, there were a couple of professors in South
• 1:05:16
  Speaker 1: Africa that I that I worked for. There was a
• 1:05:18
  Speaker 1: guy called Professor Whiteman who sort of tutored me. It's
• 1:05:21
  Speaker 1: long dead, probably, um And how about in in the
• 1:05:25
  Speaker 1: finance and finance I would say Fisher black the most
• 1:05:28
  Speaker 1: in that I got there and there was still not
• 1:05:31
  Speaker 1: a lot of concert Goldman and and I got involved
• 1:05:35
  Speaker 1: immediately in working for the bond options trading desk, and
• 1:05:38
  Speaker 1: they connected me with Fisher because he was the expert
• 1:05:41
  Speaker 1: and m he really had a very big impact on me.
• 1:05:43
  Speaker 1: So I would say I don't think he I don't
• 1:05:45
  Speaker 1: think he set out to mentor me. He was kind
• 1:05:46
  Speaker 1: of a bit of a cold fish in an ice way.
• 1:05:50
  Speaker 1: But I really learned a lot a lot from him,
• 1:05:53
  Speaker 1: both about perseverance and about not taking models too seriously.
• 1:05:58
  Speaker 1: Quite quite interesting. Um, what investors influenced the way you
• 1:06:03
  Speaker 1: think about modeling or investment? Um, that's a good question.
• 1:06:09
  Speaker 1: I to be honest, I don't invest that much. I'm
• 1:06:11
  Speaker 1: a I'm a E T F mutual fund guy. In
• 1:06:14
  Speaker 1: the old days E d F Now UM. For a
• 1:06:17
  Speaker 1: long time when I worked at Goldman and I recently
• 1:06:19
  Speaker 1: worked at a fund of funds, I wasn't allowed to
• 1:06:21
  Speaker 1: buy individual stocks. Um. So so I'm so you're an
• 1:06:25
  Speaker 1: index sir. I'm an index a pretty much. And by
• 1:06:28
  Speaker 1: the way, that is not uncommon amongst academics who say
• 1:06:32
  Speaker 1: I don't have the interest, the time, the effort. I
• 1:06:34
  Speaker 1: don't want to babysit a bunch of stocks on the
• 1:06:38
  Speaker 1: possibility about performing. Let me just go. I'm kind of
• 1:06:41
  Speaker 1: I'm kind of like that. I like it intellectually, but
• 1:06:43
  Speaker 1: I don't and I like following the markets. But um,
• 1:06:45
  Speaker 1: I have at times board options years ago when I
• 1:06:47
  Speaker 1: was allowed to, but I don't really do that anymore. Uh,
• 1:06:51
  Speaker 1: let's talk about books. Always like Peter Lynch, interesting guy, right,
• 1:06:56
  Speaker 1: fascinating and I was fascinated. But yeah, a quick digression.
• 1:07:01
  Speaker 1: I always thought Peter Lynch and the idea that when
• 1:07:05
  Speaker 1: you're out looking at things on your own and you
• 1:07:08
  Speaker 1: discover stuff. I don't know if that still exists anymore. Yeah,
• 1:07:13
  Speaker 1: I kind of like that a few times. I used
• 1:07:15
  Speaker 1: to do that. I remember noticing with my kids years
• 1:07:18
  Speaker 1: ago that all of the all of all of my
• 1:07:21
  Speaker 1: kids friends mothers were wearing rebox shoes before I've ever
• 1:07:23
  Speaker 1: heard of Reebok when they were doing aerobics, and they
• 1:07:26
  Speaker 1: were all drinking clearly Canadian at some point. He's all
• 1:07:29
  Speaker 1: like twenty five years ago. And I remember going into
• 1:07:32
  Speaker 1: Lulu Lemon a few years ago and being amazed by
• 1:07:35
  Speaker 1: the good running gear they had and seeing people drink
• 1:07:38
  Speaker 1: them drink what do you call it? Curry Green mountains.
• 1:07:41
  Speaker 1: So I still like the idea that you that you
• 1:07:43
  Speaker 1: spot something that you like, or I was like that
• 1:07:45
  Speaker 1: about Apple actually, where you just love the product and
• 1:07:47
  Speaker 1: you say I'm going to go with it without reading
• 1:07:50
  Speaker 1: about EBIT, orbit, d A or anything like that. The
• 1:07:53
  Speaker 1: question is given, how sophisticated technology and biotech and and
• 1:08:01
  Speaker 1: all sorts of things that require an expertise everything you've
• 1:08:05
  Speaker 1: described as either a consumer product in fact they're so
• 1:08:11
  Speaker 1: the does the Peter Lynch approach is it's still valid
• 1:08:15
  Speaker 1: given that so much on the market is not related
• 1:08:19
  Speaker 1: to retail or consumer spending. It's like, stop and thinking
• 1:08:22
  Speaker 1: about you're not doing biotech, you're not doing pharma, you're
• 1:08:25
  Speaker 1: not doing almost all these different software, hardware, networking technology companies.
• 1:08:32
  Speaker 1: The I wonder if Peter Lynch is of an era,
• 1:08:36
  Speaker 1: and maybe that era no longer exists. I'm inclined to
• 1:08:39
  Speaker 1: agree with you. I have friends who went into money
• 1:08:42
  Speaker 1: management after they left Golden into their own money management firms,
• 1:08:45
  Speaker 1: and occasionally one of them says to me, well, this
• 1:08:48
  Speaker 1: has been a really hard time to be a money
• 1:08:50
  Speaker 1: manager for the last fifteen years, and then I think, well,
• 1:08:53
  Speaker 1: kind of, what are you getting paid for? Right? But
• 1:08:57
  Speaker 1: how do you It's hard to do better than other people,
• 1:08:59
  Speaker 1: and you know, he sort of imagines maybe that the
• 1:09:02
  Speaker 1: world should keep prices should keep going up, and he
• 1:09:05
  Speaker 1: should get paid. Well, that's that's you know, if you're
• 1:09:09
  Speaker 1: just buying a trend, you might as well just do
• 1:09:11
  Speaker 1: the t f my Peter Lynch experiences. I moved out
• 1:09:15
  Speaker 1: of the city to the suburbs. It's like fifteen years ago,
• 1:09:18
  Speaker 1: maybe even a little. It was before nine eleven, so
• 1:09:21
  Speaker 1: it was just before two thousand and one, and I
• 1:09:24
  Speaker 1: discovered this new company that nobody had ever heard of
• 1:09:27
  Speaker 1: called home Depot, and I'm like, wow, this place is amazing.
• 1:09:30
  Speaker 1: They have everything. A yeah, we're fixing up the house.
• 1:09:35
  Speaker 1: Is the first thing I do is like I have
• 1:09:38
  Speaker 1: to buy some of this company. And I punched it
• 1:09:41
  Speaker 1: up in a on the on the Bloomberg and it's
• 1:09:44
  Speaker 1: just done nothing but go up for like fifteen years.
• 1:09:47
  Speaker 1: I'm like the last person on the planet who discovered
• 1:09:50
  Speaker 1: home Depot. So that was my like, oh, I guess
• 1:09:53
  Speaker 1: I'm a little late to the Peter Lynch party with
• 1:09:56
  Speaker 1: with Home with Home Depot. I know you like books,
• 1:10:00
  Speaker 1: and I know you like philosophers, so I have to ask,
• 1:10:03
  Speaker 1: what are some of your favorite books? Okay, fiction, nonfiction,
• 1:10:07
  Speaker 1: finance related, non it doesn't matter. Fiction, I like sort
• 1:10:11
  Speaker 1: of Um, I like good romantic books. So I like
• 1:10:13
  Speaker 1: Anna Karenina and I like Madame Bovary. Those are two
• 1:10:17
  Speaker 1: of my favorite and low leader I have to say,
• 1:10:18
  Speaker 1: so that's similar theme people people obsessively, obsessively in love.
• 1:10:25
  Speaker 1: It's always a good story. Um. Nonfiction, Yeah, I like history,
• 1:10:30
  Speaker 1: although I kind of like I have a hard time
• 1:10:32
  Speaker 1: suddenly thinking of things, but I sort of like good
• 1:10:34
  Speaker 1: I read a lot of modern I read mostly a
• 1:10:37
  Speaker 1: lot of modern fiction. But um, but nonfiction I like.
• 1:10:41
  Speaker 1: I like some philosophy. I like Schopenhauer. I like him.
• 1:10:44
  Speaker 1: I like Chopin out the most because he's kind of
• 1:10:46
  Speaker 1: cynical in a very real several times. So what what's
• 1:10:50
  Speaker 1: your favorite work of Chopin. There's a collection that I
• 1:10:52
  Speaker 1: have called him um Essays and Aphorisms. It's a thin
• 1:10:56
  Speaker 1: Penguin book with a lot of essays about everything from
• 1:10:59
  Speaker 1: getting old to wisdom to how you should not read
• 1:11:02
  Speaker 1: until you've until you've exhausted thinking. And I'm very good
• 1:11:07
  Speaker 1: and very very beautifully written, a little bit like Freud,
• 1:11:09
  Speaker 1: like you could get a Nobel Prize just for just
• 1:11:11
  Speaker 1: for his writing style. Really that there's a thin penguin.
• 1:11:15
  Speaker 1: I've got it for years. But it's called essays and
• 1:11:16
  Speaker 1: effort and really funny. Oh really yeah, I mean in
• 1:11:20
  Speaker 1: a in a in a in a slightly bitter sort
• 1:11:22
  Speaker 1: of way, but very sharp. So you strike me as
• 1:11:25
  Speaker 1: someone who would read Fineman, who is brilliant and a
• 1:11:28
  Speaker 1: serbic and funny all at the same time. Yes, I
• 1:11:32
  Speaker 1: I studied the Fineman lectures and I actually actually shook
• 1:11:35
  Speaker 1: his hand once in a men's room at a conference
• 1:11:37
  Speaker 1: in not shook his hand. I spoke to him in
• 1:11:40
  Speaker 1: in nineteen. But he was a phenomenal guy. I heard
• 1:11:45
  Speaker 1: him speak several times. The essays is supposed to be
• 1:11:48
  Speaker 1: the lectures are supposed to be phenomenal. I got a
• 1:11:51
  Speaker 1: birthday gift of the read books. No, I got someone gave.
• 1:11:55
  Speaker 1: Actually it was a gift to somebody else that they
• 1:11:57
  Speaker 1: re gifted to me. It was all the c D
• 1:12:00
  Speaker 1: ease of the and and they were all none of
• 1:12:03
  Speaker 1: them worked. It was I don't know what the heck
• 1:12:06
  Speaker 1: to do. I have like twelve CDs. They're essentially paperweights,
• 1:12:09
  Speaker 1: but it's like the whole box set and I don't
• 1:12:11
  Speaker 1: know what was DVD or it was because it was
• 1:12:15
  Speaker 1: done in the sixties and it's um. I'm sure I could.
• 1:12:18
  Speaker 1: I sure I could pick up another set somewhere, but
• 1:12:20
  Speaker 1: it's sitting somewhere in my basement in a box. The
• 1:12:23
  Speaker 1: Fineman Lectures all cd s, they're all completely but don't
• 1:12:27
  Speaker 1: read much physics anymore. I have them. I like to
• 1:12:30
  Speaker 1: keep up in a popular way, but I don't. I
• 1:12:32
  Speaker 1: don't read anything. It's a handful of of astrophysics blogs.
• 1:12:35
  Speaker 1: I still track. Phil Plate is a guy whose Twitter
• 1:12:39
  Speaker 1: handle is bad Astronomer, and he writes some really interesting stuff.
• 1:12:44
  Speaker 1: He can bring, um, some really complex things to a
• 1:12:50
  Speaker 1: to an understandable At this point, I'm a lay person,
• 1:12:53
  Speaker 1: not a you know, not able to keep up with
• 1:12:56
  Speaker 1: any of the high mass. But the ongoing advancements in
• 1:13:01
  Speaker 1: physics are just phenomenal, most recently the gravitational waves. And
• 1:13:06
  Speaker 1: it's just of all the sciences that are eventually going
• 1:13:10
  Speaker 1: to crack the secrets to the universe, it looks like
• 1:13:12
  Speaker 1: physics is way out ahead of everybody. Yeah, it's astonishing
• 1:13:16
  Speaker 1: if you if you sat out in physics, you kind
• 1:13:18
  Speaker 1: of get spoiled for everything else. That's that's a good
• 1:13:20
  Speaker 1: way to and it's it's nice to sit back and say, well,
• 1:13:23
  Speaker 1: I'm glad I don't have double labs anymore. But it's
• 1:13:26
  Speaker 1: interesting to watch this and and it almost seems that
• 1:13:30
  Speaker 1: the pace of new knowledge is accelerating. Look, we just
• 1:13:36
  Speaker 1: landed on that comet last year. These things were hard
• 1:13:40
  Speaker 1: to even imagine outside of science fiction a decade ago.
• 1:13:44
  Speaker 1: And it's just it's really to me that stuff is
• 1:13:47
  Speaker 1: is utterly fascinating. Yeah, I almost have a slightly religious
• 1:13:50
  Speaker 1: feeling about it. I'm not religious, but you think about
• 1:13:53
  Speaker 1: this gravitational way thing, and you say, okay, it took
• 1:13:56
  Speaker 1: a hundred years for a thousand people to verify the
• 1:13:59
  Speaker 1: prediction of one guy years ago, and the fact that
• 1:14:04
  Speaker 1: somebody could figure out the way the universe works just
• 1:14:07
  Speaker 1: by pure thought. It's sort of an intuition which he
• 1:14:09
  Speaker 1: then elaborates into a model. It's just sort of yeah,
• 1:14:12
  Speaker 1: it's enough to make your believerable. There's there's a fascinating book.
• 1:14:16
  Speaker 1: As long as we're talking about this, So um, there's
• 1:14:21
  Speaker 1: a and now I'm drawing a blank on the name.
• 1:14:24
  Speaker 1: There's a very famous physicist who asked about why we've
• 1:14:30
  Speaker 1: never come across life elsewhere where? Is a firm's paradox.
• 1:14:35
  Speaker 1: Where is everybody? So if if you have you have
• 1:14:39
  Speaker 1: two billion stars in in a galaxy, there are billions
• 1:14:44
  Speaker 1: and billions of galaxies, how is it possible that we've
• 1:14:47
  Speaker 1: never come across any other intelligent life anywhere else? And
• 1:14:52
  Speaker 1: some interesting biologists and physicists put together what they call
• 1:14:57
  Speaker 1: the rare Earth thesis, which is essentially, life is common
• 1:15:01
  Speaker 1: on the planet butte in the universe, but intelligent life
• 1:15:06
  Speaker 1: is relatively rare because the universe turns out to be
• 1:15:09
  Speaker 1: a very hostile place. So so that gravitational wave that
• 1:15:14
  Speaker 1: hit us was relatively modest. But there are there are magnetars,
• 1:15:18
  Speaker 1: and there are all sorts of pulsars, and if you're
• 1:15:23
  Speaker 1: even like a few hundred light years away, the gamma
• 1:15:27
  Speaker 1: waves and the radiations that wash over essentially sterilize the
• 1:15:31
  Speaker 1: planet of all life. It eventually comes back, but intelligent
• 1:15:35
  Speaker 1: life has needs a lot of stability for a long time.
• 1:15:38
  Speaker 1: I find that sort of stuff endlessly fascinated to and
• 1:15:42
  Speaker 1: the parallels to finance are very much there if you
• 1:15:45
  Speaker 1: sit back and think about it a little bit. That's
• 1:15:48
  Speaker 1: really I think I've seen a vision of this that's
• 1:15:50
  Speaker 1: really interesting, and I think I've only read about descriptively,
• 1:15:52
  Speaker 1: but where they say you might get intelligent life, but
• 1:15:55
  Speaker 1: soon or later they destroy themselves and they unable to
• 1:15:57
  Speaker 1: communicate with the rest of the universe by reverting to
• 1:16:00
  Speaker 1: any primitive state that either either universe. So first of all,
• 1:16:04
  Speaker 1: you have to be in the exact right sweet spot
• 1:16:06
  Speaker 1: distance from from the Sun. You have to hope a
• 1:16:09
  Speaker 1: meteor doesn't come along or any of the early Solar
• 1:16:12
  Speaker 1: System formations that hits you. There's another thesis that says,
• 1:16:18
  Speaker 1: so the Earth has this giant, oversized moon relative to
• 1:16:22
  Speaker 1: all the other planets, and but for that, you may
• 1:16:26
  Speaker 1: not have had tides, which really lead to accelerating a
• 1:16:31
  Speaker 1: lot of of just taking protein strings and leading to
• 1:16:36
  Speaker 1: minerals and and taking tides all the waing inland and
• 1:16:40
  Speaker 1: then having the tides leave. You get that with an
• 1:16:42
  Speaker 1: oversized moon without that, So you know, once, once you
• 1:16:46
  Speaker 1: study physics, you can never really let it go. It's
• 1:16:49
  Speaker 1: always there. I find that stuff I don't know how
• 1:16:51
  Speaker 1: we had this with this digression from other than Schopenhauer's
• 1:16:57
  Speaker 1: essays and aphorisms, I guess and and finement stuff, which
• 1:17:01
  Speaker 1: is uh, which is fascinating. So so since you joined
• 1:17:05
  Speaker 1: the industry, boy, that was like a I don't know
• 1:17:08
  Speaker 1: where that digression came from, but it's in the back
• 1:17:12
  Speaker 1: of my head. And it's uh, I never heard that
• 1:17:14
  Speaker 1: about the moon, the moon making making them cheer. Well,
• 1:17:19
  Speaker 1: it's not so much that it made it possible, but
• 1:17:23
  Speaker 1: you end up with these really large tidal Now keep
• 1:17:26
  Speaker 1: in mind the moon is slowly moving away from the
• 1:17:28
  Speaker 1: Earth um and and a few millennia ago, a few
• 1:17:33
  Speaker 1: billion years ago, it was much closer, which, by the way,
• 1:17:36
  Speaker 1: raises a whole another question. How did this moon end
• 1:17:40
  Speaker 1: up around this planet? Was it captured? Was there were
• 1:17:43
  Speaker 1: there two moons um one of which got absorbed into
• 1:17:47
  Speaker 1: the other? I mean, there's all sorts of thesis is
• 1:17:49
  Speaker 1: as to how you end up with a really big
• 1:17:52
  Speaker 1: moon relative to a mid sized planet. When we look
• 1:17:56
  Speaker 1: at Jupiter of saner and they have what is it
• 1:17:58
  Speaker 1: a hundred dozens and dozens of moons, all of which yeah,
• 1:18:04
  Speaker 1: so it's it's I'll dig up the name of that
• 1:18:07
  Speaker 1: in a while ago. I think it's called Rare Earth.
• 1:18:10
  Speaker 1: But if you like the occasional physics nonfiction, you might
• 1:18:14
  Speaker 1: find this um and it is a little uh, not
• 1:18:18
  Speaker 1: religious or spiritual, but anything that changes your perspective of
• 1:18:24
  Speaker 1: our place in the universe is really kind of fascinating
• 1:18:28
  Speaker 1: and spiritual and curious. Although there are people who insist
• 1:18:31
  Speaker 1: firm's right and will eventually find people, but so far.
• 1:18:37
  Speaker 1: And then there's another thesis that says, why are you
• 1:18:40
  Speaker 1: looking for them? If they can communicate with you, they
• 1:18:43
  Speaker 1: could come here and basically, oh, a nice planet to Uh.
• 1:18:47
  Speaker 1: There's a third one that says, don't let anybody see you,
• 1:18:49
  Speaker 1: because people in the Amazon or to the bush exactly
• 1:18:53
  Speaker 1: that we we are, the American Indians, and anybody who's
• 1:18:57
  Speaker 1: coming superior technology is superior here ability to do whatever
• 1:19:02
  Speaker 1: they want. And let's hope that doesn't happen anytime soon.
• 1:19:06
  Speaker 1: Um So, but we digress. So so let's go back
• 1:19:10
  Speaker 1: to uh, quantitative finance. So since you started in that industry,
• 1:19:16
  Speaker 1: what do you think are the biggest changes that have
• 1:19:22
  Speaker 1: affected finance? I think electronic trading and electronic markets and
• 1:19:30
  Speaker 1: the effect that said filtering down on everything so kind
• 1:19:33
  Speaker 1: of computers in a sense no more open outcry um,
• 1:19:36
  Speaker 1: you know, everything matched by computer, and that certainly affected
• 1:19:40
  Speaker 1: the careers of people and and um did that did
• 1:19:45
  Speaker 1: that enable a lot of what we see on the
• 1:19:47
  Speaker 1: quantitative side to progress. Without that, you kind of at
• 1:19:50
  Speaker 1: an impasse, aren't you. Yeah, they kind of go in
• 1:19:52
  Speaker 1: lock step. You know, you can now get good price data,
• 1:19:56
  Speaker 1: good volatility that I guess the bond market isn't totally
• 1:19:59
  Speaker 1: isn't really electronic yet, but hitting in that direction. And
• 1:20:02
  Speaker 1: currencies are still over the counter. But I think that's
• 1:20:05
  Speaker 1: the biggest trend that people can now trade by algorithms
• 1:20:07
  Speaker 1: and trade by computer, and um, those skills are more
• 1:20:11
  Speaker 1: and more more and more more and more in demand.
• 1:20:14
  Speaker 1: So when you when you look at models, so you
• 1:20:16
  Speaker 1: could create models for equities and derivatives, and you can
• 1:20:20
  Speaker 1: create models for fixed income. Can you can we not
• 1:20:25
  Speaker 1: create models for currencies because of the way they're traded.
• 1:20:30
  Speaker 1: You can still make models, but they're kind of harder
• 1:20:32
  Speaker 1: to implement because you I don't know how it is
• 1:20:34
  Speaker 1: now a little out of date, but you had to
• 1:20:35
  Speaker 1: call somebody and you can't really do statistical arbitrage easily
• 1:20:40
  Speaker 1: where you have an algorithm that just sends out orders
• 1:20:43
  Speaker 1: and buys when it needs to in cells when it
• 1:20:45
  Speaker 1: needs to, because you still have to call somebody on
• 1:20:46
  Speaker 1: the telephone. Had to do that in equities to sort
• 1:20:50
  Speaker 1: of you know, twenty five years ago, whereas now now
• 1:20:53
  Speaker 1: nobody makes schools. I wonder why currencies aren't as automated
• 1:20:58
  Speaker 1: or as electronically driven as as some of these other
• 1:21:01
  Speaker 1: asset classes. Yeah, because actually they should be in the
• 1:21:04
  Speaker 1: sense that there you know, it's hard to do with
• 1:21:05
  Speaker 1: bonds because bunds are very idiosyncratic and there aren't one
• 1:21:09
  Speaker 1: stock millions of different bonds and yeah, but currencies are
• 1:21:12
  Speaker 1: and I think it will go that way. I think
• 1:21:14
  Speaker 1: partly because in a cynical sort of way, there's a
• 1:21:16
  Speaker 1: lot of money to be made by I think part
• 1:21:19
  Speaker 1: of the people who trade currencies, my guests, would be
• 1:21:21
  Speaker 1: that they're reluctant to go electronic because there's a lot
• 1:21:24
  Speaker 1: of them. You look at what Chase charging you three
• 1:21:27
  Speaker 1: for for every time you use your credit card in
• 1:21:29
  Speaker 1: a foreign country. There's very big, very big margins over there.
• 1:21:33
  Speaker 1: So it hasn't gone electronic because there's a big incentive.
• 1:21:36
  Speaker 1: I think that's incentive to not to Did you see
• 1:21:38
  Speaker 1: somebody got into trouble some bank a few a few
• 1:21:40
  Speaker 1: months ago for giving giving clients UM the worst price,
• 1:21:46
  Speaker 1: the worst currency price of the day, consistently consistently giving
• 1:21:49
  Speaker 1: them the low if they were if they were selling
• 1:21:51
  Speaker 1: in the hive, they were buying. That's amazing. Why why
• 1:21:55
  Speaker 1: are we not surprised by that? UM? So we've we've
• 1:21:59
  Speaker 1: talked about what's changed in the past. What are the
• 1:22:02
  Speaker 1: upcoming shifts that you see, UM that's going to impact
• 1:22:06
  Speaker 1: quants or impact the concept of quantitative UH modeling. UM.
• 1:22:15
  Speaker 1: Try to think a little bit about this as you're talking.
• 1:22:19
  Speaker 1: Just the extensions of what's happening. I think the vanishing
• 1:22:22
  Speaker 1: of small investors, which has been happening and maybe not
• 1:22:25
  Speaker 1: so much in Japan, but it's certainly happened here. Nothing
• 1:22:27
  Speaker 1: is now starting to happen in Japan. What about China,
• 1:22:30
  Speaker 1: which I was going to say, China seems to be
• 1:22:32
  Speaker 1: all mom and pop investors. When does that become I
• 1:22:36
  Speaker 1: think they're more mature and institutional. I think that's that's
• 1:22:40
  Speaker 1: doomed in the long run. I mean, I think mom
• 1:22:43
  Speaker 1: and poppy investors are doomed in the long run, and
• 1:22:45
  Speaker 1: in China to China is still manipulating their markets a lot,
• 1:22:48
  Speaker 1: as far as I can tell, um, So how does
• 1:22:51
  Speaker 1: that play out? I suppose, I suppose badly. At some
• 1:22:55
  Speaker 1: point Chinese markets will collapse and I'm worse than what
• 1:22:59
  Speaker 1: we've just four correction we've seen, I would guess. So
• 1:23:02
  Speaker 1: I'm not really good at predicting the future about the stuff,
• 1:23:05
  Speaker 1: but I think as China is going to have to
• 1:23:07
  Speaker 1: democratize them now you see a lot of people are
• 1:23:10
  Speaker 1: trying to get money out of the country. They're gonna
• 1:23:11
  Speaker 1: have to put in capital controls. Um. It'll be interesting
• 1:23:15
  Speaker 1: to see what happens. Certainly, in the program I teach,
• 1:23:17
  Speaker 1: it's sort of astonishing of the of the students are
• 1:23:20
  Speaker 1: basically from mainland China. Really that's a wild number, and
• 1:23:25
  Speaker 1: that's true in most of the sciences in this country.
• 1:23:28
  Speaker 1: Are they going back home or is this a way
• 1:23:30
  Speaker 1: to get out of the country? A mix, a mix,
• 1:23:33
  Speaker 1: but a lot of them. You know, it's an expensive program.
• 1:23:35
  Speaker 1: People pay you know, I don't know, sixty or eighty
• 1:23:38
  Speaker 1: thousand dollars a year to study plus living costs. Um.
• 1:23:42
  Speaker 1: Chinese Chinese have money, they get they can get money
• 1:23:46
  Speaker 1: out of the country until now to pay for tuition
• 1:23:49
  Speaker 1: or is this just a way to know this is
• 1:23:53
  Speaker 1: for tuition? And and so yeah, they're they're, they're millions
• 1:23:56
  Speaker 1: of It's good. The millions of Chinese students in this country.
• 1:24:00
  Speaker 1: Some stay, some go back. But really the graduate schools
• 1:24:02
  Speaker 1: in the sciences and certainly in the finance engineering programs,
• 1:24:05
  Speaker 1: but I think in physics general will run on all
• 1:24:08
  Speaker 1: run on on foreign students in China. Why is that?
• 1:24:11
  Speaker 1: That's fascinating. It's a good And when I worked at
• 1:24:15
  Speaker 1: Goldman I ran this con group. You know, the majority
• 1:24:17
  Speaker 1: of foreign, not necessarily Chinese, but the majority of I
• 1:24:21
  Speaker 1: don't know, Rember. What I see in a lot of
• 1:24:23
  Speaker 1: math is so so it's it's Chinese, it's Korean, it's Indian.
• 1:24:29
  Speaker 1: Used to be Jews sort of fifty years ago. And
• 1:24:32
  Speaker 1: so what is it Each subsequent generation of immigrants takes
• 1:24:36
  Speaker 1: the hardest working area until the next. General, How does
• 1:24:41
  Speaker 1: that work? Yeah, I think I think that's what kind
• 1:24:43
  Speaker 1: of happens. They immigrants, um immigrants come in and and
• 1:24:48
  Speaker 1: do the stuff that's that's hard. Not so much hard
• 1:24:51
  Speaker 1: in a difficult sense, but hard in that very concrete
• 1:24:54
  Speaker 1: that you know, what successes and you know what, you
• 1:24:56
  Speaker 1: know what what being good at it means. And then
• 1:24:59
  Speaker 1: their kids want to be businessmen. They don't want to
• 1:25:01
  Speaker 1: work that hard. So then that opens up the flow
• 1:25:05
  Speaker 1: as first who was the Jews, and then it's Asians,
• 1:25:07
  Speaker 1: and then it's another word, it's each subsequent generation of immigrants.
• 1:25:11
  Speaker 1: So you know, a friend of mine is fond of
• 1:25:13
  Speaker 1: pointing out that when you mentioned a lot of the
• 1:25:16
  Speaker 1: Jews had come over in a way of immigration, studying
• 1:25:20
  Speaker 1: the sciences at one point in time, the predecessor to
• 1:25:23
  Speaker 1: the NBA, which back then was heavily represented with Jewish
• 1:25:30
  Speaker 1: basketball players. And I was astonished when I first heard it,
• 1:25:33
  Speaker 1: But neither did I the first time I heard that,
• 1:25:35
  Speaker 1: I'm like, what come on, you're pulling my leg. But
• 1:25:38
  Speaker 1: it's the same thesis of a wave of immigrants comes.
• 1:25:42
  Speaker 1: They're willing to do stuff that everybody who's here and
• 1:25:45
  Speaker 1: somewhat wealthy and a little spoiled perhaps don't want to do.
• 1:25:49
  Speaker 1: And then after they go through that and achieve some
• 1:25:51
  Speaker 1: degree of success, their kids don't want to do that,
• 1:25:54
  Speaker 1: and it creates yet another opening, and then the next
• 1:25:57
  Speaker 1: wave of immigrants come. That was, uh, I wonder how
• 1:26:02
  Speaker 1: far this continues, who who's going to follow the Chinese
• 1:26:05
  Speaker 1: after that? But eight of your students as Chinese in
• 1:26:11
  Speaker 1: this master's program, about like a hundred students, So I
• 1:26:13
  Speaker 1: would say I'm guessing a little bit. I would say
• 1:26:17
  Speaker 1: comes straight from mainland China. Another fort come from America
• 1:26:21
  Speaker 1: or Europe, but the Chinese citizens and went their undergrad
• 1:26:24
  Speaker 1: In fact, this is sort of a little funny. But
• 1:26:26
  Speaker 1: my son who lives in Hong Kong, he teaches history
• 1:26:29
  Speaker 1: at Hong Kong University of Science and Technology. And when
• 1:26:32
  Speaker 1: I've been there, of setting on his class and his
• 1:26:34
  Speaker 1: classroom looks more cosmopolitan than mine. Really, yeah, I wonder
• 1:26:38
  Speaker 1: what that's about. That's quite fashionating. He's got some fair
• 1:26:42
  Speaker 1: amount of and expacts and people or Australians who come there,
• 1:26:47
  Speaker 1: whereas our classes are pretty much nothing wrong with it.
• 1:26:50
  Speaker 1: But that just shows you where it's going. So your students,
• 1:26:52
  Speaker 1: who who are who graduate the master's degree in financial engineering?
• 1:26:57
  Speaker 1: Where they end up working after that? Some in China,
• 1:27:01
  Speaker 1: some in money management, some in the risk management, a
• 1:27:05
  Speaker 1: few in trading UM, some in non purely financial firms.
• 1:27:10
  Speaker 1: Now there's a big trend towards machine learning and big data. UM.
• 1:27:14
  Speaker 1: How many how many what percentage of your students stay
• 1:27:17
  Speaker 1: in the United States and work in that field? Um,
• 1:27:20
  Speaker 1: I would guess fifty or sixty, but I'm actually on
• 1:27:23
  Speaker 1: shaky ground now. I don't know. For the ballpark about
• 1:27:26
  Speaker 1: half that wouldn't surprise you. Huh, that's quite fascinating. Um. So,
• 1:27:32
  Speaker 1: speaking of students, what what sort of advice would you
• 1:27:35
  Speaker 1: give to a millennial or someone who's just graduating from
• 1:27:39
  Speaker 1: school and is interested in in a career in modeling
• 1:27:43
  Speaker 1: and quantitative finance. Okay, I'm somewhat cynical stuff. I don't know.
• 1:27:47
  Speaker 1: I would say, first of all, yeah, I started out
• 1:27:50
  Speaker 1: thinking I was going to have one career and you're
• 1:27:53
  Speaker 1: the same from what you were telling And then you
• 1:27:54
  Speaker 1: discover that you're going to change and life has defeated
• 1:27:57
  Speaker 1: to day. Yeah too, So I've kind of had three
• 1:27:59
  Speaker 1: in a sense. I started in physics, then I went
• 1:28:02
  Speaker 1: to Bell Labs for a while, and then I ended
• 1:28:04
  Speaker 1: up in finance. So I would say, expect to have
• 1:28:06
  Speaker 1: more than one career. Don't do don't if it you're
• 1:28:09
  Speaker 1: going to do one thing for the same time, it's
• 1:28:10
  Speaker 1: actually quite invigorating to change. Yeah, it's a whole different
• 1:28:14
  Speaker 1: set of muscles, and after twenty years, it's kind of
• 1:28:17
  Speaker 1: nice to have a difference. Um, I would say, get
• 1:28:20
  Speaker 1: good at programming, at least that's my experience in almost anything.
• 1:28:23
  Speaker 1: If you can do your own Yeah, I'd like to
• 1:28:24
  Speaker 1: tell people to be willing to get your hands dirty
• 1:28:27
  Speaker 1: and do your own dirty work. Don't just be a
• 1:28:29
  Speaker 1: manager maybe one day. But that's the thing I was
• 1:28:31
  Speaker 1: going to say earlier. It summer seems to me a
• 1:28:33
  Speaker 1: lot of maybe not now after the internet craze, but
• 1:28:36
  Speaker 1: in the nineties, most Americans and when I worked at
• 1:28:38
  Speaker 1: most Americans wanted to be managers and most foreigners wanted
• 1:28:42
  Speaker 1: to work with their hands or their heads sort of spread.
• 1:28:46
  Speaker 1: And now it's changed a little bit. I'm getting off topic.
• 1:28:48
  Speaker 1: It's changed a little bit because Americans are suddenly discovering
• 1:28:52
  Speaker 1: that you can get rich by by being a good
• 1:28:54
  Speaker 1: program and like like like Mark Zuckerberg or somebody like that,
• 1:28:58
  Speaker 1: and so it's changed a little bit. But for a
• 1:28:59
  Speaker 1: long time it seemed to me where you can just
• 1:29:01
  Speaker 1: wanted to be managers, so why go do financial engineering,
• 1:29:03
  Speaker 1: whereas foreigners sort of had no choice. I wonder how
• 1:29:06
  Speaker 1: much of that was a post War War two phenomena,
• 1:29:09
  Speaker 1: because if you think about the era that fouled the
• 1:29:12
  Speaker 1: Second World War, you had a huge rise of corporations
• 1:29:16
  Speaker 1: and what we almost derisively described as middle management today,
• 1:29:22
  Speaker 1: it was the path to a reasonably safe, certain comfortable job.
• 1:29:27
  Speaker 1: But that's all gone away a long time ago. I wonder,
• 1:29:30
  Speaker 1: I wonder how much of that is demographics and how
• 1:29:33
  Speaker 1: that's changed. That's interesting, Probably probably a large part. Yeah,
• 1:29:38
  Speaker 1: I was trying to think. So I was saying, be
• 1:29:40
  Speaker 1: wanting to do your own dirty work, get your hands dirty,
• 1:29:42
  Speaker 1: and learned to program. You learned to program, don't ignore
• 1:29:46
  Speaker 1: I don't know I was gonna say him. One thing
• 1:29:49
  Speaker 1: I kind of learned is when I left physics. When
• 1:29:51
  Speaker 1: I left particle physics, I am I was sort of
• 1:29:53
  Speaker 1: a little bit disgusted with myself that I thought I
• 1:29:56
  Speaker 1: was going to be a physicist and I wasn't gonna
• 1:29:57
  Speaker 1: be one. And they were jobs in apply physics as
• 1:30:00
  Speaker 1: opposed to pure research like an energy or or heat,
• 1:30:04
  Speaker 1: you know, heat stuff, and I didn't want to do that.
• 1:30:06
  Speaker 1: I thought, if I'm going to get out of physics,
• 1:30:08
  Speaker 1: I'm going to go sort of all the way. And
• 1:30:09
  Speaker 1: it works for me. But at the same time, I
• 1:30:11
  Speaker 1: realized over the years that almost everything is interesting, and
• 1:30:14
  Speaker 1: that maybe I was wrong. I could have gotten just
• 1:30:16
  Speaker 1: as interested in doing something else. So I think the
• 1:30:19
  Speaker 1: more applied as opposed to theoretical Yes, and maybe it
• 1:30:22
  Speaker 1: might have still been interesting. And but but I sort
• 1:30:24
  Speaker 1: of scandered at the time in a snobby sort of way.
• 1:30:27
  Speaker 1: And um, my experience over the years with lots of
• 1:30:30
  Speaker 1: things is that when you get involved in them hardcore
• 1:30:34
  Speaker 1: and deeply, you find all sorts of interesting things that
• 1:30:37
  Speaker 1: you didn't expect. And so I think it's that's the
• 1:30:41
  Speaker 1: advice I would give people is m is um plunge
• 1:30:44
  Speaker 1: in and if you have to do something different, and
• 1:30:47
  Speaker 1: and when you get deep inside you find a whole
• 1:30:49
  Speaker 1: world opening up. And and then our final question, what
• 1:30:53
  Speaker 1: is it that you know about modeling, about investing, about
• 1:30:56
  Speaker 1: quantitative finance today you wish you knew thirty years ago
• 1:31:00
  Speaker 1: when you you were first stepping into the field. Don't
• 1:31:04
  Speaker 1: get out when things look bad. Don't get out when
• 1:31:07
  Speaker 1: things look bad, because in the long run, don't sell
• 1:31:10
  Speaker 1: at the bottom. So, in other words, mean regression, that
• 1:31:13
  Speaker 1: reversion to one of these people that that when everything
• 1:31:17
  Speaker 1: was about to collapse, I thought, oh, my God, like
• 1:31:19
  Speaker 1: now I've been a soul before it goes to zero
• 1:31:22
  Speaker 1: and I'm but it doesn't go so far. It doesn't
• 1:31:25
  Speaker 1: go to zero. So I would say take a long
• 1:31:28
  Speaker 1: term VI you and ignore the fluctuations. But let's talk
• 1:31:31
  Speaker 1: to do Professor Dermott. I have to thank you for
• 1:31:34
  Speaker 1: being so generous with you with your time. This has
• 1:31:37
  Speaker 1: really been a fascinating conversation. I could sit here for
• 1:31:42
  Speaker 1: hours longer, but I know you have places um to
• 1:31:45
  Speaker 1: go and and things to do to review if people
• 1:31:48
  Speaker 1: want to find your work. My life is a quant
• 1:31:50
  Speaker 1: is on Amazon. Models Behaving Badly can be found just
• 1:31:54
  Speaker 1: about anywhere. A Manual Derman dot com at a Manual
• 1:31:59
  Speaker 1: Derman on Twitter, and your homepage is on at Columbia.
• 1:32:04
  Speaker 1: I don't actually have one in Columbia. I'll just have
• 1:32:05
  Speaker 1: Emmanuel Deman dot com. And I once wrote a book
• 1:32:08
  Speaker 1: of columns and short stories which you can get on
• 1:32:11
  Speaker 1: Amazon as an e book that I put them myself.
• 1:32:13
  Speaker 1: And what's the name of that's called bad Behavior? Bad Behavior.
• 1:32:18
  Speaker 1: That's not a big best seller by any means. But
• 1:32:20
  Speaker 1: I once wrote a bunch of columns for a German newspaper.
• 1:32:23
  Speaker 1: There was an editor there. He actually died shortly episode
• 1:32:26
  Speaker 1: the Frank foot or argument at Siton who liked models
• 1:32:29
  Speaker 1: behaving badly, And for about a year I wrote a
• 1:32:31
  Speaker 1: column for them every two weeks, and I took most
• 1:32:34
  Speaker 1: of it. They put it into German, but I had
• 1:32:36
  Speaker 1: it in English, and I put most of it into
• 1:32:38
  Speaker 1: this some little book. I'll put a link up to
• 1:32:41
  Speaker 1: this when this goes up. Thank you UM so much
• 1:32:44
  Speaker 1: for your time. For those of you who enjoy this conversation,
• 1:32:47
  Speaker 1: look upward down an inch on iTunes and you could
• 1:32:51
  Speaker 1: see all of our prior conversations. I would be remiss
• 1:32:56
  Speaker 1: if I did not thank UH my research direct jer
• 1:33:00
  Speaker 1: Michael Batnick, who helped do the deep dive UH into
• 1:33:03
  Speaker 1: Professor Derman's background. UH special thanks to Taylor Riggs for
• 1:33:08
  Speaker 1: handling all the booking, and Charlie Vohmer for being our producer.
• 1:33:12
  Speaker 1: You're listening to Masters in Business on Bloomberg Radio.