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Speaker 1: This is Masters in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: This week on the podcast, I have an extra special guest.

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Speaker 1: His name is Paul Wilmot, and if you are remotely

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Speaker 1: interested in anything having to do with quantitative finance, then

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Speaker 1: this is the podcast for you. UH. He is not

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Speaker 1: only a colleague in Pier of such esteemed quantz as

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Speaker 1: a manual Derman and nasiem To lab who is really

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Speaker 1: more of a pure mathematician, but he is the purveyor

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Speaker 1: of the world's largest website on quantitative finance. He has

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Speaker 1: written numerous books on UH, textbooks on quantitative finance. He

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Speaker 1: has helped create the certificate UH for Finance UH quant Work.

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Speaker 1: And he has just been absolutely on the forefront of

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Speaker 1: identifying what's wrong with financial models, markets, derivatives, risk taking.

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Speaker 1: There are there are a few people who understood why

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Speaker 1: the financial crisis of oh an O nine was coming

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Speaker 1: UH more specifically and earlier than he did. Eight years

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Speaker 1: in advance. He was warning, Hey, these models are really

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Speaker 1: problematic and and they're going to result in in big problems. Um,

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Speaker 1: They're gonna result in real issues. And he turned out

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Speaker 1: to be not only right generally, but what he was

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Speaker 1: criticizing specifically, turned out to be a large part of

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Speaker 1: of why markets and credit went went through its collapse. So,

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Speaker 1: with no further ado, here is my conversation with Paul Wilmot.

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Speaker 1: I have an extra special guest. His name is Paul Wilmot.

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Speaker 1: How do I describe him? He is a expert in

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Speaker 1: quantitative finance, a researcher and author, a consultant, the professor.

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Speaker 1: He has written over a hundred research papers on mathematics

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Speaker 1: and finance, several best selling and some would say groundbreaking textbooks.

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Speaker 1: He runs what is the biggest quantitative analysis website in

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Speaker 1: the world. Uh no, lesser a critic than seemed to

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Speaker 1: Leb described him as the smartest quant in the world,

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Speaker 1: the only one who truly understands what's going on, who

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Speaker 1: uses his head and has a sense of ethics. Paul Wilmot,

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Speaker 1: Welcome to Bloomberg. Hi, it's a place to be here.

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Speaker 1: I have to correct you already. I have to correct you.

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Speaker 1: I'm not, and never have been, a professor. I'm a

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Speaker 1: mere doctor. I'm afraid were you a lecturer? I've been.

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Speaker 1: I've had all sorts of research positions in universities, but

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Speaker 1: never reached that exalted. I'll tell you why I drew

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Speaker 1: that conclusion. There is a YouTube video of you describing

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Speaker 1: some of the chapters of some of your textbooks, and

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Speaker 1: you just have a natural professorial air. I have this thing,

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Speaker 1: and I through the assumption that I perhaps should not

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Speaker 1: have drawn. So let's talk a little bit about um,

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Speaker 1: your research, your work you're writing. I have to start

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Speaker 1: out with a quote of yours from one of your papers.

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Speaker 1: The paper published in two thousand was the Use, Misuse

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Speaker 1: and Abuse of Mathematics and Finance. That title alone is

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Speaker 1: a good place to start. But here's the quote that

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Speaker 1: was so prescient. It's clear that a major rethink is

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Speaker 1: desperately required if the world is to avoid a mathematician

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Speaker 1: led market meltdown. That is pretty much dead on. And

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Speaker 1: seven years later we had the quant crash, and a

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Speaker 1: year after that we had the full blown Great Financial Crisis.

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Speaker 1: What were you looking at that gave you such a

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Speaker 1: clear understanding of of the coming storm? Well, I also

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Speaker 1: a few years after that, I wrote that I did

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Speaker 1: specify that I thought it was going to be credit

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Speaker 1: datives that might be the problem. So even more specific, yes,

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Speaker 1: and that that's because of my background, I haven't taken

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Speaker 1: the classical quant route, and I come with a lot

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Speaker 1: of skepticism, and I'm not a great fan of following

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Speaker 1: the herd. So whenever I look at mathematical models, I

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Speaker 1: can pretty much tell you whether the based on any reality,

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Speaker 1: or maybe whether there's any dangers involved with the the

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Speaker 1: hedging or the valuation. And everywhere I looked, I just

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Speaker 1: saw that there were dangers. It was not The models

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Speaker 1: for equities are not too bad in quantitative fires. The

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Speaker 1: models for interest rates are pretty bad, except interest rates

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Speaker 1: don't really do much, certainly at the moment anyway. But

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Speaker 1: credit the models are not only the models really really

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Speaker 1: bad in terms of not matching reality and how they use,

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Speaker 1: but also credit markets are absolute enormous, tremendous. In fact,

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Speaker 1: that also from the same paper, the underlying assumptions of

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Speaker 1: financial models, such as the importance of normal distribution, the

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Speaker 1: elimination of risk, measurable correlations, etcetera, are all incorrect. And

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Speaker 1: and when I read that, I was reminded of the

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Speaker 1: famous George Box quote all models are wrong, but some

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Speaker 1: are useful. And I know that's sort of a bastardization

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Speaker 1: of of what he really was intended. But the natural

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Speaker 1: quote next question is are all models wrong? And how

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Speaker 1: can you tell the ones that are useful from the

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Speaker 1: ones that are dangerous? Well, models in finance are going

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Speaker 1: to be wrong, and it's a definition you can you

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Speaker 1: can perfectly understand what's not told. Um. And as for

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Speaker 1: the word useful, I think you have to ask what

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Speaker 1: is meant by useful and the various ways of looking

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Speaker 1: at this useful. Are they useful in helping you control risk?

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Speaker 1: Are they helpful in allowing you to do more business?

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Speaker 1: Or there are all sorts of different angles, and some

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Speaker 1: of these are sort of conflict with what the man

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Speaker 1: in the street might want. For example, a model might

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Speaker 1: be useful because it's great for marketing, and it you know,

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Speaker 1: it fools the regulators and it's not very good, but hey,

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Speaker 1: it's it's useful if you want to try and start

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Speaker 1: a really big hedge fund. Um. So it depends what

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Speaker 1: you mean. I don't think that's what professor Box was

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Speaker 1: referring exactly. However, exactly, they certainly have conserved useful purposes,

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Speaker 1: albeit perhaps not accurately predicting what the universe may look

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Speaker 1: like exactly. So you hope that when we talk about

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Speaker 1: having a model. It's useful, you'd you'd like to think

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Speaker 1: in terms of, oh, it allows us to manage our

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Speaker 1: risk or hedge our risk something like that, or or

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Speaker 1: produce important new products that are helpful in hedging your

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Speaker 1: business or whatever. But I'm a bit too cynical to

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Speaker 1: think of. So you said you didn't take the usual

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Speaker 1: route into quantitative finance. How what was your route? How

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Speaker 1: did you find your way into quant finance and how

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Speaker 1: does that differ from the typical on Wall Street. Well,

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Speaker 1: you have to go back to my my my childhood. Really,

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Speaker 1: I've always from a very young age run my own

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Speaker 1: businesses at various sizes, and even when I was a

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Speaker 1: mathematician at university doing my doctorate or doing post doctoral work,

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Speaker 1: I was always interacting with the real world, trying to

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Speaker 1: do consultancy, etcetera. And it meant that I saw a

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Speaker 1: lot of different problems from fields outside of finance. In fact,

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Speaker 1: when I started in finance in the late eighties, before that,

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Speaker 1: I didn't even know there was any mathematics in finance.

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Speaker 1: But I'd work for are anautical companies, for steel companies,

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Speaker 1: for all sorts of different businesses where you try and

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Speaker 1: take a physical problem and turn into into something mathematical.

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Speaker 1: And then a colleague introduced me to these things called options.

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Speaker 1: This was in the there was actually just before the

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Speaker 1: seven crash, and just looking into the literature, I found, hey,

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Speaker 1: there's there's real mathematics involved in in in derivatives. And

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Speaker 1: so I started just to apply the same principles of

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Speaker 1: mathematical modeling too derivatives as I had to all these

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Speaker 1: other real world physical processes that I had worked on. Actually,

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Speaker 1: the when I first saw the famous Black Shoals equation, UM,

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Speaker 1: I thought, this is this is great. This is just

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Speaker 1: like second year undergraduate mathematics. UM. These days apparently you're

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Speaker 1: supposed to have a PhD. But actually that's that's a

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Speaker 1: that's a lot of nonsense. UM. Most quant finances just

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Speaker 1: second year undergraduate maths. Some of the book is really

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Speaker 1: quite fascinating, and you start out with a history of

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Speaker 1: investment theory, and I thought, skipping from the south Sea

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Speaker 1: Bubble to Adam Smith to the efficient market hypothesis, what

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Speaker 1: is the connective tissue between all three of those major

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Speaker 1: events in the history of finance. Well, when I think

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Speaker 1: you start to see is some going from just people's

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Speaker 1: personal experience through too trying to lay down some principles

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Speaker 1: and then trying to quantify those principles. So up to

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Speaker 1: the period now, the modern era, when pretty much everything

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Speaker 1: in finance is all quantified, everything is in terms of

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Speaker 1: expected returns and volatility and quantities like that, it could

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Speaker 1: be reduced to mathematical models. Everything has become mathematics. Yes, right,

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Speaker 1: So so then I always thought of the random walk

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Speaker 1: theory as a sort of squishy psychological claim that hey,

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Speaker 1: you know some maybe some people can beat the market,

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Speaker 1: but you're probably not one of them, and you probably

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Speaker 1: can't pick them. And then once we bring in taxes

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Speaker 1: and costs and everything else, you're better off just indexing.

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Speaker 1: But you relate the whole concept of random walk to

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Speaker 1: probability theory. What ties these two together? Well, if you

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Speaker 1: go back to the different types of analysis that people

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Speaker 1: do in in finance, you've got fundamental analysis, which is

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Speaker 1: about trying to figure out, by looking at the company report,

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Speaker 1: company's reports and the directors, et cetera, the product, how

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Speaker 1: much is this company really worth? Now, that's very difficult

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Speaker 1: to do that, That's a lot of analysis is required. Um,

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Speaker 1: I mean, can you read balance sheets? I can't read

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Speaker 1: balance sheets for exactly exactly so, but the real problem

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Speaker 1: is is that you're not trying to predict what the

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Speaker 1: the the market value of the company should be, because

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Speaker 1: you're trying to predict what other people the people are

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Speaker 1: buying certain shares, think those famous beauty exactly. So that

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Speaker 1: you move on to fundamental that sorry, move on from

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Speaker 1: fundamental to technical analysis, which is exact opposite, which is

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Speaker 1: really easy. You draw a chart to some trend lines

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Speaker 1: and some patterns. Problem is that sort of thing, statistically speaking,

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Speaker 1: doesn't work. The scientific evidence suggested the vast majority of

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Speaker 1: that doesn't work. So the great break through the fifties

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Speaker 1: six seventies was to say, let's throw that away. Let's

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Speaker 1: just say we can't predict things. We've got the concept

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Speaker 1: of the efficient market hypothesis as a background for this,

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Speaker 1: but let's just say it's like tossing a coin. Now,

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Speaker 1: is it a tossing a biased coin? What's the chance

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Speaker 1: of heads, what's the chance of tail? So you put

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Speaker 1: numbers to these probabilities and the sort of from that

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Speaker 1: we get lots of theories about derivatives valuation. And once

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Speaker 1: you've got derivatives, you can value derivatives, you can create

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Speaker 1: new instruments, and the new instruments what might require new math,

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Speaker 1: new mathematics. So you have this snowballing effect where you've

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Speaker 1: got the mathematics increasing and the number and type of

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Speaker 1: products similtarency increasing. So there's a quote in the book

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Speaker 1: I really like, and I'm going to mangle this a

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Speaker 1: little bit. No investment strategy based on mainstream finance theory

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Speaker 1: can protect protect investors from market wide crashes. First, is

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Speaker 1: anyone really suggesting, Hey, our strategy is going to protect you?

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Speaker 1: Is that? I guess that's naive of me to uh

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Speaker 1: to ask. But do people really believe, oh, here's the

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Speaker 1: magic seven like portfolio insurance that will protect us. I'm

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Speaker 1: sure there are some things you can do. Um, there

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Speaker 1: are all sorts of nuances here. I remember Nassy, my

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Speaker 1: dear pal, Nassim Talent, telling me about his um the

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Speaker 1: way he pitches this this idea, and that is to say,

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Speaker 1: it's insurance. But it's not a portfile, it's not an investment.

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Speaker 1: It's insurance. You're gonna lose money. In fact, you hope

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Speaker 1: you'll lose money because if you make money on this

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Speaker 1: insurance is because your house has burnt down, which you

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Speaker 1: don't want to have happened. So that's quite that's a

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Speaker 1: psychological way of looking at this, getting the putting in place,

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Speaker 1: the the the protection as a as a as a

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Speaker 1: cost rather as an investment. Um. There are some things

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Speaker 1: you can do. Of course, the famous portfolio dynamic portfoileon

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Speaker 1: insurance of the of the eighties was it was a

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Speaker 1: classic example of a feedback effect, the positive feedback effect,

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Speaker 1: that the the very action of trying to protect your

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Speaker 1: portfolio is what caused made which is a beautiful concept.

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Speaker 1: What what I don't understand is who thought, oh, if

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Speaker 1: the market is crashing, I'll go out and buy some

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Speaker 1: pots and that will protect me. Summing it up that way,

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Speaker 1: how did anybody think that that model was valid? And

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Speaker 1: granted there's a little hindsight bias here, but still it

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Speaker 1: sounds so obvious after the fact that that would not work, right.

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Speaker 1: I don't know. I don't know the I think the

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Speaker 1: I think in all of this that a key thing

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Speaker 1: you've always got to remember is that the psychology of

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Speaker 1: the markets and that whatever the mathematical models say, somehow

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Speaker 1: human beings in this particular field do try to do

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Speaker 1: manage to mess up the models. So here's another mangled quote.

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Speaker 1: Bankers offering complex financial products don't always understand the risks,

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Speaker 1: and that when a bank goes bust, stock markets collapse,

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Speaker 1: house prices tumble, it's your bank account, your shares, and

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Speaker 1: your home equity that suffers. Right, Well, it's certainly true

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Speaker 1: in my experience that bankers and especially regulators, I have

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Speaker 1: to say, I do not know as much as they

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Speaker 1: ought to. Um, if they were doctors and they didn't know,

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Speaker 1: you know, bleachers do a little bleeding exactly, or you

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Speaker 1: know which side is the heart? Aren't you know that

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Speaker 1: sort of thing, then they be lawsuits flying. But bankers,

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Speaker 1: in my experience, there's a lot of herd mentality within

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Speaker 1: bankers and that works for them. That's how that's how

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Speaker 1: they make money is from from pretending there are no

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Speaker 1: risks and although they may deep down they may realize

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Speaker 1: there are risks. And regulators I haven't met that many regulators.

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Speaker 1: I don't seem to bump into them very often, maybe

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Speaker 1: because the things I've said about them in the past,

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Speaker 1: but my experience is often they're very good at book learning.

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Speaker 1: You know, they've they've got that they've read the math books.

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Speaker 1: They know the models, but they're sorely lacking in street

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Speaker 1: smarts in my experience, so they don't really understand how

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Speaker 1: the world works. So you have in the past suggested

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Speaker 1: a solution to the problem of both bankers that don't

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Speaker 1: understand risk and regulators that don't know how to regulate

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Speaker 1: it is a tobin tax on each transaction, a point

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Speaker 1: zero zero eight percent tax on any buy sell, any

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Speaker 1: trade whatsoever. And how would that work and how would

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Speaker 1: that protect markets? You're very good with your numbers. The

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Speaker 1: the idea of the Tobin tax is to stop very

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Speaker 1: high frequency trading because, uh, you then lose with the

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Speaker 1: high frequency. The problem with one of the problems with

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Speaker 1: high frequency trading is that you lose the connection between

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Speaker 1: the value of a share company and the price. It

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Speaker 1: just becomes about, you know, the these these some time

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Speaker 1: series of numbers, and it could be anything. It doesn't matter.

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Speaker 1: But there's a company here and there are people whose

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Speaker 1: jobs on the line. Let's talk a little bit about

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Speaker 1: the time you spent at Kayasa Capital Capital two people

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Speaker 1: separated by a common language, So you were a founding

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Speaker 1: partner of the volatility arbitrage hedge funds, which was running

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Speaker 1: about two million dollars just on the two million and so,

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Speaker 1: so tell us a little bit about volatility arbitrage, which

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Speaker 1: is sort of ironic in these days of almost no volatility.

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Speaker 1: It's interesting that that it's very difficult I find I

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Speaker 1: found to predict the direction a stock is going to

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Speaker 1: go up put down. But it seems to be easier

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Speaker 1: and there are statistical reasons for this, actually, but it

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Speaker 1: seems to be easier to to forecast volatility. How much

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Speaker 1: noise there in there is in a stock. So you

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Speaker 1: put a counterintuitive though, that it's harder to find the

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Speaker 1: signal and to use you the noise. Yes, I can

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Speaker 1: see that, but at the same time, you can see

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Speaker 1: what the market is sort of thinking volatility is because options,

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Speaker 1: the valuation of options hinges upon estimation of volatility. So

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Speaker 1: you can say, oh, here's a core option, what volatility

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Speaker 1: are they plugging into? The famous black shows formed to

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Speaker 1: give that that price that you see in the market,

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Speaker 1: and so you can you can back out a thing

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Speaker 1: called implied volatility, which is sort of a bit like

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Speaker 1: what the market thinks. So you look around for when

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Speaker 1: is the implied volatility different sufficiently different? From your forecast

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Speaker 1: volatility and if they're different, and if you just happen

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Speaker 1: to be right, then you can make some money. So

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Speaker 1: that complex it's it's the purest sort of volatility arbitrage.

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Speaker 1: I'm betting my forecasts against the markets forecasts essentially is

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Speaker 1: what is what it amounts to? So in not so

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Speaker 1: efficient market, not so efficient, not so efficient. I don't

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Speaker 1: think believes in efficient markets. Who's listening to this program

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Speaker 1: to UM? I think there are a decent number. It's

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Speaker 1: it's a really interesting point because I think there are

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Speaker 1: a number of people who say, um. Look, cliff as Nests,

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Speaker 1: who runs a q R Gene Fama was his PhD

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Speaker 1: dissertation advisor and Fama's credit. He said, hey, if you

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Speaker 1: want to go out and prove factors work, I'll I'm

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Speaker 1: happy with that. So we know the market is kind

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Speaker 1: of a sort of mostly eventually efficient. Is that? Is

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Speaker 1: that a fair way to describe it? Not necessarily. It

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Speaker 1: depends where there's any mechanism for for removing any efficiency.

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Speaker 1: People just say oh that, you know, people say things

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Speaker 1: like the markets always right, and well, we know that's

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Speaker 1: certainly not How how could the market be twenty three

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Speaker 1: percent lower the day after the crash. Was it right

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Speaker 1: the day before? Was it right the day after? Is

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Speaker 1: it wrong both times? It's kind of hard to say

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Speaker 1: the market is always right right. Well. One then example

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Speaker 1: I I sometimes give my students is about uses the

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Speaker 1: idea of car insurance. For example, suppose you've got a

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Speaker 1: twenty thousand dollar car and your annual car insurance is

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Speaker 1: let's new simple numbers a thousand dollars. A quant would say, oh,

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Speaker 1: it's a twenty thousand dollar car. It's a thousand dollar

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Speaker 1: car in shoans are that means must be a five

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Speaker 1: percent chance of crashing. And because they're missing out from

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Speaker 1: that five fence the fact that the insurance company needs

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Speaker 1: to make a profit, I'm not doing things all sorts

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Speaker 1: of things. So so the way your typical quant or

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Speaker 1: um Chicago finance professor looks at things is in a

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Speaker 1: very pure way that sort of misses all the fun.

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Speaker 1: What I think, and as I've said before, I've been

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Speaker 1: running businesses since I was a child, virtually, and the

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Speaker 1: business side of things is what makes things interesting. And

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Speaker 1: so when we approach this sort of going back to

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Speaker 1: volatility arbitrage, you'd be amazed at if you look at

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Speaker 1: the literature. The there must be tens of thousands of

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Speaker 1: papers which talk about how to how to back out

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Speaker 1: from from market prices, what the implied vault inty is,

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Speaker 1: and how they assume that the implied volatility is right,

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Speaker 1: that the market knows, the market has a crystal ball,

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Speaker 1: it knows what vaults is going to be. Must be

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Speaker 1: tens of thousands. How many papers are there which say, well,

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Speaker 1: you know what, what if the market is wrong and

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Speaker 1: the volatility, the real volatility is different from implied volatility,

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Speaker 1: how can you make money? Then that's the underlying strategy,

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Speaker 1: that said Catholic. That must must be half a dozen papers,

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Speaker 1: and surely that's the most important thing. We've got an option.

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Speaker 1: The question is what am I going to do with this?

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Speaker 1: I could hedge with it, or I could make money

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Speaker 1: from it, and then just half a dozen papers on

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Speaker 1: how to make money. So so, how did the hedge

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Speaker 1: fund do? The hedge fund was did did wonderfully for

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Speaker 1: it's its lifespan of three years, which I believe is

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Speaker 1: is the average lifespan of a hedge fund. And and

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Speaker 1: so at the end of the three years, you basically

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Speaker 1: tapped out and said I'm going to go back to writing. Well,

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Speaker 1: I I learned a lot from that that hedge fund,

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Speaker 1: a lot of things about not just about hedge funds,

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Speaker 1: but also about about myself. I found that very interesting

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Speaker 1: because the Hedgeman was based in New York um and

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Speaker 1: where were you located was located in London, so there

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Speaker 1: was this five hour time difference, and just as things

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Speaker 1: were getting interested New York was when I was sitting

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Speaker 1: down to dinner or onting to go to bed or

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Speaker 1: something like that. So that was kind of frustrating. Let's

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Speaker 1: talk about the Wilmot business model. So you run the website,

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Speaker 1: you have a print magazine, you have a Paul and

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Speaker 1: Dominic quant recruitment, the book, which we all know is

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Speaker 1: books or giant money makers. What is your business model

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Speaker 1: as quant expert. Well that the Poul and Dominics doesn't

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Speaker 1: exist anymore. That's but the and also there was the

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Speaker 1: we talked about the world's largest quant website. It's also

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Speaker 1: apparently the world's most expensive magazine. I've been told. I

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Speaker 1: don't know that's true or what's the magazine costs. It's

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Speaker 1: not six hundred dollars for six issues. I don't think

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Speaker 1: that's terribly expensive, and I looked it up. It's not

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Speaker 1: actually this And if you look at some of the

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Speaker 1: like the medical journals and stuff, these are thousands of

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Speaker 1: In fact, there was a big article not too long

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Speaker 1: ago about what used to be academic research now sells

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Speaker 1: for thousands of dollars and there's two companies that have

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Speaker 1: sucked up all these formally free publication. And that that

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Speaker 1: was a quote from Esquire magazine. So I'd just like

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Speaker 1: to repeat that it's not very useful in marketing, though

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Speaker 1: there was until recently, well there still is. The Certificate

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Speaker 1: and Quantitative Finance that that is is the world's largest

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Speaker 1: high level quant education with a company called seven City.

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Speaker 1: This CQF was was founded with a company called seven

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Speaker 1: City Learning two thousands three was it. But it was

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Speaker 1: sold up to Fitch about three or four years. So,

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Speaker 1: but what is the business model? The business model, Well,

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Speaker 1: my whole business model all my life has been do

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Speaker 1: something which is fun and then and then I accidentally

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Speaker 1: or I always have this urge to turn things into

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Speaker 1: into businesses. It's not a it's not a greed thing.

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Speaker 1: It's an enthusiasm. Think. It's almost like I've got some hobby.

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Speaker 1: I'd like to make it public. It becomes more long

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Speaker 1: last thing and sustainable if there's a revenue stream behind it. Yeah,

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Speaker 1: I don't think of it like that, you know, it's

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Speaker 1: just sort of is something in my DNA that says,

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Speaker 1: you know, for example, I've started to learn the ukulele

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Speaker 1: a few years ago, and I just know sometime in

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Speaker 1: my life there will be a time when I'm on

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Speaker 1: a stage and people are paying money to listen to

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Speaker 1: me play the ukule I'm going to take the other

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Speaker 1: side of that trade. You should hedge that bet because

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Speaker 1: I don't see that happen. No, No, like, yeah, I'm

396
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Speaker 1: pretty good, I'm getting I'm getting that, I'm getting. No,

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Speaker 1: it's not. It has to do with your skill set.

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Speaker 1: It has to do with the demand ukulele. Before you'd

399
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Speaker 1: be surprised. I would be surprised. It is the instrument.

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Speaker 1: All the schools are taking up ukule because it's I'm

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Speaker 1: on the other side of that trade. Also, what did

402
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Speaker 1: you learn at school? Trombone and piano. I want to

403
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Speaker 1: do the trombone, but they would hate it the trombone

404
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Speaker 1: because at least the piano there's a distinct key for

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Speaker 1: each note the trombone. As a ten year old, you

406
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Speaker 1: have to have an year, no when you're more or less.

407
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Speaker 1: And I had a pretty good year, but not that good.

408
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Speaker 1: But they at schools in the UK, at least they

409
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Speaker 1: traditionally learned the recorder. You know, three we all did that, okay,

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Speaker 1: But now the ukulele is replacing the recorder because really

411
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Speaker 1: because it's it's easier, you know, you can really really

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Speaker 1: ten minutes. I should have brought it. I'm sorry you didn't.

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Speaker 1: That would have been next time, next time. Why do

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Speaker 1: we get into this business models? Right, you're gonna you're

415
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Speaker 1: gonna monetize ukulele and performance. But but I've always been

416
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Speaker 1: like that, and so I've going back to the late eighties.

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Speaker 1: I was doing some research with with colleagues at university

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Speaker 1: and we thought, let's let's why do we give some

419
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Speaker 1: courses on teach people in the city, and they just

420
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Speaker 1: was phenomenally successful. So we set up a business which

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Speaker 1: and then and then we turn into a book and

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Speaker 1: it was it was suggested, why don't we self published

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Speaker 1: this book? So rather than just could hand the book

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Speaker 1: over to a publisher, we actually published it in printing, etcetera.

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Speaker 1: And and my mother and my stepfather were in charge

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Speaker 1: of sales. So my poor mother. We we put in

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Speaker 1: the advertising. We'd say twenty four hour facts hotline, um,

428
00:25:19,800 --> 00:25:21,800
Speaker 1: so her phone would be ringing. And how did the

429
00:25:21,840 --> 00:25:24,879
Speaker 1: book sell? Oh? Incredibly well it was. It was. It

430
00:25:24,960 --> 00:25:28,560
Speaker 1: was just unbelievably considered. We did hardly any advertising. This

431
00:25:28,600 --> 00:25:31,600
Speaker 1: is before Amazon. It did it incredibly well by word

432
00:25:31,600 --> 00:25:33,680
Speaker 1: of mouth and even before the book came out, people

433
00:25:33,800 --> 00:25:36,639
Speaker 1: talking about it. People I think the things. People were

434
00:25:36,640 --> 00:25:41,080
Speaker 1: paying something like tund fifty dollars for a hard copy

435
00:25:41,160 --> 00:25:44,399
Speaker 1: that you had to cut and paste the pictures into

436
00:25:44,440 --> 00:25:46,199
Speaker 1: the text. And I don't mean cutting paste like you do,

437
00:25:46,320 --> 00:25:49,439
Speaker 1: you know with the with the mouse, physically scissors and glue.

438
00:25:49,840 --> 00:25:52,440
Speaker 1: So they would get two piles. They get the with

439
00:25:52,440 --> 00:25:55,240
Speaker 1: without the images, and their separate parlem with the images,

440
00:25:55,240 --> 00:25:57,120
Speaker 1: and they paid two fifty dollars to stick the two together.

441
00:25:57,160 --> 00:26:01,359
Speaker 1: And this was on quantitative finance Fast and this was

442
00:26:01,480 --> 00:26:04,919
Speaker 1: way early in the rise of the quants. This is

443
00:26:05,040 --> 00:26:08,760
Speaker 1: this is ninety two. Okay, So so there were there

444
00:26:08,760 --> 00:26:10,639
Speaker 1: are a third fourth inning. You know, there are a

445
00:26:10,640 --> 00:26:12,800
Speaker 1: few books out there, but this one was a different

446
00:26:12,800 --> 00:26:15,200
Speaker 1: style of book. And then from them it turned into

447
00:26:15,240 --> 00:26:19,480
Speaker 1: software companies, more publishing, et Ceteraally, just were you selling

448
00:26:19,480 --> 00:26:22,800
Speaker 1: this in the UK in the US worldwide? Well, yeah,

449
00:26:22,840 --> 00:26:25,680
Speaker 1: my mother and the fax machines. Yea, yeah worldwide. We

450
00:26:25,680 --> 00:26:29,520
Speaker 1: we we we we we had. We would give courses

451
00:26:29,560 --> 00:26:32,920
Speaker 1: in down in Wall streets and we would bring over

452
00:26:33,000 --> 00:26:35,200
Speaker 1: from the UK boxes full of these books to get

453
00:26:35,200 --> 00:26:37,960
Speaker 1: to sell to people on on the on the courses

454
00:26:38,320 --> 00:26:41,600
Speaker 1: and you'd sell them all out, sell them that every time. Um,

455
00:26:41,640 --> 00:26:44,560
Speaker 1: that's pretty fascinating. Do you still self published because you

456
00:26:44,600 --> 00:26:48,000
Speaker 1: have a number of books to your credit? This is Wiley?

457
00:26:48,200 --> 00:26:52,679
Speaker 1: But what the money formula? But the other quantitative finance

458
00:26:52,680 --> 00:26:57,000
Speaker 1: books are as as you mentioned earlier, You're hinted earlier

459
00:26:57,000 --> 00:26:59,080
Speaker 1: you don't make a lot of money usually from from books,

460
00:26:59,480 --> 00:27:03,399
Speaker 1: unless of J. K. Rolling or someone. Um, So that

461
00:27:03,640 --> 00:27:06,520
Speaker 1: there came a point where the it was there are

462
00:27:06,560 --> 00:27:10,080
Speaker 1: other things that were more profitable, more successful, and the

463
00:27:10,560 --> 00:27:14,600
Speaker 1: books became more of a hobby or a publicity marketing

464
00:27:14,600 --> 00:27:20,480
Speaker 1: tool really or financial models making people overconfident and reckless

465
00:27:20,480 --> 00:27:21,760
Speaker 1: in how they behave in the mark. Well, I don't

466
00:27:21,760 --> 00:27:23,320
Speaker 1: know where it's true or not, but that I suddenly

467
00:27:23,359 --> 00:27:28,520
Speaker 1: heard that that actually the accidents involving pedestrians and cyclists

468
00:27:28,520 --> 00:27:31,199
Speaker 1: have gone up for exactly saying that because no, no, no, no,

469
00:27:31,240 --> 00:27:33,639
Speaker 1: because of because people feel safe in their car and

470
00:27:33,680 --> 00:27:37,360
Speaker 1: they've got all these these these cushions and whatnot, they're

471
00:27:37,440 --> 00:27:40,639
Speaker 1: less likely to be injured. But people drive faster and

472
00:27:40,680 --> 00:27:43,480
Speaker 1: so they kill more pedestrians. So my assumption is their

473
00:27:43,600 --> 00:27:47,800
Speaker 1: texting when they're hitting bi but the or the pedestrians

474
00:27:47,840 --> 00:27:50,080
Speaker 1: are playing with their phones. No, it could be, could be,

475
00:27:50,160 --> 00:27:53,879
Speaker 1: could be pedestrians. An't they awful? Um? It depends. It's subjective.

476
00:27:53,920 --> 00:27:56,239
Speaker 1: When you're driving a car, they're awful. And when you're

477
00:27:56,280 --> 00:27:59,840
Speaker 1: a pedestrian. Look at these crazy drives cyclists, cyclists in

478
00:28:00,040 --> 00:28:03,320
Speaker 1: Manhattan horrifying. You know you can cut all that out.

479
00:28:03,520 --> 00:28:07,480
Speaker 1: We get to try that's great stuff. The yeah, that

480
00:28:07,960 --> 00:28:10,360
Speaker 1: what you should do. Of course, it's just stick knives

481
00:28:10,400 --> 00:28:12,679
Speaker 1: in the in the in the steering wheel period of all.

482
00:28:12,720 --> 00:28:16,000
Speaker 1: And then you drive very very safely. Then we just

483
00:28:16,000 --> 00:28:20,399
Speaker 1: get a middle thing over your chest and can you do?

484
00:28:20,480 --> 00:28:22,439
Speaker 1: What can you do? It's um and you'd have to

485
00:28:22,520 --> 00:28:24,560
Speaker 1: make sure it doesn't pop up and catch you any

486
00:28:24,960 --> 00:28:28,520
Speaker 1: but but it's it's it's I do again. Going back

487
00:28:28,560 --> 00:28:33,920
Speaker 1: to regulators regulators don't. They don't understand. There's a story

488
00:28:33,960 --> 00:28:36,120
Speaker 1: I could tell, but it involves it's far too technical,

489
00:28:36,480 --> 00:28:39,040
Speaker 1: but there's some some practice that people do in banks

490
00:28:39,080 --> 00:28:41,880
Speaker 1: called calibration. You don't need to know this, but it's

491
00:28:42,040 --> 00:28:47,120
Speaker 1: something that superficially it looks like it's a good mathematical model,

492
00:28:47,120 --> 00:28:50,280
Speaker 1: but what it actually does is it it hides risk.

493
00:28:50,360 --> 00:28:54,920
Speaker 1: It makes it any model risk that's there gets hidden

494
00:28:54,920 --> 00:28:58,120
Speaker 1: by this act of calibration. And when I've spoken to regulators,

495
00:28:58,120 --> 00:29:01,320
Speaker 1: they don't really really understand that what these people who

496
00:29:01,320 --> 00:29:03,760
Speaker 1: are calibrating it doing is hiding risk. And said they

497
00:29:03,800 --> 00:29:06,920
Speaker 1: go around telling the thanks you must calibrate, which is

498
00:29:06,920 --> 00:29:09,719
Speaker 1: like saying you must hide risk, which is really not

499
00:29:09,760 --> 00:29:11,920
Speaker 1: what regulators are supposed to be doing. So let's talk

500
00:29:11,920 --> 00:29:16,480
Speaker 1: about your wish list when it comes to better regulations,

501
00:29:16,520 --> 00:29:20,120
Speaker 1: better education. And I believe in the book you call

502
00:29:20,160 --> 00:29:23,760
Speaker 1: it negative bonuses. So what would your what would your

503
00:29:23,760 --> 00:29:27,000
Speaker 1: wish list be? Well, obviously more people should go to prison,

504
00:29:28,120 --> 00:29:33,000
Speaker 1: bonuses about some people should, that's the start. The bonus

505
00:29:33,080 --> 00:29:36,600
Speaker 1: is taken away. But lots of people get paid stupid money.

506
00:29:36,600 --> 00:29:39,840
Speaker 1: I like CEOs of companies getting stupid money, especially in

507
00:29:39,880 --> 00:29:42,240
Speaker 1: the because they went to the same school as some

508
00:29:42,320 --> 00:29:46,480
Speaker 1: other CEO. That's pretty A number of people have blamed

509
00:29:46,520 --> 00:29:52,280
Speaker 1: the compensation consultants for using se as their frame of reference. Oh,

510
00:29:52,360 --> 00:29:55,880
Speaker 1: let's compare to the seventy I all of other CEOs.

511
00:29:55,920 --> 00:29:58,280
Speaker 1: Why why why are you assuming you're better than most

512
00:29:58,360 --> 00:30:00,600
Speaker 1: of the other CEOs out there, So you have this

513
00:30:00,680 --> 00:30:04,560
Speaker 1: horrible upward spiral. It's terrible. It's terrible, but also not

514
00:30:04,680 --> 00:30:06,720
Speaker 1: for them, but for the rest of Yeah, yeah, yeah, yeah.

515
00:30:06,960 --> 00:30:09,520
Speaker 1: In the in the in the book, we talk about

516
00:30:09,600 --> 00:30:11,480
Speaker 1: having something like the you know, the f A A

517
00:30:12,200 --> 00:30:16,960
Speaker 1: who regulated planes, something like that for banking and complex

518
00:30:17,000 --> 00:30:20,640
Speaker 1: financial products. The analogy is that whenever you have a

519
00:30:20,640 --> 00:30:23,160
Speaker 1: new plane, it has to be tested, etcetera, etcetera. Well,

520
00:30:23,200 --> 00:30:24,760
Speaker 1: if you have a new financial product, it has it

521
00:30:24,760 --> 00:30:27,560
Speaker 1: should be tested by some people. Um, you can't have

522
00:30:27,600 --> 00:30:29,880
Speaker 1: too many of these of this specific you can't have

523
00:30:29,920 --> 00:30:32,400
Speaker 1: too much concentration risk, just like you can't have too

524
00:30:32,400 --> 00:30:34,520
Speaker 1: many planes landing the same up at the same time.

525
00:30:34,720 --> 00:30:37,320
Speaker 1: So in all sorts of analogies we draw between the

526
00:30:37,120 --> 00:30:41,480
Speaker 1: the airline business and the world of banking. And of

527
00:30:41,520 --> 00:30:44,680
Speaker 1: course it's an international thing. Planes take off one country,

528
00:30:44,760 --> 00:30:47,520
Speaker 1: land another country, well, how hard can it be to

529
00:30:47,600 --> 00:30:52,160
Speaker 1: have an international perspective to the banking regulation? And I

530
00:30:52,240 --> 00:30:54,800
Speaker 1: have to ask, since you brought it up earlier, what

531
00:30:55,080 --> 00:30:59,360
Speaker 1: motivated you and Professor Emmanuel German at Colombia and formally

532
00:30:59,720 --> 00:31:04,800
Speaker 1: of Alman Sachs to write the Financial Modelers Manifesto. Well,

533
00:31:05,040 --> 00:31:08,760
Speaker 1: he and I both had pretty much the same um

534
00:31:10,000 --> 00:31:13,000
Speaker 1: I thought at the same time about and we both

535
00:31:13,640 --> 00:31:18,800
Speaker 1: been very skeptical about mathematical models. There's one subtle difference though,

536
00:31:19,760 --> 00:31:22,320
Speaker 1: and that is Emmanuel Down hasn't his name attached to one,

537
00:31:23,200 --> 00:31:24,960
Speaker 1: which means he's kind of got a bit of skin

538
00:31:24,960 --> 00:31:27,160
Speaker 1: in the game that which I haven't got. So I

539
00:31:27,200 --> 00:31:30,960
Speaker 1: can be slightly more, you know, than he can if

540
00:31:30,960 --> 00:31:33,840
Speaker 1: you if you catch my drift anyway. Um, but both

541
00:31:33,880 --> 00:31:38,840
Speaker 1: of us are very skeptical about about the what quantz

542
00:31:38,960 --> 00:31:42,520
Speaker 1: doing risk managers in banks and hedge funds. And it

543
00:31:42,640 --> 00:31:45,520
Speaker 1: was shortly after the crisis that we we both had

544
00:31:45,680 --> 00:31:49,880
Speaker 1: apparently independently, thought we should write something, which is, we

545
00:31:50,000 --> 00:31:53,160
Speaker 1: teamed up and it was a combination of Karl Marx

546
00:31:53,840 --> 00:31:57,120
Speaker 1: and the hippocratic oath that we've We've we've got our

547
00:31:57,160 --> 00:32:01,760
Speaker 1: inspiration first, do no harm. Yes, we have been speaking

548
00:32:01,800 --> 00:32:06,120
Speaker 1: with Paul Wilmott. He is a expert in quantitative finance.

549
00:32:06,640 --> 00:32:10,600
Speaker 1: We love your comments, feedback and suggestions right to us

550
00:32:10,640 --> 00:32:14,480
Speaker 1: at m IB podcast at Bloomberg dot net. Check out

551
00:32:14,520 --> 00:32:17,600
Speaker 1: my daily column on Bloomberg View dot com. You could

552
00:32:17,640 --> 00:32:21,680
Speaker 1: follow me on Twitter at Rid Halts. I'm Barry Ri Halts.

553
00:32:21,720 --> 00:32:39,040
Speaker 1: You're listening to Master's in Business on Bloomberg Radio. Welcome

554
00:32:39,080 --> 00:32:41,040
Speaker 1: to the podcast, Paul, Thank you so much for doing this.

555
00:32:41,200 --> 00:32:44,280
Speaker 1: I'm I've been looking forward to having this conversation. I

556
00:32:44,280 --> 00:32:47,640
Speaker 1: wish I can keep you for another couple of hours. Um,

557
00:32:47,640 --> 00:32:50,520
Speaker 1: but I have to get to at least two questions

558
00:32:50,560 --> 00:32:54,880
Speaker 1: before I jump to my standard questions. This is This

559
00:32:54,960 --> 00:32:58,280
Speaker 1: is from the book The notional value of derivatives in

560
00:32:59,800 --> 00:33:05,000
Speaker 1: was one point to quadrillion dollars. Is that correct? Yes?

561
00:33:05,240 --> 00:33:08,240
Speaker 1: Are we still in any sort of risk from a

562
00:33:08,360 --> 00:33:13,160
Speaker 1: blow up in derivatives? My my assumption is most of

563
00:33:13,360 --> 00:33:18,160
Speaker 1: that one point to quadrillion offsets itself. What is the

564
00:33:18,280 --> 00:33:23,960
Speaker 1: actual amount that has us at any sort of risk?

565
00:33:24,200 --> 00:33:26,840
Speaker 1: It's it's hard to tell, because yes, that that is notional,

566
00:33:27,080 --> 00:33:29,120
Speaker 1: So it could be that you might have that could

567
00:33:29,160 --> 00:33:33,320
Speaker 1: be one point two million million quadrillion in swaps, so

568
00:33:33,400 --> 00:33:35,800
Speaker 1: that it might just be one percent of that is

569
00:33:35,840 --> 00:33:40,160
Speaker 1: a risk um which I hustled, not nothing. However, you

570
00:33:40,200 --> 00:33:44,040
Speaker 1: can exactly however you can calculate it's it's too large

571
00:33:44,080 --> 00:33:46,920
Speaker 1: because it's that one point to quadrillion. If you can

572
00:33:46,960 --> 00:33:50,000
Speaker 1: take of zero two, you're still left with something which

573
00:33:50,040 --> 00:33:53,680
Speaker 1: is which is and the world the GDP of the world,

574
00:33:53,680 --> 00:33:56,320
Speaker 1: it's some like fifty trillion. So's it's the wrong way around.

575
00:33:56,320 --> 00:33:58,640
Speaker 1: It's almost like finance is supposed to be in the

576
00:33:58,680 --> 00:34:01,640
Speaker 1: service industry, but it's almost like people are making chairs

577
00:34:01,640 --> 00:34:05,760
Speaker 1: and tables and whatever just to keep the finance industry going.

578
00:34:06,080 --> 00:34:09,640
Speaker 1: Because the numbers, the tail is working one percent is

579
00:34:09,640 --> 00:34:11,480
Speaker 1: what a hundred and twenty billion? Is that what we're

580
00:34:11,480 --> 00:34:18,399
Speaker 1: talking about off of one drillion, twelve trillion, twelve trillion? Yes, yes,

581
00:34:18,440 --> 00:34:21,440
Speaker 1: and yes, So that's still that's still a quarter of

582
00:34:21,480 --> 00:34:23,640
Speaker 1: the world's GDP even if you take off two zero,

583
00:34:23,719 --> 00:34:29,520
Speaker 1: the numbers, numbers are just mind bargling. And there are

584
00:34:29,560 --> 00:34:32,480
Speaker 1: all these Since you were running a volatility hedge fund,

585
00:34:32,560 --> 00:34:34,520
Speaker 1: one of the questions I forgot to ask you before,

586
00:34:34,880 --> 00:34:39,000
Speaker 1: there are currently all these short volatility products outright there.

587
00:34:39,440 --> 00:34:42,279
Speaker 1: What what are your thoughts on news. Well, in our

588
00:34:42,320 --> 00:34:47,080
Speaker 1: experience that it was definitely the case that options tended

589
00:34:47,120 --> 00:34:50,440
Speaker 1: to be overpriced. So in terms of of of a strategy,

590
00:34:50,880 --> 00:34:54,400
Speaker 1: he would be selling vanilla options because they're overpriced. But

591
00:34:54,560 --> 00:34:57,560
Speaker 1: the problem is selling options is when you have some

592
00:34:57,640 --> 00:35:00,920
Speaker 1: extreme events, then you just blow up. So you need

593
00:35:00,960 --> 00:35:02,920
Speaker 1: to make sure that you've got plenty of tail protection

594
00:35:03,040 --> 00:35:05,000
Speaker 1: in case what happens, which is something that we we

595
00:35:05,080 --> 00:35:08,360
Speaker 1: specialized in, and that's why your friends with no seemed

596
00:35:08,360 --> 00:35:12,400
Speaker 1: to leave. Indeed, we get way back. I recall having

597
00:35:12,400 --> 00:35:15,239
Speaker 1: a lunch with him. It was just three of us

598
00:35:16,000 --> 00:35:18,920
Speaker 1: in the middle of the financial crisis. That had to

599
00:35:18,960 --> 00:35:22,160
Speaker 1: be the summer of oh eight before A, I, G.

600
00:35:22,320 --> 00:35:25,200
Speaker 1: And Lehman blew up and we were I can't even

601
00:35:25,200 --> 00:35:29,760
Speaker 1: tell you. The restaurant Callari ta Verna on Street. Normally

602
00:35:30,040 --> 00:35:32,239
Speaker 1: you have to make reservations a week in advance. We

603
00:35:32,360 --> 00:35:35,560
Speaker 1: walk in at one o'clock, it's empty. We sit right down.

604
00:35:35,600 --> 00:35:40,720
Speaker 1: The three of us were just laughing away. Um, he's

605
00:35:41,080 --> 00:35:44,480
Speaker 1: I don't have to tell you, he's incredibly entertaining, and

606
00:35:44,560 --> 00:35:49,719
Speaker 1: we were talking about specifically how parts of this were

607
00:35:49,719 --> 00:35:52,359
Speaker 1: so obvious coming down the pike, and no it's not

608
00:35:52,400 --> 00:35:55,880
Speaker 1: that people didn't see it. Nobody wanted to believe it.

609
00:35:55,880 --> 00:35:59,239
Speaker 1: It was quite quite astonishing. No, he's he's a very

610
00:35:59,239 --> 00:36:01,319
Speaker 1: interesting person. You have to be careful when you're aund

611
00:36:01,400 --> 00:36:05,200
Speaker 1: him because dramatic things always happen, yes, always, always. He's

612
00:36:05,200 --> 00:36:07,960
Speaker 1: a magnet. He'll be getting he'll be getting some text

613
00:36:08,000 --> 00:36:11,040
Speaker 1: messages which will tell about some of astronomical sales of

614
00:36:11,080 --> 00:36:13,480
Speaker 1: his books, or some deal he's involved in, or the

615
00:36:13,520 --> 00:36:17,080
Speaker 1: markets are doing something crazy. I was, I was with him.

616
00:36:17,200 --> 00:36:22,360
Speaker 1: I was with him September in by Liverpool Street station

617
00:36:22,880 --> 00:36:25,400
Speaker 1: when we got the phone call saying what happened? I

618
00:36:25,440 --> 00:36:28,640
Speaker 1: was with him. I think was July the seventh in

619
00:36:28,760 --> 00:36:32,000
Speaker 1: London a few years later when when we had four

620
00:36:32,040 --> 00:36:35,600
Speaker 1: bombs went off. We were giving a course that day,

621
00:36:35,200 --> 00:36:39,840
Speaker 1: and um, no, things happen around him with the same

622
00:36:39,920 --> 00:36:42,120
Speaker 1: It makes sense that he wrote The Black Swan because

623
00:36:42,239 --> 00:36:45,120
Speaker 1: he is he is a Black Swan. Yes, indeed he is.

624
00:36:45,320 --> 00:36:47,000
Speaker 1: So it's it's I wrotly. At the same time his

625
00:36:47,040 --> 00:36:49,160
Speaker 1: book came out, the film The Black Swan came out,

626
00:36:49,200 --> 00:36:52,520
Speaker 1: so if you google related completely. Um. So let's get

627
00:36:52,560 --> 00:36:54,960
Speaker 1: to some of our our favorite questions that we ask

628
00:36:55,040 --> 00:36:58,560
Speaker 1: all of our guests tell me the most important thing

629
00:36:58,920 --> 00:37:04,000
Speaker 1: people don't know about your background. I think it's I've

630
00:37:04,000 --> 00:37:08,000
Speaker 1: never had a job. Is true? That? Well, not really.

631
00:37:08,200 --> 00:37:09,440
Speaker 1: I had a job when I was when I was

632
00:37:09,440 --> 00:37:12,120
Speaker 1: seventeen for three weeks. Wait, I know you had a job.

633
00:37:12,200 --> 00:37:15,799
Speaker 1: You were a professional juggler. Oh, now you see, Well

634
00:37:15,800 --> 00:37:17,759
Speaker 1: I didn't have a boss. What I mean, I've never

635
00:37:17,800 --> 00:37:22,040
Speaker 1: really had a boss. I've been running business since since

636
00:37:22,080 --> 00:37:25,920
Speaker 1: I was about nine years old, including including the juggling

637
00:37:26,520 --> 00:37:28,600
Speaker 1: which is on your the bio and your website. I

638
00:37:28,600 --> 00:37:30,040
Speaker 1: saw that and I'm like, this has to be a

639
00:37:30,120 --> 00:37:38,040
Speaker 1: type out and her professional juggler, undercover investigator, first man

640
00:37:38,080 --> 00:37:42,560
Speaker 1: in the UK to obtain an online divorce. These are

641
00:37:42,600 --> 00:37:46,359
Speaker 1: these are rather unusual curriculum vitae details. It's not it's

642
00:37:46,480 --> 00:37:51,680
Speaker 1: it's because, yeah, I'm very bad at taking risks physically.

643
00:37:51,680 --> 00:37:53,919
Speaker 1: I don't take any physical risks. I don't take many

644
00:37:53,960 --> 00:37:56,840
Speaker 1: financial risks. But I'm happy to do any number of

645
00:37:56,920 --> 00:37:59,760
Speaker 1: reputational risks you can throw me. So that. For example,

646
00:38:00,960 --> 00:38:03,120
Speaker 1: two years ago and a friend of mine who works

647
00:38:03,160 --> 00:38:08,080
Speaker 1: for a Channel four TV channel in the UK, he

648
00:38:08,120 --> 00:38:12,280
Speaker 1: wanted he said he wanted to do some investigative undercover

649
00:38:12,440 --> 00:38:16,960
Speaker 1: thing to find out were the political parties in the

650
00:38:17,040 --> 00:38:19,560
Speaker 1: UK corrupt? If you offer the money, how far could

651
00:38:19,600 --> 00:38:24,000
Speaker 1: you get? Now, previously you would have someone pretend to

652
00:38:24,040 --> 00:38:25,640
Speaker 1: be someone, and they said they wanted to go a

653
00:38:25,680 --> 00:38:28,719
Speaker 1: step further in this age of Google, they won't to

654
00:38:28,719 --> 00:38:31,839
Speaker 1: have someone who was real, a real business person, person

655
00:38:31,880 --> 00:38:34,880
Speaker 1: who didn't mind making itself look stupid. And so he

656
00:38:34,920 --> 00:38:38,080
Speaker 1: thought of me, and so so for six months I

657
00:38:38,160 --> 00:38:41,160
Speaker 1: had to have meetings with all sorts of politicians of

658
00:38:41,200 --> 00:38:43,680
Speaker 1: the main parties in the UK and offer them money,

659
00:38:43,719 --> 00:38:46,359
Speaker 1: and offer them money and so and see how what

660
00:38:46,440 --> 00:38:48,920
Speaker 1: would happen, how far far I could go, all without

661
00:38:49,320 --> 00:38:53,280
Speaker 1: entrapping them, so that you know, and all often aren't

662
00:38:53,320 --> 00:38:56,600
Speaker 1: you at risk for committing a crime? In the United States,

663
00:38:56,640 --> 00:39:00,680
Speaker 1: offering an elected official cash to do your bidding is

664
00:39:00,680 --> 00:39:04,040
Speaker 1: a felony through a nuances here indeed, and they had

665
00:39:04,080 --> 00:39:07,160
Speaker 1: teams of lawyers because actually because it was a it

666
00:39:07,239 --> 00:39:09,480
Speaker 1: was a TV program or on the newspaper that the

667
00:39:09,520 --> 00:39:12,840
Speaker 1: TV channels have a very strict rules. So for example,

668
00:39:13,120 --> 00:39:17,160
Speaker 1: we couldn't do I ended up doing lots of hidden

669
00:39:17,160 --> 00:39:20,160
Speaker 1: cameras stuff. But you're not allowed to do that for

670
00:39:20,239 --> 00:39:22,920
Speaker 1: TV unless you've got grounds. So we did a lot

671
00:39:22,920 --> 00:39:26,080
Speaker 1: of prepriority stuff and we got some sort of dodgy

672
00:39:26,320 --> 00:39:29,520
Speaker 1: things which unfortunately never got recorded. And then I went

673
00:39:29,600 --> 00:39:32,319
Speaker 1: in with with the equipment on the had the pen

674
00:39:32,400 --> 00:39:34,640
Speaker 1: in the pocket, the thing in the in the in

675
00:39:34,680 --> 00:39:37,600
Speaker 1: the Did you catch anybody? Yeah, yeah, yeah there was

676
00:39:38,120 --> 00:39:40,760
Speaker 1: some somebody had to resign from the lived down party.

677
00:39:40,800 --> 00:39:43,239
Speaker 1: And so it's great fun. You know, we're coming to

678
00:39:43,239 --> 00:39:45,560
Speaker 1: the house of lords with hidden cameras. How do you

679
00:39:45,600 --> 00:39:47,880
Speaker 1: how do you smuggled hidden cameras into the into the government?

680
00:39:47,960 --> 00:39:49,800
Speaker 1: Now you have to go through a metal detector and

681
00:39:49,880 --> 00:39:53,120
Speaker 1: something else. It's harder to do that, exactly, exactly, all right.

682
00:39:54,440 --> 00:39:57,600
Speaker 1: So so you've never had a boss is the thing

683
00:39:57,640 --> 00:39:59,759
Speaker 1: most people don't know about you. But but I like

684
00:40:00,440 --> 00:40:03,960
Speaker 1: special investigator. Let's talk a little about some of your mentors.

685
00:40:03,960 --> 00:40:06,040
Speaker 1: Who are you? Who are the people who helped guide

686
00:40:06,080 --> 00:40:11,840
Speaker 1: your career a law? Well, the I guess the obviously

687
00:40:11,840 --> 00:40:15,920
Speaker 1: want my mother. My mother, she always she always encouraged

688
00:40:15,960 --> 00:40:19,440
Speaker 1: me to explains the undercover thing, encouraged me to do

689
00:40:19,560 --> 00:40:23,200
Speaker 1: things even though they might have frightened me. Again with

690
00:40:23,239 --> 00:40:26,480
Speaker 1: the caveat nothing physical. I always got out of games

691
00:40:26,480 --> 00:40:31,200
Speaker 1: at school just reputational risk. Yeah, yeah, anything. Yeah, I'm

692
00:40:31,200 --> 00:40:33,400
Speaker 1: going to draw the line of singing in public though. Okay,

693
00:40:33,480 --> 00:40:39,320
Speaker 1: that's so no karaoke, no, but not to carry oke. Um.

694
00:40:39,360 --> 00:40:42,080
Speaker 1: So so so I found that that's very helpful that

695
00:40:42,080 --> 00:40:46,120
Speaker 1: that if something frightens me, I think I've could do it.

696
00:40:46,160 --> 00:40:48,600
Speaker 1: Now it's frightening me, so I've got to do it. Um.

697
00:40:48,640 --> 00:40:51,480
Speaker 1: That's a good philosophy, I think. So. Um. And this

698
00:40:51,520 --> 00:40:53,680
Speaker 1: is way before that, that book Feel of Fear and

699
00:40:53,680 --> 00:40:56,799
Speaker 1: Do It Anyway, way before decades before that. I'm unfamiliar

700
00:40:56,840 --> 00:40:58,400
Speaker 1: with that. You don another book, Feel the Feel of

701
00:40:58,440 --> 00:41:02,239
Speaker 1: Fear and Do It Anyway. Okay, it's some self help,

702
00:41:02,280 --> 00:41:06,040
Speaker 1: but from the eighties, I think another one. There's a

703
00:41:06,080 --> 00:41:11,640
Speaker 1: there's a my tutor and supervisor, John Ockendon. He I

704
00:41:11,719 --> 00:41:14,640
Speaker 1: was very lucky when I was studying mathematics to fall

705
00:41:14,680 --> 00:41:19,440
Speaker 1: in with a group of people mathematicians who who like

706
00:41:19,640 --> 00:41:25,840
Speaker 1: solving real world problems, physical problems, for example, using fluid

707
00:41:25,880 --> 00:41:31,480
Speaker 1: mechanics or things like that. Um, because most most mathematicians

708
00:41:31,520 --> 00:41:34,600
Speaker 1: are quite snobbish about the mathematics, and there's a there's

709
00:41:34,640 --> 00:41:38,960
Speaker 1: a hierarchy amongst mathematicians which is at the very highest

710
00:41:38,960 --> 00:41:41,520
Speaker 1: of the people who work on these these very profound,

711
00:41:41,600 --> 00:41:45,719
Speaker 1: deep problems firms, the you know, theoretical things that you

712
00:41:45,760 --> 00:41:49,279
Speaker 1: know to the man the street, no idea what they're

713
00:41:49,280 --> 00:41:52,160
Speaker 1: on about or what the relevance really but really really

714
00:41:52,200 --> 00:41:56,360
Speaker 1: tough things, tough, you know. Then you moved down the

715
00:41:56,440 --> 00:42:00,239
Speaker 1: ranks and pure mathematicians who work in the apps act.

716
00:42:00,239 --> 00:42:04,360
Speaker 1: Then you've got applied mathematicians and these this group of

717
00:42:04,400 --> 00:42:06,880
Speaker 1: people would pretty much the lowest of the low in

718
00:42:06,920 --> 00:42:09,960
Speaker 1: the sense of the hierarchy, and they used the maths

719
00:42:10,000 --> 00:42:14,840
Speaker 1: to solve real problems. Um, what are they thinking? Exactly exactly?

720
00:42:15,040 --> 00:42:16,879
Speaker 1: But it was great fun and it gets It means

721
00:42:16,920 --> 00:42:19,880
Speaker 1: that that there are a very few people. Actually, I

722
00:42:19,920 --> 00:42:21,680
Speaker 1: can only think of one other person in the world

723
00:42:22,080 --> 00:42:26,680
Speaker 1: who's got had such a breadth of mathematical experience as

724
00:42:26,719 --> 00:42:29,520
Speaker 1: I have. That's why it can be very skeptical about

725
00:42:29,560 --> 00:42:32,520
Speaker 1: the models, because I've seen so much of the mathematical

726
00:42:32,560 --> 00:42:35,440
Speaker 1: world that other people haven't seen. And of course when

727
00:42:35,440 --> 00:42:38,440
Speaker 1: I started working in finance, they had to develop a

728
00:42:38,480 --> 00:42:40,880
Speaker 1: whole new category even lower than the lowest of the

729
00:42:40,920 --> 00:42:44,640
Speaker 1: low for people working in mathematical finance. So I have

730
00:42:44,680 --> 00:42:46,799
Speaker 1: to ask who was the other person who has a

731
00:42:46,840 --> 00:42:52,919
Speaker 1: similar breath of mathematical experience. Take him he's um, yeah,

732
00:42:53,000 --> 00:42:56,879
Speaker 1: he's he's He's worked on a lot of volatility stuff.

733
00:42:56,920 --> 00:42:59,759
Speaker 1: He's very clever guy, but he's had he's had a

734
00:42:59,760 --> 00:43:02,360
Speaker 1: boss many times, so he doesn't have the answer to

735
00:43:02,400 --> 00:43:06,160
Speaker 1: that question. Look what what investors have influenced you? Who?

736
00:43:06,239 --> 00:43:13,920
Speaker 1: Who's affected your approach to looking at markets? Um? Which investors?

737
00:43:15,520 --> 00:43:19,319
Speaker 1: I don't really follow people in that sense. That's a

738
00:43:19,400 --> 00:43:23,480
Speaker 1: good answer everybody else, says Warren Buffett. But not following

739
00:43:23,480 --> 00:43:27,240
Speaker 1: people is a net I'm I'm I have this this

740
00:43:27,239 --> 00:43:30,480
Speaker 1: this talent, which is if if if people say one thing,

741
00:43:30,520 --> 00:43:34,040
Speaker 1: I say the exact opposite, and it comes a point

742
00:43:34,040 --> 00:43:35,880
Speaker 1: where I don't really know what my own opinion is.

743
00:43:36,200 --> 00:43:38,839
Speaker 1: You're just you're just fading the group. I just just

744
00:43:39,120 --> 00:43:43,040
Speaker 1: you know, annoys people, winds them up. But it sort

745
00:43:43,040 --> 00:43:48,840
Speaker 1: of works. The divergent opinion is a variant perception absolutely works.

746
00:43:49,120 --> 00:43:52,759
Speaker 1: Let's talk about everybody's favorite question. Books. What are some

747
00:43:52,840 --> 00:43:57,759
Speaker 1: of your favorite books? Finance? Fact fiction or nonfiction? Finance nonfinance?

748
00:43:57,920 --> 00:44:05,200
Speaker 1: I almost never never ever read nonfiction. Almost never. You

749
00:44:05,280 --> 00:44:08,000
Speaker 1: only read fiction pretty much only ever read see I

750
00:44:08,040 --> 00:44:11,480
Speaker 1: love nonfiction, and I never I love fiction, and I

751
00:44:11,520 --> 00:44:14,040
Speaker 1: never get to it. Okay, so most of what I

752
00:44:14,080 --> 00:44:17,200
Speaker 1: read is is nonfiction. I find that the most of

753
00:44:17,239 --> 00:44:21,960
Speaker 1: the nonfiction that I try to read is sort of obvious,

754
00:44:22,080 --> 00:44:25,160
Speaker 1: and I think I feel like you'd subscribed to one

755
00:44:25,160 --> 00:44:27,799
Speaker 1: of these things where they condense a five page book

756
00:44:27,800 --> 00:44:34,000
Speaker 1: into two page So history, biography, some biographies, but not

757
00:44:34,520 --> 00:44:36,319
Speaker 1: give us a few times. What do you like? So

758
00:44:36,360 --> 00:44:42,200
Speaker 1: if you prefer fiction, prefers fiction, I say one of

759
00:44:42,239 --> 00:44:46,560
Speaker 1: my my all time favorite books is the The Life

760
00:44:46,600 --> 00:44:52,080
Speaker 1: and Opinions of Tristram Shandy Gentlemen mid eighteenth century. I

761
00:44:52,120 --> 00:44:56,319
Speaker 1: think it was It's a totally well, I can't even

762
00:44:56,360 --> 00:44:59,279
Speaker 1: begin to strive it. It's been described as a as

763
00:44:59,320 --> 00:45:05,200
Speaker 1: an unfil unfilmable book because it's it's so I can't

764
00:45:05,200 --> 00:45:07,320
Speaker 1: even begin to describe it. You should read it, Okay,

765
00:45:07,440 --> 00:45:09,759
Speaker 1: I have not. It's it's a fantastic book. But I

766
00:45:09,800 --> 00:45:14,319
Speaker 1: tend to alternate fiction. I tend to alternate between yeah, yeah,

767
00:45:14,360 --> 00:45:18,120
Speaker 1: the Life and Opinions of Tristram Shandy's he's fictional, Tristan

768
00:45:18,320 --> 00:45:24,440
Speaker 1: trist Tristram Shandy. I tend to alternate between what you

769
00:45:24,520 --> 00:45:26,600
Speaker 1: might call a classic book and then something just to

770
00:45:26,840 --> 00:45:30,800
Speaker 1: just to recover from the classic book. And I'm very

771
00:45:30,960 --> 00:45:33,279
Speaker 1: and now very impatient with books. Up until the age

772
00:45:33,320 --> 00:45:35,839
Speaker 1: of thirty, I would say that if I started a book,

773
00:45:35,880 --> 00:45:37,960
Speaker 1: I had to finish it, and you know, you get

774
00:45:38,239 --> 00:45:40,200
Speaker 1: you get over that pretty quick. I got it was

775
00:45:40,360 --> 00:45:42,160
Speaker 1: the greatest thing that ever happened to me, other than

776
00:45:42,320 --> 00:45:46,000
Speaker 1: birth of my children, to realize that I could just

777
00:45:46,040 --> 00:45:50,840
Speaker 1: stop reading a book away. Oh, this is disappointing. So

778
00:45:50,840 --> 00:45:53,520
Speaker 1: so I if a book's more than say, fond of pages,

779
00:45:53,520 --> 00:45:57,239
Speaker 1: I probably won't even buy it. If it if it

780
00:45:57,239 --> 00:46:02,120
Speaker 1: gets rave reviews on Amazon, if it's kind of classical,

781
00:46:02,200 --> 00:46:05,120
Speaker 1: then I might avoid it because I know it's you know, critique,

782
00:46:05,160 --> 00:46:09,880
Speaker 1: a little bit funny, and I'll alternate between maybe some

783
00:46:09,960 --> 00:46:14,600
Speaker 1: classic and something easy. One thing I've discovered relatively recently

784
00:46:14,640 --> 00:46:18,319
Speaker 1: is Lee Child. Now you know Lee Child? Sure, Jack

785
00:46:18,360 --> 00:46:21,440
Speaker 1: Reacher or the fascinating thing about my wife plows through

786
00:46:21,480 --> 00:46:24,200
Speaker 1: those books. Fascinating thing is of all the kind of

787
00:46:24,560 --> 00:46:26,800
Speaker 1: he's not exactly pulp fiction, but it's heading that actually

788
00:46:26,880 --> 00:46:29,600
Speaker 1: he's the only one that middle class people will admit

789
00:46:29,640 --> 00:46:32,879
Speaker 1: to reading. Oh really, Yes. The funny thing is, if

790
00:46:32,880 --> 00:46:35,920
Speaker 1: you've seen the movie, it's a short white guy. But

791
00:46:35,960 --> 00:46:38,359
Speaker 1: in the in the book, it's a tall black guy.

792
00:46:38,400 --> 00:46:43,920
Speaker 1: I don't know why they didn't catch that. Um he's tall, No,

793
00:46:44,000 --> 00:46:46,200
Speaker 1: he's not. I don't think he's black. I thought he was.

794
00:46:46,920 --> 00:46:51,000
Speaker 1: I don't know how many of you read um one

795
00:46:51,120 --> 00:46:55,440
Speaker 1: my wife. My wife's read ten. Okay, well I have

796
00:46:55,600 --> 00:46:57,200
Speaker 1: to have to ask. I could be, but we can

797
00:46:57,239 --> 00:46:59,439
Speaker 1: definitely agree that Tom Cruise as a short white guy

798
00:46:59,719 --> 00:47:03,000
Speaker 1: for sure, to say the least. Um, let's one other book.

799
00:47:03,000 --> 00:47:06,239
Speaker 1: Give me one other thing you've read recently. Something I've read,

800
00:47:06,280 --> 00:47:10,239
Speaker 1: Oh okay, okay, okay and enjoyed, okay, okay, but you

801
00:47:10,239 --> 00:47:12,360
Speaker 1: don't have to have enjoyed I mentioned earlier. J. K.

802
00:47:12,560 --> 00:47:15,880
Speaker 1: Rowling she wrote this, so you under a pseudonym the

803
00:47:15,920 --> 00:47:19,480
Speaker 1: book which one I don't remember, but she's done this

804
00:47:19,480 --> 00:47:25,200
Speaker 1: one called The Cuckoo's Calling something that it's awful. I

805
00:47:25,239 --> 00:47:28,360
Speaker 1: mean really, I mean really. I love the Harry Potter books,

806
00:47:28,800 --> 00:47:31,560
Speaker 1: I love them, but this is diet. It's like she's

807
00:47:31,560 --> 00:47:33,680
Speaker 1: trying to be actually Christie, but it's like three times

808
00:47:33,680 --> 00:47:36,239
Speaker 1: as long as it should be and you just don't

809
00:47:36,239 --> 00:47:39,560
Speaker 1: I finished that just because you know what who done it.

810
00:47:39,680 --> 00:47:41,560
Speaker 1: But by the time you get to the end, you

811
00:47:41,640 --> 00:47:44,840
Speaker 1: realize anybody could have done it, and she just picked randomly,

812
00:47:44,880 --> 00:47:49,000
Speaker 1: so you just did it was rubbish void. Please all right,

813
00:47:49,080 --> 00:47:52,000
Speaker 1: I'm gonna I'm gonna cross that off my list. Um,

814
00:47:52,080 --> 00:47:58,440
Speaker 1: So since you first started following quantitative finance, what's changed

815
00:47:58,480 --> 00:48:00,560
Speaker 1: in the Indians in the industry. What do you think

816
00:48:00,680 --> 00:48:04,080
Speaker 1: is the Is it the rise of technology, is it

817
00:48:04,160 --> 00:48:07,680
Speaker 1: the wholesale adoption? What what is the big shift in

818
00:48:08,080 --> 00:48:12,480
Speaker 1: quantitative finance? Well, the technology you have to mention technology indeed, Um,

819
00:48:13,640 --> 00:48:16,120
Speaker 1: but that's happened everywhere, not just in quantity of finance,

820
00:48:16,120 --> 00:48:19,680
Speaker 1: but also the number of people, the sheer quantities of people,

821
00:48:20,120 --> 00:48:23,239
Speaker 1: and it's taken over finance pretty much. It has, and

822
00:48:23,239 --> 00:48:25,719
Speaker 1: I'm not sure that there are the people are necessarily

823
00:48:26,560 --> 00:48:29,960
Speaker 1: as good as they used to be. That's my sense,

824
00:48:30,160 --> 00:48:33,840
Speaker 1: saluning the talent pool just through sheer numbers or the

825
00:48:33,880 --> 00:48:36,040
Speaker 1: warm people being attracted to it. It's a bit of

826
00:48:36,080 --> 00:48:39,640
Speaker 1: both and also education. I have my own educational program,

827
00:48:39,800 --> 00:48:43,160
Speaker 1: the certificate and Quantitative Finance, but most people go through

828
00:48:43,520 --> 00:48:48,120
Speaker 1: Masters in Finance programs of finance engineering at university and

829
00:48:48,160 --> 00:48:53,120
Speaker 1: they're taught by by people who don't necessarily understand how

830
00:48:53,160 --> 00:48:56,640
Speaker 1: the the how the markets work, or you yourself have

831
00:48:56,680 --> 00:49:00,360
Speaker 1: mentioned between us, we've mentioned verse the psychological side things,

832
00:49:00,800 --> 00:49:05,240
Speaker 1: and you really have to understand human nature really almost

833
00:49:05,280 --> 00:49:08,960
Speaker 1: before you start doing the mathematics. And that's something people

834
00:49:09,040 --> 00:49:13,600
Speaker 1: completely miss. They think the models explain everything. And most

835
00:49:13,640 --> 00:49:15,920
Speaker 1: of these programs people go on that that that taught

836
00:49:15,960 --> 00:49:18,680
Speaker 1: the mathematics, but they're not taught when things break down,

837
00:49:18,760 --> 00:49:22,560
Speaker 1: why things break down, etcetera. So education is pretty bad,

838
00:49:22,600 --> 00:49:25,719
Speaker 1: I think. So tell us about a time you failed

839
00:49:26,040 --> 00:49:28,640
Speaker 1: and what you learned from the experience at the time

840
00:49:28,680 --> 00:49:36,200
Speaker 1: I failed. Um, the there's there's one. This may be

841
00:49:36,239 --> 00:49:38,759
Speaker 1: a bit cheesy, it may not be strictly true, but

842
00:49:38,800 --> 00:49:42,520
Speaker 1: it sounds quite good. There was a time that we said,

843
00:49:42,840 --> 00:49:45,200
Speaker 1: I must have been about thirteen, and there was a

844
00:49:45,400 --> 00:49:47,279
Speaker 1: there was a phase of we were all playing all

845
00:49:47,320 --> 00:49:50,520
Speaker 1: the card games and gambling, you know, just for pennies,

846
00:49:50,760 --> 00:49:53,600
Speaker 1: really pennies, because this is it's an early nineteen seventies.

847
00:49:53,920 --> 00:49:57,560
Speaker 1: And I remember one time making a bet with Billy Jones.

848
00:49:59,120 --> 00:50:02,120
Speaker 1: Bill Jones, he's Billy Jones and my class, Billy Jones,

849
00:50:03,760 --> 00:50:08,040
Speaker 1: another thirteen year old gotcha and it was I think

850
00:50:08,080 --> 00:50:11,279
Speaker 1: I think it's about seven p right. It was all

851
00:50:11,320 --> 00:50:13,360
Speaker 1: the money I had in my pocket seven p and

852
00:50:13,400 --> 00:50:15,919
Speaker 1: I lost this bet. It was a stupid, stupid, stupid bet.

853
00:50:16,480 --> 00:50:18,560
Speaker 1: But I made the bet and I lost it. And

854
00:50:18,760 --> 00:50:20,839
Speaker 1: clearly it's you know, you can see me crying now

855
00:50:20,840 --> 00:50:23,759
Speaker 1: as we speak. It's had an impact on my on

856
00:50:23,840 --> 00:50:26,080
Speaker 1: my entire life, and I think it's it's made me

857
00:50:26,160 --> 00:50:29,520
Speaker 1: very risk of us. Well, we're all, we're all theoretically

858
00:50:29,560 --> 00:50:33,759
Speaker 1: somewhat risk averse, but you suffered a little. Um, it's

859
00:50:33,760 --> 00:50:35,799
Speaker 1: actually helpful. The worst thing in the world that can

860
00:50:35,840 --> 00:50:38,719
Speaker 1: happen to someone is they walk into a casino and

861
00:50:38,800 --> 00:50:41,879
Speaker 1: win money, or their first time they start training they

862
00:50:41,920 --> 00:50:45,880
Speaker 1: make money. You're much better off experiencing laws up front,

863
00:50:46,280 --> 00:50:49,279
Speaker 1: Billy Jones to think, that's right, that's right. Let's let's

864
00:50:49,320 --> 00:50:53,320
Speaker 1: jump to my our last and two favorite questions. Uh,

865
00:50:53,520 --> 00:50:55,359
Speaker 1: these are the ones that seem to have the most

866
00:50:55,400 --> 00:50:59,240
Speaker 1: amount of resonance with people. So if a recent college

867
00:50:59,239 --> 00:51:02,480
Speaker 1: graduate or a millennial would come up to you and say,

868
00:51:02,800 --> 00:51:06,480
Speaker 1: I'm interested in a career in quantitative finance, what sort

869
00:51:06,520 --> 00:51:10,040
Speaker 1: of advice would you give them? Yeah? Yeah, yeah, yeah,

870
00:51:10,400 --> 00:51:14,839
Speaker 1: oh okay, what would it? It's it's what do people

871
00:51:14,880 --> 00:51:17,520
Speaker 1: you usually say for this sort of thing that it

872
00:51:17,760 --> 00:51:21,920
Speaker 1: ranges from. It ranges from I'm the wrong person to

873
00:51:22,000 --> 00:51:24,640
Speaker 1: ask to let me tell you these three things and

874
00:51:24,760 --> 00:51:27,919
Speaker 1: never forget them, and in between okay, well i'm gonna

875
00:51:27,920 --> 00:51:31,160
Speaker 1: get I'm gonna give you another point on the spectrum,

876
00:51:31,160 --> 00:51:37,000
Speaker 1: which is have children early. Okay, postpone the career thing. Really,

877
00:51:37,000 --> 00:51:41,920
Speaker 1: as much as you can do you have children. So

878
00:51:42,000 --> 00:51:45,000
Speaker 1: I'm I'm a late bloomer. I would say have children

879
00:51:45,000 --> 00:51:47,920
Speaker 1: early because most people talk about they want to have

880
00:51:47,960 --> 00:51:50,520
Speaker 1: this to get their careers kickstarted. And when they talk

881
00:51:50,560 --> 00:51:53,799
Speaker 1: about their career, they're always talking about either being a

882
00:51:53,840 --> 00:51:56,200
Speaker 1: lawyer or or a quantity or something. It's not like

883
00:51:56,200 --> 00:51:59,719
Speaker 1: they're curing you can wait, it can wait being a

884
00:51:59,800 --> 00:52:02,359
Speaker 1: lawyer away it really it really upsets me when I

885
00:52:02,400 --> 00:52:07,040
Speaker 1: see people who who just good around children, but they've anyway.

886
00:52:07,160 --> 00:52:08,840
Speaker 1: So that would be to just wait a while and

887
00:52:08,840 --> 00:52:11,880
Speaker 1: have children first and our last and final question, what

888
00:52:11,920 --> 00:52:15,880
Speaker 1: do you know about quantitative finance today that you wish

889
00:52:15,880 --> 00:52:22,600
Speaker 1: you knew twenty five years ago? One thing that well

890
00:52:23,080 --> 00:52:29,760
Speaker 1: I realized halfway through that period would be that's the

891
00:52:29,960 --> 00:52:35,160
Speaker 1: There's a lot of people have names attached to models,

892
00:52:35,200 --> 00:52:37,799
Speaker 1: but usually those models I don't know to offend any

893
00:52:37,840 --> 00:52:41,240
Speaker 1: friends of mine here, but usually those models offender offend away.

894
00:52:41,560 --> 00:52:43,880
Speaker 1: How many people listen to this? No, just it's just

895
00:52:43,960 --> 00:52:47,600
Speaker 1: the two of us. The They have come up with

896
00:52:47,640 --> 00:52:51,719
Speaker 1: a model, and usually it's it's a minor tweak two

897
00:52:52,800 --> 00:52:55,799
Speaker 1: to something to some other model, and it's not necessarily

898
00:52:55,840 --> 00:52:58,080
Speaker 1: a great model in any way, shape or forming. It's

899
00:52:58,080 --> 00:53:00,200
Speaker 1: a bad model tweaked to be something else, to be

900
00:53:00,239 --> 00:53:05,800
Speaker 1: even worse, not even to better. Usually it's tweaked to

901
00:53:05,880 --> 00:53:08,600
Speaker 1: make it easier to use. And you did not know

902
00:53:08,680 --> 00:53:11,200
Speaker 1: that twenty years ago. I didn't realize that that was

903
00:53:11,239 --> 00:53:15,359
Speaker 1: sort of important. Um. And I wonder if I knew

904
00:53:15,360 --> 00:53:17,920
Speaker 1: that twenty five years ago, would I have taken a

905
00:53:17,920 --> 00:53:22,759
Speaker 1: different route. I really hope I wouldn't, because my route

906
00:53:22,760 --> 00:53:24,720
Speaker 1: has always been to do whatever I think is fun.

907
00:53:25,120 --> 00:53:27,240
Speaker 1: And that's why you look at my list of papers

908
00:53:27,280 --> 00:53:32,759
Speaker 1: also of crazy things in that in my list of papers. Um,

909
00:53:32,800 --> 00:53:35,759
Speaker 1: so that this isn't really something I wish I knew

910
00:53:35,800 --> 00:53:37,400
Speaker 1: I would take an advantage of. I would like to

911
00:53:37,400 --> 00:53:39,440
Speaker 1: think I knew it and I decided not to do.

912
00:53:39,480 --> 00:53:41,680
Speaker 1: It's a it's actually it's it's it's Keane's again. I

913
00:53:41,680 --> 00:53:45,840
Speaker 1: think it's Kane's it is. He says it is better

914
00:53:46,000 --> 00:53:52,319
Speaker 1: to fail conventions than to succeed unconventionally, and I very

915
00:53:52,400 --> 00:53:56,880
Speaker 1: much succeeded unconventionally inventionally. I don't know where I'm going

916
00:53:56,920 --> 00:54:00,160
Speaker 1: with any of this, but but it's fascinating. Nonetheless, we

917
00:54:00,280 --> 00:54:04,600
Speaker 1: have been speaking with Paul Wilmot, quantitative finance expert and

918
00:54:04,760 --> 00:54:07,560
Speaker 1: author his his latest book, Paul, what's the name of

919
00:54:07,600 --> 00:54:11,200
Speaker 1: your new book? It's The Money Formula, Don't you Finance?

920
00:54:11,239 --> 00:54:14,480
Speaker 1: Pseudo science and how mathematicians took over the markets. If

921
00:54:14,520 --> 00:54:16,799
Speaker 1: you enjoy this conversation, be sure and look up an

922
00:54:16,840 --> 00:54:19,439
Speaker 1: intro Down an Inch on Apple iTunes and you could

923
00:54:19,440 --> 00:54:21,759
Speaker 1: see any of the other hundred and fifty or so

924
00:54:22,239 --> 00:54:27,640
Speaker 1: such conversations we've had. You could find us on Apple iTunes,

925
00:54:27,880 --> 00:54:32,520
Speaker 1: overcast wherever fine podcasts are sold. I would be remiss

926
00:54:32,600 --> 00:54:35,560
Speaker 1: if I did not think the crack staff that helps

927
00:54:36,000 --> 00:54:40,160
Speaker 1: put this podcast together each week. Medina Parwana is my

928
00:54:40,360 --> 00:54:44,440
Speaker 1: technical producer and audio engineer. Taylor Riggs is our booker

929
00:54:44,520 --> 00:54:47,840
Speaker 1: slash producer. Mike bat Nick is our head of research.

930
00:54:48,400 --> 00:54:51,920
Speaker 1: I'm Barry Ridholtz. You've been listening to Masters in Business

931
00:54:52,360 --> 00:55:00,879
Speaker 1: on Bloomberg radio