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Paul Wilmott Has Some Feelings About Quantitative Models · Masters in Business

0:02
Speaker 1: This is Masters in Business with Barry Ridholts on Bloomberg Radio.
0:09
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
0:20
Speaker 1: this is the podcast for you. UH. He is not
0:24
Speaker 1: only a colleague in Pier of such esteemed quantz as
0:28
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
0:41
Speaker 1: written numerous books on UH, textbooks on quantitative finance. He
0:47
Speaker 1: has helped create the certificate UH for Finance UH quant Work.
0:53
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.
1:05
Speaker 1: There are there are a few people who understood why
1:09
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
1:20
Speaker 1: problematic and and they're going to result in in big problems. Um,
1:25
Speaker 1: They're gonna result in real issues. And he turned out
1:27
Speaker 1: to be not only right generally, but what he was
1:30
Speaker 1: criticizing specifically, turned out to be a large part of
1:35
Speaker 1: of why markets and credit went went through its collapse. So,
1:39
Speaker 1: with no further ado, here is my conversation with Paul Wilmot.
1:47
Speaker 1: I have an extra special guest. His name is Paul Wilmot.
1:51
Speaker 1: How do I describe him? He is a expert in
1:55
Speaker 1: quantitative finance, a researcher and author, a consultant, the professor.
2:00
Speaker 1: He has written over a hundred research papers on mathematics
2:04
Speaker 1: and finance, several best selling and some would say groundbreaking textbooks.
2:09
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,
2:23
Speaker 1: the only one who truly understands what's going on, who
2:27
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.
2:33
Speaker 1: I have to correct you already. I have to correct you.
2:36
Speaker 1: I'm not, and never have been, a professor. I'm a
2:39
Speaker 1: mere doctor. I'm afraid were you a lecturer? I've been.
2:42
Speaker 1: I've had all sorts of research positions in universities, but
2:45
Speaker 1: never reached that exalted. I'll tell you why I drew
2:48
Speaker 1: that conclusion. There is a YouTube video of you describing
2:52
Speaker 1: some of the chapters of some of your textbooks, and
2:55
Speaker 1: you just have a natural professorial air. I have this thing,
2:59
Speaker 1: and I through the assumption that I perhaps should not
3:01
Speaker 1: have drawn. So let's talk a little bit about um,
3:06
Speaker 1: your research, your work you're writing. I have to start
3:10
Speaker 1: out with a quote of yours from one of your papers.
3:13
Speaker 1: The paper published in two thousand was the Use, Misuse
3:17
Speaker 1: and Abuse of Mathematics and Finance. That title alone is
3:22
Speaker 1: a good place to start. But here's the quote that
3:24
Speaker 1: was so prescient. It's clear that a major rethink is
3:28
Speaker 1: desperately required if the world is to avoid a mathematician
3:32
Speaker 1: led market meltdown. That is pretty much dead on. And
3:38
Speaker 1: seven years later we had the quant crash, and a
3:41
Speaker 1: year after that we had the full blown Great Financial Crisis.
3:45
Speaker 1: What were you looking at that gave you such a
3:48
Speaker 1: clear understanding of of the coming storm? Well, I also
3:52
Speaker 1: a few years after that, I wrote that I did
3:54
Speaker 1: specify that I thought it was going to be credit
3:56
Speaker 1: datives that might be the problem. So even more specific, yes,
4:00
Speaker 1: and that that's because of my background, I haven't taken
4:04
Speaker 1: the classical quant route, and I come with a lot
4:09
Speaker 1: of skepticism, and I'm not a great fan of following
4:13
Speaker 1: the herd. So whenever I look at mathematical models, I
4:16
Speaker 1: can pretty much tell you whether the based on any reality,
4:20
Speaker 1: or maybe whether there's any dangers involved with the the
4:23
Speaker 1: hedging or the valuation. And everywhere I looked, I just
4:26
Speaker 1: saw that there were dangers. It was not The models
4:30
Speaker 1: for equities are not too bad in quantitative fires. The
4:34
Speaker 1: models for interest rates are pretty bad, except interest rates
4:38
Speaker 1: don't really do much, certainly at the moment anyway. But
4:41
Speaker 1: credit the models are not only the models really really
4:46
Speaker 1: bad in terms of not matching reality and how they use,
4:48
Speaker 1: but also credit markets are absolute enormous, tremendous. In fact,
4:53
Speaker 1: that also from the same paper, the underlying assumptions of
4:56
Speaker 1: financial models, such as the importance of normal distribution, the
5:00
Speaker 1: elimination of risk, measurable correlations, etcetera, are all incorrect. And
5:06
Speaker 1: and when I read that, I was reminded of the
5:10
Speaker 1: famous George Box quote all models are wrong, but some
5:13
Speaker 1: are useful. And I know that's sort of a bastardization
5:17
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
5:24
Speaker 1: can you tell the ones that are useful from the
5:27
Speaker 1: ones that are dangerous? Well, models in finance are going
5:30
Speaker 1: to be wrong, and it's a definition you can you
5:33
Speaker 1: can perfectly understand what's not told. Um. And as for
5:37
Speaker 1: the word useful, I think you have to ask what
5:40
Speaker 1: is meant by useful and the various ways of looking
5:43
Speaker 1: at this useful. Are they useful in helping you control risk?
5:48
Speaker 1: Are they helpful in allowing you to do more business?
5:52
Speaker 1: Or there are all sorts of different angles, and some
5:55
Speaker 1: of these are sort of conflict with what the man
5:58
Speaker 1: in the street might want. For example, a model might
6:00
Speaker 1: be useful because it's great for marketing, and it you know,
6:03
Speaker 1: it fools the regulators and it's not very good, but hey,
6:07
Speaker 1: it's it's useful if you want to try and start
6:09
Speaker 1: a really big hedge fund. Um. So it depends what
6:13
Speaker 1: you mean. I don't think that's what professor Box was
6:15
Speaker 1: referring exactly. However, exactly, they certainly have conserved useful purposes,
6:20
Speaker 1: albeit perhaps not accurately predicting what the universe may look
6:25
Speaker 1: like exactly. So you hope that when we talk about
6:28
Speaker 1: having a model. It's useful, you'd you'd like to think
6:30
Speaker 1: in terms of, oh, it allows us to manage our
6:33
Speaker 1: risk or hedge our risk something like that, or or
6:37
Speaker 1: produce important new products that are helpful in hedging your
6:43
Speaker 1: business or whatever. But I'm a bit too cynical to
6:47
Speaker 1: think of. So you said you didn't take the usual
6:50
Speaker 1: route into quantitative finance. How what was your route? How
6:53
Speaker 1: did you find your way into quant finance and how
6:57
Speaker 1: does that differ from the typical on Wall Street. Well,
7:01
Speaker 1: you have to go back to my my my childhood. Really,
7:04
Speaker 1: I've always from a very young age run my own
7:08
Speaker 1: businesses at various sizes, and even when I was a
7:13
Speaker 1: mathematician at university doing my doctorate or doing post doctoral work,
7:18
Speaker 1: I was always interacting with the real world, trying to
7:22
Speaker 1: do consultancy, etcetera. And it meant that I saw a
7:26
Speaker 1: lot of different problems from fields outside of finance. In fact,
7:32
Speaker 1: when I started in finance in the late eighties, before that,
7:35
Speaker 1: I didn't even know there was any mathematics in finance.
7:37
Speaker 1: But I'd work for are anautical companies, for steel companies,
7:42
Speaker 1: for all sorts of different businesses where you try and
7:46
Speaker 1: take a physical problem and turn into into something mathematical.
7:50
Speaker 1: And then a colleague introduced me to these things called options.
7:54
Speaker 1: This was in the there was actually just before the
7:57
Speaker 1: seven crash, and just looking into the literature, I found, hey,
8:02
Speaker 1: there's there's real mathematics involved in in in derivatives. And
8:08
Speaker 1: so I started just to apply the same principles of
8:11
Speaker 1: mathematical modeling too derivatives as I had to all these
8:14
Speaker 1: other real world physical processes that I had worked on. Actually,
8:19
Speaker 1: the when I first saw the famous Black Shoals equation, UM,
8:23
Speaker 1: I thought, this is this is great. This is just
8:25
Speaker 1: like second year undergraduate mathematics. UM. These days apparently you're
8:30
Speaker 1: supposed to have a PhD. But actually that's that's a
8:33
Speaker 1: that's a lot of nonsense. UM. Most quant finances just
8:36
Speaker 1: second year undergraduate maths. Some of the book is really
8:39
Speaker 1: quite fascinating, and you start out with a history of
8:43
Speaker 1: investment theory, and I thought, skipping from the south Sea
8:48
Speaker 1: Bubble to Adam Smith to the efficient market hypothesis, what
8:52
Speaker 1: is the connective tissue between all three of those major
8:56
Speaker 1: events in the history of finance. Well, when I think
9:00
Speaker 1: you start to see is some going from just people's
9:04
Speaker 1: personal experience through too trying to lay down some principles
9:08
Speaker 1: and then trying to quantify those principles. So up to
9:11
Speaker 1: the period now, the modern era, when pretty much everything
9:14
Speaker 1: in finance is all quantified, everything is in terms of
9:18
Speaker 1: expected returns and volatility and quantities like that, it could
9:22
Speaker 1: be reduced to mathematical models. Everything has become mathematics. Yes, right,
9:26
Speaker 1: So so then I always thought of the random walk
9:29
Speaker 1: theory as a sort of squishy psychological claim that hey,
9:34
Speaker 1: you know some maybe some people can beat the market,
9:38
Speaker 1: but you're probably not one of them, and you probably
9:41
Speaker 1: can't pick them. And then once we bring in taxes
9:44
Speaker 1: and costs and everything else, you're better off just indexing.
9:47
Speaker 1: But you relate the whole concept of random walk to
9:52
Speaker 1: probability theory. What ties these two together? Well, if you
9:56
Speaker 1: go back to the different types of analysis that people
9:58
Speaker 1: do in in finance, you've got fundamental analysis, which is
10:02
Speaker 1: about trying to figure out, by looking at the company report,
10:06
Speaker 1: company's reports and the directors, et cetera, the product, how
10:10
Speaker 1: much is this company really worth? Now, that's very difficult
10:14
Speaker 1: to do that, That's a lot of analysis is required. Um,
10:17
Speaker 1: I mean, can you read balance sheets? I can't read
10:19
Speaker 1: balance sheets for exactly exactly so, but the real problem
10:24
Speaker 1: is is that you're not trying to predict what the
10:27
Speaker 1: the the market value of the company should be, because
10:31
Speaker 1: you're trying to predict what other people the people are
10:33
Speaker 1: buying certain shares, think those famous beauty exactly. So that
10:37
Speaker 1: you move on to fundamental that sorry, move on from
10:40
Speaker 1: fundamental to technical analysis, which is exact opposite, which is
10:43
Speaker 1: really easy. You draw a chart to some trend lines
10:46
Speaker 1: and some patterns. Problem is that sort of thing, statistically speaking,
10:50
Speaker 1: doesn't work. The scientific evidence suggested the vast majority of
10:54
Speaker 1: that doesn't work. So the great break through the fifties
10:57
Speaker 1: six seventies was to say, let's throw that away. Let's
11:00
Speaker 1: just say we can't predict things. We've got the concept
11:04
Speaker 1: of the efficient market hypothesis as a background for this,
11:08
Speaker 1: but let's just say it's like tossing a coin. Now,
11:10
Speaker 1: is it a tossing a biased coin? What's the chance
11:13
Speaker 1: of heads, what's the chance of tail? So you put
11:15
Speaker 1: numbers to these probabilities and the sort of from that
11:20
Speaker 1: we get lots of theories about derivatives valuation. And once
11:24
Speaker 1: you've got derivatives, you can value derivatives, you can create
11:27
Speaker 1: new instruments, and the new instruments what might require new math,
11:30
Speaker 1: new mathematics. So you have this snowballing effect where you've
11:34
Speaker 1: got the mathematics increasing and the number and type of
11:37
Speaker 1: products similtarency increasing. So there's a quote in the book
11:41
Speaker 1: I really like, and I'm going to mangle this a
11:43
Speaker 1: little bit. No investment strategy based on mainstream finance theory
11:48
Speaker 1: can protect protect investors from market wide crashes. First, is
11:53
Speaker 1: anyone really suggesting, Hey, our strategy is going to protect you?
11:56
Speaker 1: Is that? I guess that's naive of me to uh
12:00
Speaker 1: to ask. But do people really believe, oh, here's the
12:04
Speaker 1: magic seven like portfolio insurance that will protect us. I'm
12:09
Speaker 1: sure there are some things you can do. Um, there
12:11
Speaker 1: are all sorts of nuances here. I remember Nassy, my
12:14
Speaker 1: dear pal, Nassim Talent, telling me about his um the
12:18
Speaker 1: way he pitches this this idea, and that is to say,
12:22
Speaker 1: it's insurance. But it's not a portfile, it's not an investment.
12:25
Speaker 1: It's insurance. You're gonna lose money. In fact, you hope
12:27
Speaker 1: you'll lose money because if you make money on this
12:29
Speaker 1: insurance is because your house has burnt down, which you
12:32
Speaker 1: don't want to have happened. So that's quite that's a
12:36
Speaker 1: psychological way of looking at this, getting the putting in place,
12:40
Speaker 1: the the the protection as a as a as a
12:43
Speaker 1: cost rather as an investment. Um. There are some things
12:48
Speaker 1: you can do. Of course, the famous portfolio dynamic portfoileon
12:52
Speaker 1: insurance of the of the eighties was it was a
12:54
Speaker 1: classic example of a feedback effect, the positive feedback effect,
12:59
Speaker 1: that the the very action of trying to protect your
13:02
Speaker 1: portfolio is what caused made which is a beautiful concept.
13:07
Speaker 1: What what I don't understand is who thought, oh, if
13:10
Speaker 1: the market is crashing, I'll go out and buy some
13:12
Speaker 1: pots and that will protect me. Summing it up that way,
13:15
Speaker 1: how did anybody think that that model was valid? And
13:19
Speaker 1: granted there's a little hindsight bias here, but still it
13:23
Speaker 1: sounds so obvious after the fact that that would not work, right.
13:28
Speaker 1: I don't know. I don't know the I think the
13:30
Speaker 1: I think in all of this that a key thing
13:33
Speaker 1: you've always got to remember is that the psychology of
13:35
Speaker 1: the markets and that whatever the mathematical models say, somehow
13:40
Speaker 1: human beings in this particular field do try to do
13:43
Speaker 1: manage to mess up the models. So here's another mangled quote.
13:49
Speaker 1: Bankers offering complex financial products don't always understand the risks,
13:55
Speaker 1: and that when a bank goes bust, stock markets collapse,
13:59
Speaker 1: house prices tumble, it's your bank account, your shares, and
14:02
Speaker 1: your home equity that suffers. Right, Well, it's certainly true
14:07
Speaker 1: in my experience that bankers and especially regulators, I have
14:11
Speaker 1: to say, I do not know as much as they
14:15
Speaker 1: ought to. Um, if they were doctors and they didn't know,
14:19
Speaker 1: you know, bleachers do a little bleeding exactly, or you
14:23
Speaker 1: know which side is the heart? Aren't you know that
14:25
Speaker 1: sort of thing, then they be lawsuits flying. But bankers,
14:29
Speaker 1: in my experience, there's a lot of herd mentality within
14:33
Speaker 1: bankers and that works for them. That's how that's how
14:36
Speaker 1: they make money is from from pretending there are no
14:40
Speaker 1: risks and although they may deep down they may realize
14:44
Speaker 1: there are risks. And regulators I haven't met that many regulators.
14:48
Speaker 1: I don't seem to bump into them very often, maybe
14:51
Speaker 1: because the things I've said about them in the past,
14:53
Speaker 1: but my experience is often they're very good at book learning.
14:57
Speaker 1: You know, they've they've got that they've read the math books.
15:00
Speaker 1: They know the models, but they're sorely lacking in street
15:03
Speaker 1: smarts in my experience, so they don't really understand how
15:06
Speaker 1: the world works. So you have in the past suggested
15:10
Speaker 1: a solution to the problem of both bankers that don't
15:15
Speaker 1: understand risk and regulators that don't know how to regulate
15:18
Speaker 1: it is a tobin tax on each transaction, a point
15:23
Speaker 1: zero zero eight percent tax on any buy sell, any
15:28
Speaker 1: trade whatsoever. And how would that work and how would
15:31
Speaker 1: that protect markets? You're very good with your numbers. The
15:35
Speaker 1: the idea of the Tobin tax is to stop very
15:41
Speaker 1: high frequency trading because, uh, you then lose with the
15:45
Speaker 1: high frequency. The problem with one of the problems with
15:47
Speaker 1: high frequency trading is that you lose the connection between
15:52
Speaker 1: the value of a share company and the price. It
15:56
Speaker 1: just becomes about, you know, the these these some time
16:00
Speaker 1: series of numbers, and it could be anything. It doesn't matter.
16:03
Speaker 1: But there's a company here and there are people whose
16:06
Speaker 1: jobs on the line. Let's talk a little bit about
16:10
Speaker 1: the time you spent at Kayasa Capital Capital two people
16:15
Speaker 1: separated by a common language, So you were a founding
16:17
Speaker 1: partner of the volatility arbitrage hedge funds, which was running
16:23
Speaker 1: about two million dollars just on the two million and so,
16:26
Speaker 1: so tell us a little bit about volatility arbitrage, which
16:30
Speaker 1: is sort of ironic in these days of almost no volatility.
16:36
Speaker 1: It's interesting that that it's very difficult I find I
16:39
Speaker 1: found to predict the direction a stock is going to
16:42
Speaker 1: go up put down. But it seems to be easier
16:46
Speaker 1: and there are statistical reasons for this, actually, but it
16:49
Speaker 1: seems to be easier to to forecast volatility. How much
16:53
Speaker 1: noise there in there is in a stock. So you
16:55
Speaker 1: put a counterintuitive though, that it's harder to find the
16:59
Speaker 1: signal and to use you the noise. Yes, I can
17:01
Speaker 1: see that, but at the same time, you can see
17:04
Speaker 1: what the market is sort of thinking volatility is because options,
17:09
Speaker 1: the valuation of options hinges upon estimation of volatility. So
17:14
Speaker 1: you can say, oh, here's a core option, what volatility
17:17
Speaker 1: are they plugging into? The famous black shows formed to
17:19
Speaker 1: give that that price that you see in the market,
17:22
Speaker 1: and so you can you can back out a thing
17:23
Speaker 1: called implied volatility, which is sort of a bit like
17:26
Speaker 1: what the market thinks. So you look around for when
17:30
Speaker 1: is the implied volatility different sufficiently different? From your forecast
17:35
Speaker 1: volatility and if they're different, and if you just happen
17:38
Speaker 1: to be right, then you can make some money. So
17:42
Speaker 1: that complex it's it's the purest sort of volatility arbitrage.
17:48
Speaker 1: I'm betting my forecasts against the markets forecasts essentially is
17:54
Speaker 1: what is what it amounts to? So in not so
17:56
Speaker 1: efficient market, not so efficient, not so efficient. I don't
17:59
Speaker 1: think believes in efficient markets. Who's listening to this program
18:02
Speaker 1: to UM? I think there are a decent number. It's
18:06
Speaker 1: it's a really interesting point because I think there are
18:09
Speaker 1: a number of people who say, um. Look, cliff as Nests,
18:14
Speaker 1: who runs a q R Gene Fama was his PhD
18:18
Speaker 1: dissertation advisor and Fama's credit. He said, hey, if you
18:22
Speaker 1: want to go out and prove factors work, I'll I'm
18:25
Speaker 1: happy with that. So we know the market is kind
18:28
Speaker 1: of a sort of mostly eventually efficient. Is that? Is
18:32
Speaker 1: that a fair way to describe it? Not necessarily. It
18:35
Speaker 1: depends where there's any mechanism for for removing any efficiency.
18:40
Speaker 1: People just say oh that, you know, people say things
18:42
Speaker 1: like the markets always right, and well, we know that's
18:45
Speaker 1: certainly not How how could the market be twenty three
18:49
Speaker 1: percent lower the day after the crash. Was it right
18:52
Speaker 1: the day before? Was it right the day after? Is
18:54
Speaker 1: it wrong both times? It's kind of hard to say
18:57
Speaker 1: the market is always right right. Well. One then example
18:59
Speaker 1: I I sometimes give my students is about uses the
19:03
Speaker 1: idea of car insurance. For example, suppose you've got a
19:05
Speaker 1: twenty thousand dollar car and your annual car insurance is
19:09
Speaker 1: let's new simple numbers a thousand dollars. A quant would say, oh,
19:13
Speaker 1: it's a twenty thousand dollar car. It's a thousand dollar
19:15
Speaker 1: car in shoans are that means must be a five
19:16
Speaker 1: percent chance of crashing. And because they're missing out from
19:19
Speaker 1: that five fence the fact that the insurance company needs
19:22
Speaker 1: to make a profit, I'm not doing things all sorts
19:25
Speaker 1: of things. So so the way your typical quant or
19:29
Speaker 1: um Chicago finance professor looks at things is in a
19:34
Speaker 1: very pure way that sort of misses all the fun.
19:37
Speaker 1: What I think, and as I've said before, I've been
19:40
Speaker 1: running businesses since I was a child, virtually, and the
19:44
Speaker 1: business side of things is what makes things interesting. And
19:48
Speaker 1: so when we approach this sort of going back to
19:51
Speaker 1: volatility arbitrage, you'd be amazed at if you look at
19:56
Speaker 1: the literature. The there must be tens of thousands of
20:00
Speaker 1: papers which talk about how to how to back out
20:07
Speaker 1: from from market prices, what the implied vault inty is,
20:10
Speaker 1: and how they assume that the implied volatility is right,
20:13
Speaker 1: that the market knows, the market has a crystal ball,
20:16
Speaker 1: it knows what vaults is going to be. Must be
20:18
Speaker 1: tens of thousands. How many papers are there which say, well,
20:21
Speaker 1: you know what, what if the market is wrong and
20:24
Speaker 1: the volatility, the real volatility is different from implied volatility,
20:28
Speaker 1: how can you make money? Then that's the underlying strategy,
20:30
Speaker 1: that said Catholic. That must must be half a dozen papers,
20:36
Speaker 1: and surely that's the most important thing. We've got an option.
20:39
Speaker 1: The question is what am I going to do with this?
20:42
Speaker 1: I could hedge with it, or I could make money
20:44
Speaker 1: from it, and then just half a dozen papers on
20:47
Speaker 1: how to make money. So so, how did the hedge
20:50
Speaker 1: fund do? The hedge fund was did did wonderfully for
20:54
Speaker 1: it's its lifespan of three years, which I believe is
20:57
Speaker 1: is the average lifespan of a hedge fund. And and
21:01
Speaker 1: so at the end of the three years, you basically
21:03
Speaker 1: tapped out and said I'm going to go back to writing. Well,
21:06
Speaker 1: I I learned a lot from that that hedge fund,
21:08
Speaker 1: a lot of things about not just about hedge funds,
21:11
Speaker 1: but also about about myself. I found that very interesting
21:16
Speaker 1: because the Hedgeman was based in New York um and
21:19
Speaker 1: where were you located was located in London, so there
21:22
Speaker 1: was this five hour time difference, and just as things
21:25
Speaker 1: were getting interested New York was when I was sitting
21:28
Speaker 1: down to dinner or onting to go to bed or
21:30
Speaker 1: something like that. So that was kind of frustrating. Let's
21:32
Speaker 1: talk about the Wilmot business model. So you run the website,
21:37
Speaker 1: you have a print magazine, you have a Paul and
21:41
Speaker 1: Dominic quant recruitment, the book, which we all know is
21:45
Speaker 1: books or giant money makers. What is your business model
21:49
Speaker 1: as quant expert. Well that the Poul and Dominics doesn't
21:53
Speaker 1: exist anymore. That's but the and also there was the
21:57
Speaker 1: we talked about the world's largest quant website. It's also
22:00
Speaker 1: apparently the world's most expensive magazine. I've been told. I
22:03
Speaker 1: don't know that's true or what's the magazine costs. It's
22:07
Speaker 1: not six hundred dollars for six issues. I don't think
22:10
Speaker 1: that's terribly expensive, and I looked it up. It's not
22:13
Speaker 1: actually this And if you look at some of the
22:14
Speaker 1: like the medical journals and stuff, these are thousands of
22:17
Speaker 1: In fact, there was a big article not too long
22:19
Speaker 1: ago about what used to be academic research now sells
22:23
Speaker 1: for thousands of dollars and there's two companies that have
22:26
Speaker 1: sucked up all these formally free publication. And that that
22:29
Speaker 1: was a quote from Esquire magazine. So I'd just like
22:31
Speaker 1: to repeat that it's not very useful in marketing, though
22:35
Speaker 1: there was until recently, well there still is. The Certificate
22:38
Speaker 1: and Quantitative Finance that that is is the world's largest
22:41
Speaker 1: high level quant education with a company called seven City.
22:45
Speaker 1: This CQF was was founded with a company called seven
22:47
Speaker 1: City Learning two thousands three was it. But it was
22:52
Speaker 1: sold up to Fitch about three or four years. So,
22:56
Speaker 1: but what is the business model? The business model, Well,
22:58
Speaker 1: my whole business model all my life has been do
23:01
Speaker 1: something which is fun and then and then I accidentally
23:05
Speaker 1: or I always have this urge to turn things into
23:07
Speaker 1: into businesses. It's not a it's not a greed thing.
23:10
Speaker 1: It's an enthusiasm. Think. It's almost like I've got some hobby.
23:13
Speaker 1: I'd like to make it public. It becomes more long
23:16
Speaker 1: last thing and sustainable if there's a revenue stream behind it. Yeah,
23:20
Speaker 1: I don't think of it like that, you know, it's
23:21
Speaker 1: just sort of is something in my DNA that says,
23:24
Speaker 1: you know, for example, I've started to learn the ukulele
23:27
Speaker 1: a few years ago, and I just know sometime in
23:30
Speaker 1: my life there will be a time when I'm on
23:32
Speaker 1: a stage and people are paying money to listen to
23:34
Speaker 1: me play the ukule I'm going to take the other
23:35
Speaker 1: side of that trade. You should hedge that bet because
23:37
Speaker 1: I don't see that happen. No, No, like, yeah, I'm
23:39
Speaker 1: pretty good, I'm getting I'm getting that, I'm getting. No,
23:41
Speaker 1: it's not. It has to do with your skill set.
23:44
Speaker 1: It has to do with the demand ukulele. Before you'd
23:47
Speaker 1: be surprised. I would be surprised. It is the instrument.
23:50
Speaker 1: All the schools are taking up ukule because it's I'm
23:52
Speaker 1: on the other side of that trade. Also, what did
23:55
Speaker 1: you learn at school? Trombone and piano. I want to
23:58
Speaker 1: do the trombone, but they would hate it the trombone
24:01
Speaker 1: because at least the piano there's a distinct key for
24:03
Speaker 1: each note the trombone. As a ten year old, you
24:06
Speaker 1: have to have an year, no when you're more or less.
24:10
Speaker 1: And I had a pretty good year, but not that good.
24:12
Speaker 1: But they at schools in the UK, at least they
24:15
Speaker 1: traditionally learned the recorder. You know, three we all did that, okay,
24:20
Speaker 1: But now the ukulele is replacing the recorder because really
24:23
Speaker 1: because it's it's easier, you know, you can really really
24:26
Speaker 1: ten minutes. I should have brought it. I'm sorry you didn't.
24:30
Speaker 1: That would have been next time, next time. Why do
24:32
Speaker 1: we get into this business models? Right, you're gonna you're
24:35
Speaker 1: gonna monetize ukulele and performance. But but I've always been
24:39
Speaker 1: like that, and so I've going back to the late eighties.
24:44
Speaker 1: I was doing some research with with colleagues at university
24:49
Speaker 1: and we thought, let's let's why do we give some
24:52
Speaker 1: courses on teach people in the city, and they just
24:55
Speaker 1: was phenomenally successful. So we set up a business which
24:58
Speaker 1: and then and then we turn into a book and
25:01
Speaker 1: it was it was suggested, why don't we self published
25:03
Speaker 1: this book? So rather than just could hand the book
25:05
Speaker 1: over to a publisher, we actually published it in printing, etcetera.
25:08
Speaker 1: And and my mother and my stepfather were in charge
25:12
Speaker 1: of sales. So my poor mother. We we put in
25:15
Speaker 1: the advertising. We'd say twenty four hour facts hotline, um,
25:19
Speaker 1: so her phone would be ringing. And how did the
25:21
Speaker 1: book sell? Oh? Incredibly well it was. It was. It
25:24
Speaker 1: was just unbelievably considered. We did hardly any advertising. This
25:28
Speaker 1: is before Amazon. It did it incredibly well by word
25:31
Speaker 1: of mouth and even before the book came out, people
25:33
Speaker 1: talking about it. People I think the things. People were
25:36
Speaker 1: paying something like tund fifty dollars for a hard copy
25:41
Speaker 1: that you had to cut and paste the pictures into
25:44
Speaker 1: the text. And I don't mean cutting paste like you do,
25:46
Speaker 1: you know with the with the mouse, physically scissors and glue.
25:49
Speaker 1: So they would get two piles. They get the with
25:52
Speaker 1: without the images, and their separate parlem with the images,
25:55
Speaker 1: and they paid two fifty dollars to stick the two together.
25:57
Speaker 1: And this was on quantitative finance Fast and this was
26:01
Speaker 1: way early in the rise of the quants. This is
26:05
Speaker 1: this is ninety two. Okay, So so there were there
26:08
Speaker 1: are a third fourth inning. You know, there are a
26:10
Speaker 1: few books out there, but this one was a different
26:12
Speaker 1: style of book. And then from them it turned into
26:15
Speaker 1: software companies, more publishing, et Ceteraally, just were you selling
26:19
Speaker 1: this in the UK in the US worldwide? Well, yeah,
26:22
Speaker 1: my mother and the fax machines. Yea, yeah worldwide. We
26:25
Speaker 1: we we we we we had. We would give courses
26:29
Speaker 1: in down in Wall streets and we would bring over
26:33
Speaker 1: from the UK boxes full of these books to get
26:35
Speaker 1: to sell to people on on the on the courses
26:38
Speaker 1: and you'd sell them all out, sell them that every time. Um,
26:41
Speaker 1: that's pretty fascinating. Do you still self published because you
26:44
Speaker 1: have a number of books to your credit? This is Wiley?
26:48
Speaker 1: But what the money formula? But the other quantitative finance
26:52
Speaker 1: books are as as you mentioned earlier, You're hinted earlier
26:57
Speaker 1: you don't make a lot of money usually from from books,
26:59
Speaker 1: unless of J. K. Rolling or someone. Um, So that
27:03
Speaker 1: there came a point where the it was there are
27:06
Speaker 1: other things that were more profitable, more successful, and the
27:10
Speaker 1: books became more of a hobby or a publicity marketing
27:14
Speaker 1: tool really or financial models making people overconfident and reckless
27:20
Speaker 1: in how they behave in the mark. Well, I don't
27:21
Speaker 1: know where it's true or not, but that I suddenly
27:23
Speaker 1: heard that that actually the accidents involving pedestrians and cyclists
27:28
Speaker 1: have gone up for exactly saying that because no, no, no, no,
27:31
Speaker 1: because of because people feel safe in their car and
27:33
Speaker 1: they've got all these these these cushions and whatnot, they're
27:37
Speaker 1: less likely to be injured. But people drive faster and
27:40
Speaker 1: so they kill more pedestrians. So my assumption is their
27:43
Speaker 1: texting when they're hitting bi but the or the pedestrians
27:47
Speaker 1: are playing with their phones. No, it could be, could be,
27:50
Speaker 1: could be pedestrians. An't they awful? Um? It depends. It's subjective.
27:53
Speaker 1: When you're driving a car, they're awful. And when you're
27:56
Speaker 1: a pedestrian. Look at these crazy drives cyclists, cyclists in
28:00
Speaker 1: Manhattan horrifying. You know you can cut all that out.
28:03
Speaker 1: We get to try that's great stuff. The yeah, that
28:07
Speaker 1: what you should do. Of course, it's just stick knives
28:10
Speaker 1: in the in the in the steering wheel period of all.
28:12
Speaker 1: And then you drive very very safely. Then we just
28:16
Speaker 1: get a middle thing over your chest and can you do?
28:20
Speaker 1: What can you do? It's um and you'd have to
28:22
Speaker 1: make sure it doesn't pop up and catch you any
28:24
Speaker 1: but but it's it's it's I do again. Going back
28:28
Speaker 1: to regulators regulators don't. They don't understand. There's a story
28:33
Speaker 1: I could tell, but it involves it's far too technical,
28:36
Speaker 1: but there's some some practice that people do in banks
28:39
Speaker 1: called calibration. You don't need to know this, but it's
28:42
Speaker 1: something that superficially it looks like it's a good mathematical model,
28:47
Speaker 1: but what it actually does is it it hides risk.
28:50
Speaker 1: It makes it any model risk that's there gets hidden
28:54
Speaker 1: by this act of calibration. And when I've spoken to regulators,
28:58
Speaker 1: they don't really really understand that what these people who
29:01
Speaker 1: are calibrating it doing is hiding risk. And said they
29:03
Speaker 1: go around telling the thanks you must calibrate, which is
29:06
Speaker 1: like saying you must hide risk, which is really not
29:09
Speaker 1: what regulators are supposed to be doing. So let's talk
29:11
Speaker 1: about your wish list when it comes to better regulations,
29:16
Speaker 1: better education. And I believe in the book you call
29:20
Speaker 1: it negative bonuses. So what would your what would your
29:23
Speaker 1: wish list be? Well, obviously more people should go to prison,
29:28
Speaker 1: bonuses about some people should, that's the start. The bonus
29:33
Speaker 1: is taken away. But lots of people get paid stupid money.
29:36
Speaker 1: I like CEOs of companies getting stupid money, especially in
29:39
Speaker 1: the because they went to the same school as some
29:42
Speaker 1: other CEO. That's pretty A number of people have blamed
29:46
Speaker 1: the compensation consultants for using se as their frame of reference. Oh,
29:52
Speaker 1: let's compare to the seventy I all of other CEOs.
29:55
Speaker 1: Why why why are you assuming you're better than most
29:58
Speaker 1: of the other CEOs out there, So you have this
30:00
Speaker 1: horrible upward spiral. It's terrible. It's terrible, but also not
30:04
Speaker 1: for them, but for the rest of Yeah, yeah, yeah, yeah.
30:06
Speaker 1: In the in the in the book, we talk about
30:09
Speaker 1: having something like the you know, the f A A
30:12
Speaker 1: who regulated planes, something like that for banking and complex
30:17
Speaker 1: financial products. The analogy is that whenever you have a
30:20
Speaker 1: new plane, it has to be tested, etcetera, etcetera. Well,
30:23
Speaker 1: if you have a new financial product, it has it
30:24
Speaker 1: should be tested by some people. Um, you can't have
30:27
Speaker 1: too many of these of this specific you can't have
30:29
Speaker 1: too much concentration risk, just like you can't have too
30:32
Speaker 1: many planes landing the same up at the same time.
30:34
Speaker 1: So in all sorts of analogies we draw between the
30:37
Speaker 1: the airline business and the world of banking. And of
30:41
Speaker 1: course it's an international thing. Planes take off one country,
30:44
Speaker 1: land another country, well, how hard can it be to
30:47
Speaker 1: have an international perspective to the banking regulation? And I
30:52
Speaker 1: have to ask, since you brought it up earlier, what
30:55
Speaker 1: motivated you and Professor Emmanuel German at Colombia and formally
30:59
Speaker 1: of Alman Sachs to write the Financial Modelers Manifesto. Well,
31:05
Speaker 1: he and I both had pretty much the same um
31:10
Speaker 1: I thought at the same time about and we both
31:13
Speaker 1: been very skeptical about mathematical models. There's one subtle difference though,
31:19
Speaker 1: and that is Emmanuel Down hasn't his name attached to one,
31:23
Speaker 1: which means he's kind of got a bit of skin
31:24
Speaker 1: in the game that which I haven't got. So I
31:27
Speaker 1: can be slightly more, you know, than he can if
31:30
Speaker 1: you if you catch my drift anyway. Um, but both
31:33
Speaker 1: of us are very skeptical about about the what quantz
31:38
Speaker 1: doing risk managers in banks and hedge funds. And it
31:42
Speaker 1: was shortly after the crisis that we we both had
31:45
Speaker 1: apparently independently, thought we should write something, which is, we
31:50
Speaker 1: teamed up and it was a combination of Karl Marx
31:53
Speaker 1: and the hippocratic oath that we've We've we've got our
31:57
Speaker 1: inspiration first, do no harm. Yes, we have been speaking
32:01
Speaker 1: with Paul Wilmott. He is a expert in quantitative finance.
32:06
Speaker 1: We love your comments, feedback and suggestions right to us
32:10
Speaker 1: at m IB podcast at Bloomberg dot net. Check out
32:14
Speaker 1: my daily column on Bloomberg View dot com. You could
32:17
Speaker 1: follow me on Twitter at Rid Halts. I'm Barry Ri Halts.
32:21
Speaker 1: You're listening to Master's in Business on Bloomberg Radio. Welcome
32:39
Speaker 1: to the podcast, Paul, Thank you so much for doing this.
32:41
Speaker 1: I'm I've been looking forward to having this conversation. I
32:44
Speaker 1: wish I can keep you for another couple of hours. Um,
32:47
Speaker 1: but I have to get to at least two questions
32:50
Speaker 1: before I jump to my standard questions. This is This
32:54
Speaker 1: is from the book The notional value of derivatives in
32:59
Speaker 1: was one point to quadrillion dollars. Is that correct? Yes?
33:05
Speaker 1: Are we still in any sort of risk from a
33:08
Speaker 1: blow up in derivatives? My my assumption is most of
33:13
Speaker 1: that one point to quadrillion offsets itself. What is the
33:18
Speaker 1: actual amount that has us at any sort of risk?
33:24
Speaker 1: It's it's hard to tell, because yes, that that is notional,
33:27
Speaker 1: So it could be that you might have that could
33:29
Speaker 1: be one point two million million quadrillion in swaps, so
33:33
Speaker 1: that it might just be one percent of that is
33:35
Speaker 1: a risk um which I hustled, not nothing. However, you
33:40
Speaker 1: can exactly however you can calculate it's it's too large
33:44
Speaker 1: because it's that one point to quadrillion. If you can
33:46
Speaker 1: take of zero two, you're still left with something which
33:50
Speaker 1: is which is and the world the GDP of the world,
33:53
Speaker 1: it's some like fifty trillion. So's it's the wrong way around.
33:56
Speaker 1: It's almost like finance is supposed to be in the
33:58
Speaker 1: service industry, but it's almost like people are making chairs
34:01
Speaker 1: and tables and whatever just to keep the finance industry going.
34:06
Speaker 1: Because the numbers, the tail is working one percent is
34:09
Speaker 1: what a hundred and twenty billion? Is that what we're
34:11
Speaker 1: talking about off of one drillion, twelve trillion, twelve trillion? Yes, yes,
34:18
Speaker 1: and yes, So that's still that's still a quarter of
34:21
Speaker 1: the world's GDP even if you take off two zero,
34:23
Speaker 1: the numbers, numbers are just mind bargling. And there are
34:29
Speaker 1: all these Since you were running a volatility hedge fund,
34:32
Speaker 1: one of the questions I forgot to ask you before,
34:34
Speaker 1: there are currently all these short volatility products outright there.
34:39
Speaker 1: What what are your thoughts on news. Well, in our
34:42
Speaker 1: experience that it was definitely the case that options tended
34:47
Speaker 1: to be overpriced. So in terms of of of a strategy,
34:50
Speaker 1: he would be selling vanilla options because they're overpriced. But
34:54
Speaker 1: the problem is selling options is when you have some
34:57
Speaker 1: extreme events, then you just blow up. So you need
35:00
Speaker 1: to make sure that you've got plenty of tail protection
35:03
Speaker 1: in case what happens, which is something that we we
35:05
Speaker 1: specialized in, and that's why your friends with no seemed
35:08
Speaker 1: to leave. Indeed, we get way back. I recall having
35:12
Speaker 1: a lunch with him. It was just three of us
35:16
Speaker 1: in the middle of the financial crisis. That had to
35:18
Speaker 1: be the summer of oh eight before A, I, G.
35:22
Speaker 1: And Lehman blew up and we were I can't even
35:25
Speaker 1: tell you. The restaurant Callari ta Verna on Street. Normally
35:30
Speaker 1: you have to make reservations a week in advance. We
35:32
Speaker 1: walk in at one o'clock, it's empty. We sit right down.
35:35
Speaker 1: The three of us were just laughing away. Um, he's
35:41
Speaker 1: I don't have to tell you, he's incredibly entertaining, and
35:44
Speaker 1: we were talking about specifically how parts of this were
35:49
Speaker 1: so obvious coming down the pike, and no it's not
35:52
Speaker 1: that people didn't see it. Nobody wanted to believe it.
35:55
Speaker 1: It was quite quite astonishing. No, he's he's a very
35:59
Speaker 1: interesting person. You have to be careful when you're aund
36:01
Speaker 1: him because dramatic things always happen, yes, always, always. He's
36:05
Speaker 1: a magnet. He'll be getting he'll be getting some text
36:08
Speaker 1: messages which will tell about some of astronomical sales of
36:11
Speaker 1: his books, or some deal he's involved in, or the
36:13
Speaker 1: markets are doing something crazy. I was, I was with him.
36:17
Speaker 1: I was with him September in by Liverpool Street station
36:22
Speaker 1: when we got the phone call saying what happened? I
36:25
Speaker 1: was with him. I think was July the seventh in
36:28
Speaker 1: London a few years later when when we had four
36:32
Speaker 1: bombs went off. We were giving a course that day,
36:35
Speaker 1: and um, no, things happen around him with the same
36:39
Speaker 1: It makes sense that he wrote The Black Swan because
36:42
Speaker 1: he is he is a Black Swan. Yes, indeed he is.
36:45
Speaker 1: So it's it's I wrotly. At the same time his
36:47
Speaker 1: book came out, the film The Black Swan came out,
36:49
Speaker 1: so if you google related completely. Um. So let's get
36:52
Speaker 1: to some of our our favorite questions that we ask
36:55
Speaker 1: all of our guests tell me the most important thing
36:58
Speaker 1: people don't know about your background. I think it's I've
37:04
Speaker 1: never had a job. Is true? That? Well, not really.
37:08
Speaker 1: I had a job when I was when I was
37:09
Speaker 1: seventeen for three weeks. Wait, I know you had a job.
37:12
Speaker 1: You were a professional juggler. Oh, now you see, Well
37:15
Speaker 1: I didn't have a boss. What I mean, I've never
37:17
Speaker 1: really had a boss. I've been running business since since
37:22
Speaker 1: I was about nine years old, including including the juggling
37:26
Speaker 1: which is on your the bio and your website. I
37:28
Speaker 1: saw that and I'm like, this has to be a
37:30
Speaker 1: type out and her professional juggler, undercover investigator, first man
37:38
Speaker 1: in the UK to obtain an online divorce. These are
37:42
Speaker 1: these are rather unusual curriculum vitae details. It's not it's
37:46
Speaker 1: it's because, yeah, I'm very bad at taking risks physically.
37:51
Speaker 1: I don't take any physical risks. I don't take many
37:53
Speaker 1: financial risks. But I'm happy to do any number of
37:56
Speaker 1: reputational risks you can throw me. So that. For example,
38:00
Speaker 1: two years ago and a friend of mine who works
38:03
Speaker 1: for a Channel four TV channel in the UK, he
38:08
Speaker 1: wanted he said he wanted to do some investigative undercover
38:12
Speaker 1: thing to find out were the political parties in the
38:17
Speaker 1: UK corrupt? If you offer the money, how far could
38:19
Speaker 1: you get? Now, previously you would have someone pretend to
38:24
Speaker 1: be someone, and they said they wanted to go a
38:25
Speaker 1: step further in this age of Google, they won't to
38:28
Speaker 1: have someone who was real, a real business person, person
38:31
Speaker 1: who didn't mind making itself look stupid. And so he
38:34
Speaker 1: thought of me, and so so for six months I
38:38
Speaker 1: had to have meetings with all sorts of politicians of
38:41
Speaker 1: the main parties in the UK and offer them money,
38:43
Speaker 1: and offer them money and so and see how what
38:46
Speaker 1: would happen, how far far I could go, all without
38:49
Speaker 1: entrapping them, so that you know, and all often aren't
38:53
Speaker 1: you at risk for committing a crime? In the United States,
38:56
Speaker 1: offering an elected official cash to do your bidding is
39:00
Speaker 1: a felony through a nuances here indeed, and they had
39:04
Speaker 1: teams of lawyers because actually because it was a it
39:07
Speaker 1: was a TV program or on the newspaper that the
39:09
Speaker 1: TV channels have a very strict rules. So for example,
39:13
Speaker 1: we couldn't do I ended up doing lots of hidden
39:17
Speaker 1: cameras stuff. But you're not allowed to do that for
39:20
Speaker 1: TV unless you've got grounds. So we did a lot
39:22
Speaker 1: of prepriority stuff and we got some sort of dodgy
39:26
Speaker 1: things which unfortunately never got recorded. And then I went
39:29
Speaker 1: in with with the equipment on the had the pen
39:32
Speaker 1: in the pocket, the thing in the in the in
39:34
Speaker 1: the Did you catch anybody? Yeah, yeah, yeah there was
39:38
Speaker 1: some somebody had to resign from the lived down party.
39:40
Speaker 1: And so it's great fun. You know, we're coming to
39:43
Speaker 1: the house of lords with hidden cameras. How do you
39:45
Speaker 1: how do you smuggled hidden cameras into the into the government?
39:47
Speaker 1: Now you have to go through a metal detector and
39:49
Speaker 1: something else. It's harder to do that, exactly, exactly, all right.
39:54
Speaker 1: So so you've never had a boss is the thing
39:57
Speaker 1: most people don't know about you. But but I like
40:00
Speaker 1: special investigator. Let's talk a little about some of your mentors.
40:03
Speaker 1: Who are you? Who are the people who helped guide
40:06
Speaker 1: your career a law? Well, the I guess the obviously
40:11
Speaker 1: want my mother. My mother, she always she always encouraged
40:15
Speaker 1: me to explains the undercover thing, encouraged me to do
40:19
Speaker 1: things even though they might have frightened me. Again with
40:23
Speaker 1: the caveat nothing physical. I always got out of games
40:26
Speaker 1: at school just reputational risk. Yeah, yeah, anything. Yeah, I'm
40:31
Speaker 1: going to draw the line of singing in public though. Okay,
40:33
Speaker 1: that's so no karaoke, no, but not to carry oke. Um.
40:39
Speaker 1: So so so I found that that's very helpful that
40:42
Speaker 1: that if something frightens me, I think I've could do it.
40:46
Speaker 1: Now it's frightening me, so I've got to do it. Um.
40:48
Speaker 1: That's a good philosophy, I think. So. Um. And this
40:51
Speaker 1: is way before that, that book Feel of Fear and
40:53
Speaker 1: Do It Anyway, way before decades before that. I'm unfamiliar
40:56
Speaker 1: with that. You don another book, Feel the Feel of
40:58
Speaker 1: Fear and Do It Anyway. Okay, it's some self help,
41:02
Speaker 1: but from the eighties, I think another one. There's a
41:06
Speaker 1: there's a my tutor and supervisor, John Ockendon. He I
41:11
Speaker 1: was very lucky when I was studying mathematics to fall
41:14
Speaker 1: in with a group of people mathematicians who who like
41:19
Speaker 1: solving real world problems, physical problems, for example, using fluid
41:25
Speaker 1: mechanics or things like that. Um, because most most mathematicians
41:31
Speaker 1: are quite snobbish about the mathematics, and there's a there's
41:34
Speaker 1: a hierarchy amongst mathematicians which is at the very highest
41:38
Speaker 1: of the people who work on these these very profound,
41:41
Speaker 1: deep problems firms, the you know, theoretical things that you
41:45
Speaker 1: know to the man the street, no idea what they're
41:49
Speaker 1: on about or what the relevance really but really really
41:52
Speaker 1: tough things, tough, you know. Then you moved down the
41:56
Speaker 1: ranks and pure mathematicians who work in the apps act.
42:00
Speaker 1: Then you've got applied mathematicians and these this group of
42:04
Speaker 1: people would pretty much the lowest of the low in
42:06
Speaker 1: the sense of the hierarchy, and they used the maths
42:10
Speaker 1: to solve real problems. Um, what are they thinking? Exactly exactly?
42:15
Speaker 1: But it was great fun and it gets It means
42:16
Speaker 1: that that there are a very few people. Actually, I
42:19
Speaker 1: can only think of one other person in the world
42:22
Speaker 1: who's got had such a breadth of mathematical experience as
42:26
Speaker 1: I have. That's why it can be very skeptical about
42:29
Speaker 1: the models, because I've seen so much of the mathematical
42:32
Speaker 1: world that other people haven't seen. And of course when
42:35
Speaker 1: I started working in finance, they had to develop a
42:38
Speaker 1: whole new category even lower than the lowest of the
42:40
Speaker 1: low for people working in mathematical finance. So I have
42:44
Speaker 1: to ask who was the other person who has a
42:46
Speaker 1: similar breath of mathematical experience. Take him he's um, yeah,
42:53
Speaker 1: he's he's He's worked on a lot of volatility stuff.
42:56
Speaker 1: He's very clever guy, but he's had he's had a
42:59
Speaker 1: boss many times, so he doesn't have the answer to
43:02
Speaker 1: that question. Look what what investors have influenced you? Who?
43:06
Speaker 1: Who's affected your approach to looking at markets? Um? Which investors?
43:15
Speaker 1: I don't really follow people in that sense. That's a
43:19
Speaker 1: good answer everybody else, says Warren Buffett. But not following
43:23
Speaker 1: people is a net I'm I'm I have this this
43:27
Speaker 1: this talent, which is if if if people say one thing,
43:30
Speaker 1: I say the exact opposite, and it comes a point
43:34
Speaker 1: where I don't really know what my own opinion is.
43:36
Speaker 1: You're just you're just fading the group. I just just
43:39
Speaker 1: you know, annoys people, winds them up. But it sort
43:43
Speaker 1: of works. The divergent opinion is a variant perception absolutely works.
43:49
Speaker 1: Let's talk about everybody's favorite question. Books. What are some
43:52
Speaker 1: of your favorite books? Finance? Fact fiction or nonfiction? Finance nonfinance?
43:57
Speaker 1: I almost never never ever read nonfiction. Almost never. You
44:05
Speaker 1: only read fiction pretty much only ever read see I
44:08
Speaker 1: love nonfiction, and I never I love fiction, and I
44:11
Speaker 1: never get to it. Okay, so most of what I
44:14
Speaker 1: read is is nonfiction. I find that the most of
44:17
Speaker 1: the nonfiction that I try to read is sort of obvious,
44:22
Speaker 1: and I think I feel like you'd subscribed to one
44:25
Speaker 1: of these things where they condense a five page book
44:27
Speaker 1: into two page So history, biography, some biographies, but not
44:34
Speaker 1: give us a few times. What do you like? So
44:36
Speaker 1: if you prefer fiction, prefers fiction, I say one of
44:42
Speaker 1: my my all time favorite books is the The Life
44:46
Speaker 1: and Opinions of Tristram Shandy Gentlemen mid eighteenth century. I
44:52
Speaker 1: think it was It's a totally well, I can't even
44:56
Speaker 1: begin to strive it. It's been described as a as
44:59
Speaker 1: an unfil unfilmable book because it's it's so I can't
45:05
Speaker 1: even begin to describe it. You should read it, Okay,
45:07
Speaker 1: I have not. It's it's a fantastic book. But I
45:09
Speaker 1: tend to alternate fiction. I tend to alternate between yeah, yeah,
45:14
Speaker 1: the Life and Opinions of Tristram Shandy's he's fictional, Tristan
45:18
Speaker 1: trist Tristram Shandy. I tend to alternate between what you
45:24
Speaker 1: might call a classic book and then something just to
45:26
Speaker 1: just to recover from the classic book. And I'm very
45:30
Speaker 1: and now very impatient with books. Up until the age
45:33
Speaker 1: of thirty, I would say that if I started a book,
45:35
Speaker 1: I had to finish it, and you know, you get
45:38
Speaker 1: you get over that pretty quick. I got it was
45:40
Speaker 1: the greatest thing that ever happened to me, other than
45:42
Speaker 1: birth of my children, to realize that I could just
45:46
Speaker 1: stop reading a book away. Oh, this is disappointing. So
45:50
Speaker 1: so I if a book's more than say, fond of pages,
45:53
Speaker 1: I probably won't even buy it. If it if it
45:57
Speaker 1: gets rave reviews on Amazon, if it's kind of classical,
46:02
Speaker 1: then I might avoid it because I know it's you know, critique,
46:05
Speaker 1: a little bit funny, and I'll alternate between maybe some
46:09
Speaker 1: classic and something easy. One thing I've discovered relatively recently
46:14
Speaker 1: is Lee Child. Now you know Lee Child? Sure, Jack
46:18
Speaker 1: Reacher or the fascinating thing about my wife plows through
46:21
Speaker 1: those books. Fascinating thing is of all the kind of
46:24
Speaker 1: he's not exactly pulp fiction, but it's heading that actually
46:26
Speaker 1: he's the only one that middle class people will admit
46:29
Speaker 1: to reading. Oh really, Yes. The funny thing is, if
46:32
Speaker 1: you've seen the movie, it's a short white guy. But
46:35
Speaker 1: in the in the book, it's a tall black guy.
46:38
Speaker 1: I don't know why they didn't catch that. Um he's tall, No,
46:44
Speaker 1: he's not. I don't think he's black. I thought he was.
46:46
Speaker 1: I don't know how many of you read um one
46:51
Speaker 1: my wife. My wife's read ten. Okay, well I have
46:55
Speaker 1: to have to ask. I could be, but we can
46:57
Speaker 1: definitely agree that Tom Cruise as a short white guy
46:59
Speaker 1: for sure, to say the least. Um, let's one other book.
47:03
Speaker 1: Give me one other thing you've read recently. Something I've read,
47:06
Speaker 1: Oh okay, okay, okay and enjoyed, okay, okay, but you
47:10
Speaker 1: don't have to have enjoyed I mentioned earlier. J. K.
47:12
Speaker 1: Rowling she wrote this, so you under a pseudonym the
47:15
Speaker 1: book which one I don't remember, but she's done this
47:19
Speaker 1: one called The Cuckoo's Calling something that it's awful. I
47:25
Speaker 1: mean really, I mean really. I love the Harry Potter books,
47:28
Speaker 1: I love them, but this is diet. It's like she's
47:31
Speaker 1: trying to be actually Christie, but it's like three times
47:33
Speaker 1: as long as it should be and you just don't
47:36
Speaker 1: I finished that just because you know what who done it.
47:39
Speaker 1: But by the time you get to the end, you
47:41
Speaker 1: realize anybody could have done it, and she just picked randomly,
47:44
Speaker 1: so you just did it was rubbish void. Please all right,
47:49
Speaker 1: I'm gonna I'm gonna cross that off my list. Um,
47:52
Speaker 1: So since you first started following quantitative finance, what's changed
47:58
Speaker 1: in the Indians in the industry. What do you think
48:00
Speaker 1: is the Is it the rise of technology, is it
48:04
Speaker 1: the wholesale adoption? What what is the big shift in
48:08
Speaker 1: quantitative finance? Well, the technology you have to mention technology indeed, Um,
48:13
Speaker 1: but that's happened everywhere, not just in quantity of finance,
48:16
Speaker 1: but also the number of people, the sheer quantities of people,
48:20
Speaker 1: and it's taken over finance pretty much. It has, and
48:23
Speaker 1: I'm not sure that there are the people are necessarily
48:26
Speaker 1: as good as they used to be. That's my sense,
48:30
Speaker 1: saluning the talent pool just through sheer numbers or the
48:33
Speaker 1: warm people being attracted to it. It's a bit of
48:36
Speaker 1: both and also education. I have my own educational program,
48:39
Speaker 1: the certificate and Quantitative Finance, but most people go through
48:43
Speaker 1: Masters in Finance programs of finance engineering at university and
48:48
Speaker 1: they're taught by by people who don't necessarily understand how
48:53
Speaker 1: the the how the markets work, or you yourself have
48:56
Speaker 1: mentioned between us, we've mentioned verse the psychological side things,
49:00
Speaker 1: and you really have to understand human nature really almost
49:05
Speaker 1: before you start doing the mathematics. And that's something people
49:09
Speaker 1: completely miss. They think the models explain everything. And most
49:13
Speaker 1: of these programs people go on that that that taught
49:15
Speaker 1: the mathematics, but they're not taught when things break down,
49:18
Speaker 1: why things break down, etcetera. So education is pretty bad,
49:22
Speaker 1: I think. So tell us about a time you failed
49:26
Speaker 1: and what you learned from the experience at the time
49:28
Speaker 1: I failed. Um, the there's there's one. This may be
49:36
Speaker 1: a bit cheesy, it may not be strictly true, but
49:38
Speaker 1: it sounds quite good. There was a time that we said,
49:42
Speaker 1: I must have been about thirteen, and there was a
49:45
Speaker 1: there was a phase of we were all playing all
49:47
Speaker 1: the card games and gambling, you know, just for pennies,
49:50
Speaker 1: really pennies, because this is it's an early nineteen seventies.
49:53
Speaker 1: And I remember one time making a bet with Billy Jones.
49:59
Speaker 1: Bill Jones, he's Billy Jones and my class, Billy Jones,
50:03
Speaker 1: another thirteen year old gotcha and it was I think
50:08
Speaker 1: I think it's about seven p right. It was all
50:11
Speaker 1: the money I had in my pocket seven p and
50:13
Speaker 1: I lost this bet. It was a stupid, stupid, stupid bet.
50:16
Speaker 1: But I made the bet and I lost it. And
50:18
Speaker 1: clearly it's you know, you can see me crying now
50:20
Speaker 1: as we speak. It's had an impact on my on
50:23
Speaker 1: my entire life, and I think it's it's made me
50:26
Speaker 1: very risk of us. Well, we're all, we're all theoretically
50:29
Speaker 1: somewhat risk averse, but you suffered a little. Um, it's
50:33
Speaker 1: actually helpful. The worst thing in the world that can
50:35
Speaker 1: happen to someone is they walk into a casino and
50:38
Speaker 1: win money, or their first time they start training they
50:41
Speaker 1: make money. You're much better off experiencing laws up front,
50:46
Speaker 1: Billy Jones to think, that's right, that's right. Let's let's
50:49
Speaker 1: jump to my our last and two favorite questions. Uh,
50:53
Speaker 1: these are the ones that seem to have the most
50:55
Speaker 1: amount of resonance with people. So if a recent college
50:59
Speaker 1: graduate or a millennial would come up to you and say,
51:02
Speaker 1: I'm interested in a career in quantitative finance, what sort
51:06
Speaker 1: of advice would you give them? Yeah? Yeah, yeah, yeah,
51:10
Speaker 1: oh okay, what would it? It's it's what do people
51:14
Speaker 1: you usually say for this sort of thing that it
51:17
Speaker 1: ranges from. It ranges from I'm the wrong person to
51:22
Speaker 1: ask to let me tell you these three things and
51:24
Speaker 1: never forget them, and in between okay, well i'm gonna
51:27
Speaker 1: get I'm gonna give you another point on the spectrum,
51:31
Speaker 1: which is have children early. Okay, postpone the career thing. Really,
51:37
Speaker 1: as much as you can do you have children. So
51:42
Speaker 1: I'm I'm a late bloomer. I would say have children
51:45
Speaker 1: early because most people talk about they want to have
51:47
Speaker 1: this to get their careers kickstarted. And when they talk
51:50
Speaker 1: about their career, they're always talking about either being a
51:53
Speaker 1: lawyer or or a quantity or something. It's not like
51:56
Speaker 1: they're curing you can wait, it can wait being a
51:59
Speaker 1: lawyer away it really it really upsets me when I
52:02
Speaker 1: see people who who just good around children, but they've anyway.
52:07
Speaker 1: So that would be to just wait a while and
52:08
Speaker 1: have children first and our last and final question, what
52:11
Speaker 1: do you know about quantitative finance today that you wish
52:15
Speaker 1: you knew twenty five years ago? One thing that well
52:23
Speaker 1: I realized halfway through that period would be that's the
52:29
Speaker 1: There's a lot of people have names attached to models,
52:35
Speaker 1: but usually those models I don't know to offend any
52:37
Speaker 1: friends of mine here, but usually those models offender offend away.
52:41
Speaker 1: How many people listen to this? No, just it's just
52:43
Speaker 1: the two of us. The They have come up with
52:47
Speaker 1: a model, and usually it's it's a minor tweak two
52:52
Speaker 1: to something to some other model, and it's not necessarily
52:55
Speaker 1: a great model in any way, shape or forming. It's
52:58
Speaker 1: a bad model tweaked to be something else, to be
53:00
Speaker 1: even worse, not even to better. Usually it's tweaked to
53:05
Speaker 1: make it easier to use. And you did not know
53:08
Speaker 1: that twenty years ago. I didn't realize that that was
53:11
Speaker 1: sort of important. Um. And I wonder if I knew
53:15
Speaker 1: that twenty five years ago, would I have taken a
53:17
Speaker 1: different route. I really hope I wouldn't, because my route
53:22
Speaker 1: has always been to do whatever I think is fun.
53:25
Speaker 1: And that's why you look at my list of papers
53:27
Speaker 1: also of crazy things in that in my list of papers. Um,
53:32
Speaker 1: so that this isn't really something I wish I knew
53:35
Speaker 1: I would take an advantage of. I would like to
53:37
Speaker 1: think I knew it and I decided not to do.
53:39
Speaker 1: It's a it's actually it's it's it's Keane's again. I
53:41
Speaker 1: think it's Kane's it is. He says it is better
53:46
Speaker 1: to fail conventions than to succeed unconventionally, and I very
53:52
Speaker 1: much succeeded unconventionally inventionally. I don't know where I'm going
53:56
Speaker 1: with any of this, but but it's fascinating. Nonetheless, we
54:00
Speaker 1: have been speaking with Paul Wilmot, quantitative finance expert and
54:04
Speaker 1: author his his latest book, Paul, what's the name of
54:07
Speaker 1: your new book? It's The Money Formula, Don't you Finance?
54:11
Speaker 1: Pseudo science and how mathematicians took over the markets. If
54:14
Speaker 1: you enjoy this conversation, be sure and look up an
54:16
Speaker 1: intro Down an Inch on Apple iTunes and you could
54:19
Speaker 1: see any of the other hundred and fifty or so
54:22
Speaker 1: such conversations we've had. You could find us on Apple iTunes,
54:27
Speaker 1: overcast wherever fine podcasts are sold. I would be remiss
54:32
Speaker 1: if I did not think the crack staff that helps
54:36
Speaker 1: put this podcast together each week. Medina Parwana is my
54:40
Speaker 1: technical producer and audio engineer. Taylor Riggs is our booker
54:44
Speaker 1: slash producer. Mike bat Nick is our head of research.
54:48
Speaker 1: I'm Barry Ridholtz. You've been listening to Masters in Business
54:52
Speaker 1: on Bloomberg radio