Speech at Commencement 2014 to Berkeley MSE Grads
POSTED — April 19, 2014 — Lectures
Berkeley MFE Commencement
March 21, 2014
It’s truly a great pleasure for me to be at the University of California at Berkeley today. Not quite 50 years ago, when I was an undergraduate studying physics in Cape Town, I began applying to go to the United States for graduate school. It seemed to be the right thing to do if you were serious about your field.
So I applied to three schools: Columbia, because I knew someone in Cape Town who had just gone there, and because it was in New York City; Caltech, because Feynman was there and had recently been awarded the Nobel prize and also published the stylish and insightful Feynman lectures on physics, though I didn’t understand at the time what he had actually accomplished; and Berkeley, because it was in the news for the start of the revolts against arbitrary authority on campus. I read the other day that year is the 50th anniversary of the Berkeley Free Speech Movement that seem to me to have kicked off the Sixties. For those of you who are too young to remember that, whichI suppose is all of you graduating today and maybe much of the faculty too, take a look — it’ll make interesting reading.
So, a few years later I ended up at Columbia, but I’ve always had a soft spot for Berkeley. Twenty years later I had a PhD in physics and had been a physics academic, and then through various serendipities moved from physics to finance. Berkeley played a role in that too. The first paper I ever read in finance was the paper on the binomial model co-authored by Mark Rubinstein with Cox and Ross. Much of what I understand about finance still comes from understanding that paper and trying to extend it in various ways. Similarly but later with other papers by Berkeley authors like Hayne Leland, Mark Garman and Terry Marsh. So, here I am today, in indirect ways someone who was influenced by Berkeley, and I have tried to think about what encouragement and perhaps advice to give you as you set out to enter the world of finance to which I came as a latecomer and an outsider.
First I want to remark on how the world has changed.
Before I became a financial engineer, I was a theoretical physicist. I came to financial engineering without the kinds of excellent and comprehensive training that you’ve just received. There was no way to get that kind of training in 1985 when I arrived at Goldman Sachs. I arrived one day in November and was set by my boss to work on building binomial options models for bonds. There were only two text books on options you could buy: Jarrow and Rudd, and Cox and Rubinstein, two classics I still own. I quickly set about reading them. There was no one to teach you and no training program at Goldman in those days. You pretty much had to teach yourself. In many ways that was good. The whole field of quantitative finance, at leasts in industry, was a kind of amateur heaven in those days. You could spend a short time learning something and then you were ready to start to try to do something new. It’s nice to have lived in an era like that but those days have unfortunately passed. I feel a bit sorry for your generation — you have so much formalism to learn before you feel you can do anything practical and useful, though that isn’t always the case.
Life for financial engineers has changed since those days, and I once wrote. In 1985, I quickly noticed the embarrassment involved in being “quantitative”. Sometimes, talking in a crowded elevator with another “quant,” you might start to say something about the duration or convexity of a financial instrument. Duration was sophisticated stuff in those days, though now it’s introductory for you! If the person you were talking to had been at the firm a little longer than you, he – it was usually a he, unlike a lot of the people I see before me, though, as now, most of us were non-American born – that person would cringe a little, and rapidly try to change the subject. “See the Yankees game last night?” he might ask, the sort of things a real bond trader might say.
Soon, you began to realize, there was something a little shameful about two consenting adults talking math in a crowded elevator; there was something embarrassing about mentioning programming or Java in the company of bankers. There was something awful about being “outed” as a quant in public. People in the elevator just looked away.
Even in the Nineties, quantitative skills were reluctantly tolerated. Once, a friend and I were talking on the trading floor when one of the convertible traders walked between us, momentarily. Suddenly he grimaced and winced; he clutched his temples with both hands as though a sharp pain had pierced him and exclaimed, “Aaarrgghhhh! The force field! It’s too intense! Let me get out of the way!”
I truly remember that even in the Nineties big shots in business didn’t put their email address on their business cards — that was for geeks — and didn’t put a PhD on their card either. That branded you.
Now, in the aftermath of the financial crisis, after big firms have made lots of money via financial engineering and lost lots of money by carelessness or hubris, after quantitative and algorithmic trading has become the fashion, after many hedge funds call themselves quant, financial engineering and risk management have become hot areas. That’s good for you, and gives you as a financial engineer much more power in the world than we had, but it also adds responsibilities which I’m going to talk about a little later.
In this new environment, let me give you some pieces of advice based on my own experience.
1. I want to talk about is Doing Dirty Work. You’ve learned a lot of stochastic calculus and optimization and stuff like that in the past 18 months. That’s necessary and important. But equally important, if you’re going into the business world, is that the business world runs on dirty work. Don’t scorn or eschew getting your hands dirty . Most of the useful things I’ve done in life involved getting my hands dirty, esp. in business. It’s OK to do your own programming, your own figures, your own dirty work. You learn a lot more by doing it yourself than by avoiding it or giving it to someone else to do. Don’t think anything is beneath you because you have an education.
2. The second thing: Be Reliable. When you start working, get to be so good at something that people around you can rely on you for it. I once hired a guy at Goldman to help set up our new Sun Microsystems workstations for derivatives systems in 1990 and when he arrived I started babbling to him in a panic about all the things I had to get done and the things that weren’t working. He turned to me and said: “Don’t worry about that, that’s what I’m here for!” I can’t tell you what a good impression that makes. Remember that the firm is hiring you for what you can do for them, not vice versa.
3. I also want to talk a little about The Nature of Financial Engineering, the characteristics that make it both difficult and interesting.
Science seeks to discover the fundamental principles that describe the world, and is reductive. Engineering is about using those principles, constructively, for a purpose. Mechanical engineering is concerned with building devices based on Newton’s laws, suitably combined with heuristic or empirical rules about more complex forces (friction, for example) that are too difficult to derive from first principles. Electrical engineering is the study of how to create useful electrical devices based on Maxwell’s equations and solid-state physics, combined with similar heuristics. Similarly, bio-engineering is the art of building prosthetics and other biologically active devices based on the principles of biochemistry, physiology and molecular biology.
So what is financial engineering? In a logically consistent world, financial engineering should be based on financial science. Financial engineering would be the study of how to create functional financial devices – convertible bonds, synthetic CDOs, etc. – that perform in desired ways, not just at expiration, but throughout their lifetime.
But what exactly is financial science?
Brownian motion and other idealizations you’ve learned about, while they capture some of the essential features of risk, are not truly accurate descriptions of the characteristic behavior of financial objects. We don’t know the correct stochastic partial differential equations for a stock or its volatility. Maybe we never will, because people’s behavior changes. There are no proven models that work reliably.
Therefore, financial models are often crude but useful analogies with better understood physical systems. We pretend stocks behave like smoke diffusing, or that short term rates satisfy geometric Brownian motion. None of this is true in the same sense that it’s true that planets satisfy Newton’s laws.
There is as yet no truly reliable financial science beneath financial engineering. Financial models attempt to describe the ripples on a vast and ill-understood sea of ephemeral human passions, using variables such as volatility and liquidity that are clever quantitative proxies for complex human behaviors. Such models are roughly reliable only as long as the world doesn’t change too much. When it does, when crowds panic, anything can happen.
That’s the bad part of the story but it’s also the good part of the story. The good part is that financial engineering is really a multidisciplinary field that involves not only science but art too. It involves financial knowledge, business knowledge, mathematics, statistics. It also involves psychology and introspection. Also, very very importantly, computation, because there’s little you can achieve without computation. So think of yourself as working in an interdisciplinary field in which you have to bring to bear many skills to solve practical and theoretical problems.
4. That brings me to the idea of Good Taste and Intuition.
Your job will often involve integrating different aspects of the financial field. To do it well you will need to combine your quantitative knowledge with market knowledge, taste and the intuition that grows from experience. Don’t just be satisfied with getting a number as an answer; be able to explain in words and ideas why, roughly, the number is what it is, why it increases or decreases in certain ways as certain things change. Always try to develop a visceral or physical or economic intuition to support or even precede your results. You’d be surprised how many complicated things can be understood simply if you struggle with them.
The most topical economist of today, Keynes, gave a speech about the intuition of Sir Isaac Newton, the founder of the modern scientific approach, at the tercentenary of his birth in Cambridge, England. Keynes had read some long lost notes of Newton’s, and spoke about Newton’s focus and intuition:
I believe that the clue to his mind is to be found in his unusual powers of continuous concentrated introspection … His peculiar gift was the power of holding continuously in his mind a purely mental problem until he had seen straight through it. I fancy his pre-eminence is due to his muscles of intuition being the strongest and most enduring with which a man has ever been gifted. Anyone who has ever attempted pure scientific or philosophical thought knows how one can hold a problem momentarily in one's mind and apply all one's powers of concentration to piercing through it, and how it will dissolve and escape and you find that what you are surveying is a blank. I believe that Newton could hold a problem in his mind for hours and days and weeks until it surrendered to him its secret. Then being a supreme mathematical technician he could dress it up, how you will, for purposes of exposition, but it was his intuition which was pre-eminently extraordinary - 'so happy in his conjectures', said De Morgan, 'as to seem to know more than he could possibly have any means of proving'. (De Morgan laws of logic)
There is the story of how he informed Halley of one of his most fundamental discoveries of planetary motion. 'Yes,' replied Halley, 'but how do you know that? Have you proved it?' Newton was taken aback - Why, I've known it for years', he replied. 'If you'll give me a few days, I'll certainly find you a proof of it' - as in due course he did …
I quote this because I find it inspiring, and I hope you do too. Not only art, but science too, requires intuition.
I want to go on a bit more about good taste. The world of markets, which is the world of people, is hard to fathom. Financial models are merely approximations and analogies. Since no model can accurately describe that world, it’s actually important to try hard but not too hard. You have to know when to stop and you have to know what to leave out of your model and leave to your users’ intuition. That’s why implied volatilities and implied variables and calibration play such a big part in finance. To be useful, one should be ambitious in believing a model can represent the world, but not too ambitious. What works best are simple low-dimensional models with a few essential characteristics. Most real things are too messy for a full theoretical treatment, and so models with good taste that can be calibrated to observable fungible tradable securities prices, are very important and count for much.
5. I want to give you some words of caution too, about the misuse of models.
Finance is a very large and growing part of the economy. It can do good — this is a capitalist world and credit and securitization is the way that new ventures get funded — but most of the people who go into it — most but not all — don’t go into it because they want to do good. In that sense it’s not like medicine or social work. There’s nothing wrong with that.
Modeling is human. Financial modeling is human too. And people need models to understand how to value securities, how to invest. Risk is everywhere. Because of that, because investors want return with the least amount of risk, models are a great sales tool. People buy securities on the basis of models of some kind. Salespeople use models to sell illiquid securities for liquid cash. One therefore has to be careful that one’s models are not misused in an unethical way, and I became very aware of this in my professional life.
Several years ago therefore Paul Wilmott and I wrote The Financial Modelers’ Manifesto, an attempt to provide a Hippocratic oath for financial engineers, part of which I quote:
The Modelers' Hippocratic Oath
~ I will remember that I didn't make the world, and it doesn't satisfy my equations.
~ Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.
~ I will never sacrifice reality for elegance without explaining why I have done so.
~ Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.
~ I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension.
I think these are good principles, and you fill find they are hard to put into practice when your models are used to make money. It’s not easy, but you have to try.
I’ve said a lot, and I want to finish on a positive note about financial engineering, which I’ve been doing for almost 30 years.
I once read a biography of Goethe, Germany’s 18th Century Shakespeare, a great Romantic, and one of the last people to make contributions to both art and science, which is what, in some sense, we are trying to do too.
Scientists often look down on Goethe, and regard Goethe as a poet who strayed beyond his proper place, who shouldn’t have tried to do science. His critics said he mistakenly thought of nature as a work of art, and that he was trying to be qualitative where he should be quantitative.
But, according to the book I read, Goethe was not so naive as to think that nature is a work of art. Rather, he believed that our knowledge and description of nature is a work of art.
That’s how I like to picture what we can do in financial modeling – making a beautiful and truthful description of what we can see. We’re involved in intuiting, inventing or concocting approximate laws and patterns. We can synthesize both art and science in creating understanding. We can use our intuition, our scientific knowledge and our pedagogical skills to paint a picture of how think qualitatively, and then, within limits, quantitatively, about the world of human affairs, and in so doing, have an impact on how other people think.
Go out there, have a good impact, and have a good time.