Financial engineering as a career: Part 1
by Emanuel Derman
Several years ago, my son, who did a PhD thesis on the reception history of Max Weber, the founding father of sociology, introduced me to two influential essays by Weber, entitled respectively Science as a Vocation and Politics as a Vocation. In them Weber discusses what problems you have to face, and what personality and character you have to own, if you decide to make these fields your calling, and he’s surprisingly thoughtful and yet practical about it.I thought it would be interesting to begin to think about the same questions with respect to entering the field of Quantitative Finance, particularly from a practitioner’s point of view.
According to Zvi Bodie, financial engineering is the application of science-based mathematical models to decisions about saving, investing, borrowing, lending, and managing risk. I think that’s a reasonable definition.
Science – mechanics, electrodynamics, molecular biology, etc., – seeks to discover the fundamental principles that describe the world, and is usually reductive and analytic. Engineering is about using those principles, constructively and synthetically, for a purpose. Thus, 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 layered above a solid base of financial science. Financial engineering would be the study of how to create functional financial devices – convertible bonds, warrants, synthetic CDOs, etc. – that perform in desired ways, not just at expiration, but throughout their lifetime. That’s what Black-Scholes does – it tells you, under certain assumptions, how to engineer a perfect option from stock and bonds.
But what exactly is financial science?
Canonical financial engineering or quantitative finance rests upon the science of Brownian motion and other idealizations that, while they capture some of the essential features of uncertainty, are not finally very accurate descriptions of the characteristic behavior of financial objects. (You should perhaps even object to my use of the word ‘characteristic’ since it’s not clear that financial markets even have time-invariant characteristics.) Markets are filled with anomalies that disagree with standard theories. Stock evolution, to take just one of many examples, isn’t Brownian. We don’t really know what describes its motion. Maybe we never will. And when we try to model stochastic volatility, it’s an order of magnitude vaguer.
So, the point I want to make, for those people who consider coming into the field from one of the hard sciences, is that financial engineering rests on a shaky basis. That’s not to say that it isn’t worth doing. In one sense it makes it more interesting. If you’re going to work in this field, you have to understand that you’re not doing classical science at all, and that the classical scientific approach doesn’t have the unimpeachable value it has in the hard sciences. You have to ask yourself if you can live with that.
The Value Of What You Do Is Often Not Deep
A personal story. In 1985, bond options were the hot new product, the synthetic CDOs of the era. At that time most trading desks used Black-Scholes style models for bond options, in which each bond was treated as a weird kind of stock. Our major theoretical problem was how to consistently model the yield curve and all the bonds that defined them in unison, so that we could then value options on them. Eventually we came up with BDT, which, while it had its problems, was at least self-consistent and perhaps more usable than what came before.
But, surprisingly, it was the new user interface to the model, that I built myself in those primitive pre-mouse days of screen-based programs, that had the biggest impact. It worked well because I made it do what the traders needed, which I learned from working with them. My new version saved them countless keystrokes compared to the command-line interface that drove the previous FORTRAN version. All my model’s input and output were visible on one screen. There were also fields for storing information about the client and the trade. And best of all, you could save a possible trade under discussion and retrieve it the next day for continued development and discussion.
Though primitive by today’s standards, this interface was astonishingly better than what the desk had used before, and the traders and salespeople were overjoyed. By creating and saving the most common types of option trades as templates in files at the start of each day, they could respond rapidly to clients, accommodating many more requests much more efficiently.
Ever since then, it’s been impossible for me to overlook the difference a simple and well-designed piece of software can make to a business, no matter how good or bad the model underneath.
If you work in this field, then, despite the genuine glories of quantitative finance, you may have to face the fact that you can have the most dramatic effect by improving the ergonomics of trading and sales. Is that fact something you can live with?
Crude and Approximate But Often Useful
Financial Engineering is a multidisciplinary field. It involves financial knowledge, business knowledge, mathematics, statistics, and very importantly, computation, because there’s little you can achieve without computation. There are very few analytic solutions that apply to the markets and products you actually deal with, so you must approximate all the time, and decide what complexities to ignore.
Because of this you need experience to be genuinely useful, and so there are very few young geniuses in the field, unlike mathematics or chess. Experience and some wisdom is often necessary, because you’re dealing with people and their quirks, and a large part of it is a social science. Hard science assumes there is a stable world underlying the observed phenomenon; in social sciences that stability is much less obvious, perhaps even non-existent, because you’re playing with people and they keep changing the rules.
The methodology of quantitative finance is different from large parts of physics or chemistry or even biology. Financial models are crude, and are mostly analogies. They say something like “it may be useful to think that people value bonds by discounting them over the paths of all future short-term interest rates.” This isn’t true, but it’s just possibly useful. In contrast, in physics you can say that the quantum mechanical world behaves as though a particle really does take all future paths to its target, with interfering probabilities. This isn’t merely useful, it’s actually true.
So, if you work as a practitioner, you will have to live with the fact that you are going to have to make crude, false but hopefully useful approximations.
If you go to work in a big investment bank, you’ll soon discover that traders and salespeople order you about and often make more money but have less technical skills than you. That’s less true today, when more trading is technology and algorithm based, and when products are more complex, and when the buy side offers many different opportunities, but it’s still often the case.
Many practitioners or programmers gets weary after a while, and want to become “one of them.” But they may not have the skill or more importantly the personality to do that. There are more opportunities these days, especially at hedge funds, but nevertheless I’ve seen many people get disillusioned by having to continue in their mainly technical role. Can you change to be what you want? Can you live with being who you are?
Financial engineering as a career: Part 2
What’s Your Edge?
If you are going to seek a career in quantitative finance, what’s your advantage? Is it computer science, financial theorizing, pragmatic modeling, sales or trading, working on the desk with people or solitarily in an office? Which are you best at and, also, which do you enjoy doing most?
For years I came across people who could have had wonderful careers combining finance and applied computer science, a combination rarely found in investment banks, and yet so few of them want to take advantage of their computer skills.
Collegial or Solitary?
What environment do you like working in? Academic or bureaucratic? Do you like being supervised and taught or do you want to go your own way? Do you want to work in a large organization or a small one? This can make the difference between choosing an investment bank, a hedge fund, or a financial software company.
One incidental remark about working in business which I’ve realized only slowly. If you like interacting with people and working toward common goals, then well-run businesses are great places to work. Everyone is paid to work towards the same end, and there can be tremendous cooperation and team work. Academic institutions tend to be filled largely with people who by choice want to work quietly and alone, and so often, funnily enough, business life can be more collegial than college life.
If you choose to work in a business, do you have the personality for it? Can you bear the slow bureaucracy of a large organization, the ladder you have to climb, being ordered around and sometimes yelled at, the overwhelming respect paid to generating profit? If you’re a quant working for a desk, can you over the long haul bear being regarded as a tool rather than a prime mover? Some of this is changing, but some prejudice has always lurked beneath the surface. Money changes everything.
Short-Term or Long-Term? Research or Application?
If you choose to work in research, building models, do you have the inner drive to work alone for long periods without too much feedback or encouragement? (A PhD is a good training for this.).
In my case, I like the mix of both.
And, if you work in a university, to a large extent you will be cut off from knowing what is truly useful in practice, as opposed to what you imagine is useful. Many people in universities have false mental models of the way models are used on Wall Street. They imagine too much reliance on models and prediction, or too little.
The truth is that most useable and useful models are relatively simple filters that convert quantities you can grasp intuitively into dollar values. Black-Scholes converts your intuition about volatility into the dollar price of a complicated derivative security. This is very different from the physics or engineering you were brought up on, where models predict behavior accurately.
If you’re a scientist by training or nature, you will have to tolerate the anti-academic attitude of many people in the business world, though that is changing too. Are you comfortable with the idea that research should be kept secret, because that’s what many people on the Street believe, even if the secrets they think they know were sometimes borrowed from someone else, are not really that secret much of the time, and perhaps are rarely worth being kept secret? (Looking at the world with a jaundiced eye, you notice that a manager hates anyone that leaves their firm for another, taking useful experience and knowledge with him, until he or she does it himself.)
As For Man, His Days Are Like Grass
Weber, my son once explained to me, pointed out that as a scientist your every theory will most likely soon be replaced by a better one. You have to live with that. That’s even more true in quantitative finance. And it’s the opposite with art, where beautiful creations are timeless. We all still admire the original Venus de Milo, but no-one reads even the original Newton.
Because financial models are so ephemeral, I sometimes find myself opposing undergraduate degrees in the field; maybe it’s better to spend your undergraduate years learning really solid things that will last forever.
Semi-Apologia Pro Vita Sua
One final remark about values, perhaps not entirely inappropriate.
If you’re interested in more than just equations and solving them, then: What is the meaning and value of the work you do in the larger world?
If you’re a practicing scientist, I think it’s at least superficially clear: you’re adding to knowledge, pursuing the truth, (thinking that you’re) helping (perhaps) make the world a better place.
As regards quantitative finance, it’s less clear. Many people think you’re mis-employing your talents when you go to work in finance. (Nevertheless, when people ask me if I couldn’t be using my skills more usefully, I ask them the same.) Yes, you are adding to knowledge, but what is it used for? Often, simply to make money. Yes, that making of money may make markets more efficient, but is that sufficient social justification? I sometimes think that at least in finance, to paraphrase Johnson, invoking efficiency is the last refuge of scoundrels/self-interested people. But everyone is self-interested.
Personally, I like to think that to do quantitative finance you need to be committed to clarity – that there’s value in the world to seeing anything clearly and understanding it honestly, to seeing the world the way it really is. That’s enough of a vocation — to understand some part of the world. To be a good practitioner I like to think you need a dedication to unsentimental truth about whatever you deal with, wherever it may take you. I like to think that part of our job on earth is to be perceptive and accurate about as much as possible, including ourselves, about the way the world really works. If you do that, even for small things, it can add up to something bigger. It’s the one standard that transcends individual fields of study. That’s part of my rationale. There are others parts too. But mostly, it’s interesting work and sometimes useful and I’m not a saint. You have to decide for yourself what your rationale is, and whether the field is merely your job, or your career, or, if you’re lucky, your vocation.
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